Minimum variance

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4x4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.

Abstract— This paper presents an adaptive control scheme for suppressing jitter in laser beams. The variable-order adaptive controller is based on an adaptive lattice filter that implicitly identifies the disturbance statistics from real-time sensor data. The multi-channel adaptive ...

The minimum variance (MV) beamformer, also known as the Capon or minimum variance distortionless response (MVDR) beamformer, uses the recorded wavefield to compute a set of optimal weights to be applied to each sensor, before coherently adding the sensor outputs. The weights are chosen such that the variance of the output is minimized while maintaining unit gain in the view direction. The MV beamformer offers improved resolution and image quality compared to the conventional delay-and-sum (DAS) beamformer. The MV beamformer was originally introduced for passive systems. When adapting the MV beamformer to active sonar, sub-array averaging is necessary in order to avoid signal cancellation of coherent signals, and to reduce the sensitivity to errors in the wavefield parameters. Sub-array averaging is a technique developed for flat, uniformly sampled arrays, and it is not immediately evident that this will give satisfactory results on a non-flat array. In this work, we have successfully implemented the MV beamformer on an active 1D circular sonar array suitable for fishery sonar. We demonstrate, through simulations, that it is feasible to apply the MV beamformer with sub-array averaging to a circular array. Our results have been verified using experimental data from the Simrad SX90 fishery sonar.

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these "hierarchical time series". They are commonly forecast using either a "bottom-up" or a "top-down" method. In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottom-up approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions. We show in a simulation study that our method performs well compared to the top-down approach and the bottom-up method. We demonstrate our proposed method by forecasting Australian tourism demand where the data are disaggregated by purpose of travel and geographical region.

... 27-51, 1970. 23. Jagannathan, R. and Ma, T., "Risk reduction in large portfolios: Why imposing the wrong constraints helps," J. Finance, v58, pp. 1651-1684, 2003. ... Lorenzo Garlappi: colleagues. Francisco J. Nogales: colleagues. Raman Uppal: colleagues. ...

In this paper, several instrumental variable (IV) and instrumental variable-related methods for closed-loop system identification are considered and set in an extended IV framework. Extended IV methods require the appropriate choice of particular design variables, as the number and type of instrumental signals, data prefiltering and the choice of an appropriate norm of the extended IV-criterion. The optimal IV estimator achieves minimum variance, but requires the exact knowledge of the noise model. For the closed-loop situation several IV methods are put in an extended IV framework and characterized by different choices of design variables. Their variance properties are considered and illustrated with a simulation example.

Multiconjugate adaptive optics ͑MCAO͒ systems with 10 4-10 5 degrees of freedom have been proposed for future giant telescopes. Using standard matrix methods to compute, optimize, and implement wavefront control algorithms for these systems is impractical, since the number of calculations required to compute and apply the reconstruction matrix scales respectively with the cube and the square of the number of adaptive optics degrees of freedom. We develop scalable open-loop iterative sparse matrix implementations of minimum variance wave-front reconstruction for telescope diameters up to 32 m with more than 10 4 actuators. The basic approach is the preconditioned conjugate gradient method with an efficient preconditioner, whose block structure is defined by the atmospheric turbulent layers very much like the layer-oriented MCAO algorithms of current interest. Two cost-effective preconditioners are investigated: a multigrid solver and a simpler block symmetric Gauss-Seidel ͑BSGS͒ sweep. Both options require off-line sparse Cholesky factorizations of the diagonal blocks of the matrix system. The cost to precompute these factors scales approximately as the three-halves power of the number of estimated phase grid points per atmospheric layer, and their average update rate is typically of the order of 10 Ϫ2 Hz, i.e., 4-5 orders of magnitude lower than the typical 10 3 Hz temporal sampling rate. All other computations scale almost linearly with the total number of estimated phase grid points. We present numerical simulation results to illustrate algorithm convergence. Convergence rates of both preconditioners are similar, regardless of measurement noise level, indicating that the layer-oriented BSGS sweep is as effective as the more elaborated multiresolution preconditioner.

A simple trading model is presented in which Bayes' rule is used to aggregate traders' forecasts about risky assets' future returns. In this financial market, Bayes' rule operates like an omnipotent market-maker performing functions that in 1776 Adam Smith attributed to an ''invisible hand.'' We have analyzed two distinct cases: in the first scenario, the traders' forecast errors are uncorrelated, and in the second scenario, the traders' forecast errors are correlated. The contribution of our paper is fourfold: first, we prove that the ''efficient market'' mean-return can be expressed as a complex linear combination of the traders' forecasts. The weights depend on the forecast variances, as well as on the correlations among the traders' forecasts. Second we show that the ''efficient'' variance is equal to the inverse of the sum of the traders' precision errors, and is also related to the correlations among the traders' forecast errors. Third, we prove that the efficient market return is the best linear minimum variance estimator (BLMVE) of the security's mean return (in the sense that it minimizes the sum of the traders' mean squared forecast errors). Thus, an efficient market aggregates traders' heterogeneous information in an optimal way. Fourth, we prove that an efficient market produces a mean return (price) as a Blackwell sufficient (most informative) experiment among all possible aggregated expected return (price) forecasts.

Region growing is a very useful technique for image segmentation. Its efficiency mainly depends on its aggregation criterion. In the present paper, a new algorithm is proposed with a homogeneity criterion based on an adequate tuning between spatial neighbourhood and histogram neighbourhood. It differs from other techniques by reconsidering the pixel (or voxel) assignments on each step by a process which minimizes variance through special dilations. Thus, the region created by an initial seed can be non-connected and possibly does not contain this seed. Examples are given in dental surgery for 2D X-Ray images (and their associated 3D block) and for 3D images acquired by the Morphometre, the new 3D scanner constructed by GEMSE (General Electric Medical Systems). © 1997 Published by Elsevier Science B.V.

A generalized minimum variance control law is derived for the control of nonlinear, possibly time-varying, multi-variable systems. The solution for the tracking and feedback/feedforward control law was obtained in the time-domain using a nonlinear operator representation of the process. The cost index involves both error and control signal costing terms and is normally quadratic but may also involve nonlinear functions. The feedback controller obtained is simple to implement and includes an internal model of the process. The tracking controller can include future reference change information, providing a predictive control capability. In one form the compensator might be considered a nonlinear version of the Smith Predictor that has feedforward action.

Variance-based thresholding methods could be biased from the threshold found by expert and the underlying mechanism responsible for this bias is explored in this paper. An analysis on the minimum class variance thresholding (MCVT) and the Otsu method, which minimizes the within-class variance, is carried out. It turns out that the bias for the Otsu method is due to differences in class variances or class probabilities and the resulting threshold is biased towards the component with larger class variance or larger class probability. The MCVT method is found to be similar to the minimum error thresholding.

A price process is scale-invariant if and only if the returns distribution is independent of the price measurement scale. We show that most stochastic processes used for pricing options on financial assets have this property and that many models not previously recognised as scale-invariant are indeed so. We also prove that price hedge ratios for a wide class of contingent claims under a wide class of pricing models are model-free. In particular, previous results on model-free price hedge ratios of vanilla options based on scale-invariant models are extended to any contingent claim with homogeneous pay-off, including complex, path-dependent options. However, model-free hedge ratios only have the minimum variance property in scale-invariant stochastic volatility models when price-volatility correlation is zero. In other stochastic volatility models and in scale-invariant local volatility models, model-free hedge ratios are not minimum variance ratios and our empirical results demonstrate that they are less efficient than minimum variance hedge ratios.

This paper presents an empirical comparison of the out of sample hedging performance from naïve and minimum variance hedge ratios for the four largest US index exchange traded funds (ETFs). Efficient hedging is important to offset long and short positions on market maker's accounts, particularly imbalances in net creation or redemption demands around the time of dividend payments. Our evaluation of out of sample hedging performance includes aversion to negative skewness and excess kurtosis. The results should be of interest to hedge funds employing tax arbitrage or leveraged long-short equity strategies as well as to ETF market makers.

The adsorption characteristics of polyacrylamide polymer on iron ore tailings have been studied to understand the mechanism of adsorption. Adsorptions of both the anionic and non-ionic flocculants were analyzed using Langmuir isotherm and Freundlich isotherm. It was found that in comparison to the Langmuir model the Freundlich model was a better fit to the adsorption data according to the minimum variance criterion. It was established with the support of the Langmuir model that the adsorption of anionic and non-ionic flocculants is facilitated by hydrogen bonding. The zeta potential affects the settling rate and in this case the isoelectric point of the tailings which is basically a mixture of various oxides was measured to be 4.66. The settling rate was found to increase when the pH decreased from 10 to 5. The settling rate increased with increasing flocculant dosage up to a maximum value at 80 g polymer/t solid corresponding to the maximum adsorption of polymer. However, at higher pH the clarity of supernatant liquid was very poor and flocculation was not effective for ultrafine particles at that pH. with the tailings. The particle sizes of tailings are ultrafine in nature ranging from 1 to 45 m with a d 50 of around 7 m. It is important to mention that the iron ore tailings contain heterogeneous mixtures of iron oxides, silicates and aluminium-silicates. These tailings contain 40-50% (w/w) iron. The solid percentage in the tailings varies from 5 to 10% (w/w). The pH of the water in the tailings varies from 5.5 to 6.5. The beneficiation process requires the makeup water at around of 300-500 l/t of ore processed. It is essential to maximize the water recovery from the products and tailings by using dewatering techniques. Polymeric flocculation finds a wide application for dewatering in industries especially involving mineral processing, hydrometallurgy, and water treatment [1-3]. These polymers may be anionic, cationic or non-ionic. In flocculation, fine particulates suspended in liquid, interact with the flocculating agent and consequently aggregate to form flocs which settle rapidly under the influence of gravity. The flocculation process primarily involves transport of a particle to the closest distance of approach of another particle leading to collision and adhesion of particles resulting in aggregation and formation of flocs . The characteristics of flocs depend upon several factors such as surface chemistry, particle size distribution, density and shape of particle, viscosity and dielectric constant of suspension, chemical nature, molecular weight, charge and charge density of flocculant . It has been observed that polymers can flocculate ultrafine particles through various mechanisms such as bridging, charge neutralization, electrostatic patch and depletion flocculation . Ray and Hogg reported that bridging occurs by addition of high molecular weight polymer which forms the link 1385-8947/$ -see front matter

Stratified sampling is a methodology in which a heterogeneous population is partitioned into homogeneous subgroups called strata. The focus is on solving the combined problem of sample allocation and stratum boundary determination with the genetic algorithm (GA). Assuming that the number of strata and the total sample size are arbitrarily predetermined, stratum boundaries are determined using an objective function of minimum variance of the estimator; with sample size allocated through equal, proportional, Neyman, and GA allocation methods. Some numerical examples are given and the performance of GA is compared with the geometric and the cumulative root frequency methods.

The use of futures contracts as a hedging instrument has been the focus of much research. At the theoretical level, an optimal hedge strategy is traditionally based on the expected-utility maximization paradigm. A simplification of this paradigm leads to the minimum-variance criterion. Although this paradigm is quite well accepted, alternative approaches have been sought. At the empirical level, research on futures hedging has benefited from the recent developments in the econometrics literature. Much research has been done on improving the estimation of the optimal hedge ratio. As more is known about the statistical properties of financial time series, more sophisticated estimation methods are proposed. In this survey we review some recent developments in futures hedging. We delineate the theoretical underpinning of various methods and discuss the econometric implementation of the methods. . Of course, we are solely responsible for any omissions and commissions.

Abstract A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors' measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This ...

This paper investigates the optimal short-term hedging of Exchange Traded Fund (ETF) portfolios with index futures. Using daily data from May 2000 to December 2004 on the four largest passive ETFs (the Spider, the Diamond, the Cubes and the Russell iShare) and their corresponding index futures we examine the performance of minimum variance hedges for efficient variance reduction and for investors with exponential utility. Our findings relate to daily hedging based on OLS regression, exponentially weighted moving averages and ECM-GARCH models and the utility-based performance evaluation criterion is adopted to capture an efficient reduction in skewness and kurtosis as well as the variance. The basis risk on US equity indices is now extremely low and as a result we find no evidence that minimum variance hedge ratios outperform a naïve 1:1 futures hedge, either for individual ETFs or for portfolios of ETFs.

This paper provides an empirical study of the effectiveness of hedging the spider, a passive exchange traded fund (ETF) that replicates the S&P500 index. The spider is by far the largest ETF in the world: trading on the spider has grown so much during the past few years that it is now amongst the few most traded securities in the AMEX. The large net daily creation and redemption orders of recent years pose a problem to the market makers in the spider, as the orders may be too large to execute in the cash market. They face a decision about whether to hedge spider positions on their own book; and if so, how should they hedge? We have employed several sophisticated minimum variance estimates for the future hedge ratio, including OLS regression, an ECM to account for maturity effects and the cointegration of the spot and the future prices and, to the ECM residuals we apply EWMA and number of bivariate GARCH models to account for timevariation in the hedge ratio. We have applied these models to daily data for a 1-day rebalancing frequency and to weekly data for a 5-day re-balancing frequency, using data since the spider's inception until the end of 2004. Marginal differences in the 'optimal' hedge ratios are apparent, but they are simply too small to have any significant effect on the hedged portfolio volatility. In out-ofsample testing we find that the naïve hedge where an equal and opposite position is taken in the future performs as well as the more technically sophisticated models, at both the daily and the weekly re-balancing frequency. Finally, we have considered the differences between hedging the spot index and hedging the spider. The efficiency of hedging the spider is superior to that of the index and the spider hedged portfolios have significantly lower volatility than the spot index hedged portfolios.

This empirical study examines the impact of both advanced electronic trading platforms and index exchange traded funds (ETFs) on the minimum variance hedging of stock indices with futures. Our findings show that minimum variance hedging may provide an out-of-sample hedging performance that is superior to that of the naïve futures hedge, but only in markets without active trading of ETFs and advanced development of electronic communications networks. However there is no evidence to suggest that complex econometric models that include, for instance, time varying conditional covariances and error correction can improve on the simple ordinary least squares hedge ratio. Furthermore, in markets with actively traded index ETFs and where electronic trading has become established, no significant efficiency gains are apparent from any minimum variance hedge.

Las comunidades vegetales del desierto, se caracterizan por su fragilidad y condiciones muy específicas debido a los factores medioambientales. El ecosistema desierto, tiene gran importancia no sólo por la gran superficie que ocupa sino por sus características específicas y diversidad de especies que presenta, muchas de ellas en riesgo. El objetivo del estudio fue definir las clases, asociaciones vegetales y la superficie que ocupan en la zona de estudio utilizando valores de cobertura vegetal y presencia de especies. Se muestrearon 110 sitios utilizando la metodología del Gap Análisis. Se aplicó un análisis multivariado de conglomerados y se usó la técnica de varianza mínima de Ward. El resultado reflejó similaridad entre lo sitios observados de r 2 =80.93%, por lo que se obtuvo un buen grado de confiabilidad de las agrupaciones; se identificaron 3 grupos con 2 conglomerados cada uno. El grupo 1 con 23 sitios y 9 especies dominantes, el grupo 2 con 28 sitios y 2 especies dominantes y el grupo 3 con 58 sitios y 7 especies dominantes. Las especies que están presentes en mas del 50% del área de estudio fueron: Larrea tridentata y Prosopis glandulosa.

We present a portfolio model of financial intermediation in which currency choice is determined by hedging decisions on both sides of a bank's balance sheet. We show that minimum variance portfolio (MVP) allocations provide a natural benchmark to estimate the scope for dollarization of assets and liabilities (financial dollarization) as a function of macroeconomic uncertainty. Within this benchmark, we find that financial dollarization displays high persistence whenever the expected volatility of the inflation rate remains high in relation to that of the real exchange rate, even after price stabilization has been achieved. The empirical evidence confirms that MVP dollarization approximates financial dollarization closely for a broad sample of countries. 

In this paper we consider the weights of the global minimum variance portfolio (GMVP). The returns are assumed to follow a matrix elliptically contoured distribution, i.e., the returns are assumed to be neither independent nor normally distributed. A test for the general linear hypothesis is given. The distribution of the test statistic is derived under the null and the alternative hypothesis. It turns out that its distribution is invariant with respect to the type of the matrix elliptical distribution, i.e., the stochastic properties of the GMVP do not depend either on the mean vector or on the distributional assumptions imposed on asset returns. In an empirical study we analyze an international diversified portfolio.

With institutional investors increasingly involved in alternative investments, portfolio optimisation within a large universe of hedge funds has become a key area for research. This paper develops a portfolio construction model that is specifically designed for funds of hedge funds, incorporating specific controls for operational limitations, data biases and incompleteness. Absolute performance is targeted by selecting funds according to their

With institutional investors increasingly involved in alternative investments, portfolio optimisation within a large universe of hedge funds has become a key area for research. This paper develops a portfolio construction model that is specifically designed for funds of hedge funds, incorporating specific controls for operational limitations, data biases and incompleteness.

Combining only SDSS LRG and WMAP data places robust constraints on many cosmological parameters that complement prior analyses of multiple data sets. The LRGs provide independent cross-checks on Om and the baryon fraction in good agreement with WMAP. Within the context of flat LCDM models, our LRG measurements complement WMAP by sharpening the constraints on the matter density, the neutrino density and the tensor amplitude by about a factor of two, giving Omega_m=0.24+-0.02 (1 sigma), sum m_nu < 0.9 eV (95%) and r<0.3 (95%). Baryon oscillations are clearly detected and provide a robust measurement of the comoving distance to the median survey redshift z=0.35 independent of curvature and dark energy properties. Within the LCDM framework, our power spectrum measurement improves the evidence for spatial flatness, sharpening the curvature constraint Omega_tot=1.05+-0.05 from WMAP alone to Omega_tot=1.003+-0.010. Assuming Omega_tot=1, the equation of state parameter is constrained to w=-0.94+-0.09, indicating the potential for more ambitious future LRG measurements to provide precision tests of the nature of dark energy. All these constraints are essentially independent of scales k>0.1h/Mpc and associated nonlinear complications, yet agree well with more aggressive published analyses where nonlinear modeling is crucial.

The existing linear algorithms exhibit various high sensitivities to noise. The analysis presented in this paper provides insight into the causes for such high sensitivities. It is shown in this paper that even a small pixel-level perturbation may override the epipolar information that is essential for the linear algorithms to distinguish different motions. This analysis indicates the need for optimal estimation in the presence of noise. Then, we introduce methods for optimal motion and structure estimation under two situations of noise distribution: 1) known and 2) unknown. Computationally, the optimal estimation amounts to minimizing a nonlinear function. For the correct convergence of this nonlinear minimization, we use a two-step approach. The first step is using a linear algorithm to give a preliminary estimate for the parameters. The second step is minimizing the optimal objective function starting from that preliminary estimate as an initial guess. A remarkable accuracy improvement has been achieved by this two-step approach over using the linear algorithm alone. In order to assess the accuracy of the optimal solution, the error in the solution of the optimal estimation algorithm is compared with a theoretical lower error bound-CramCr-Rao bound. The simulations have shown that with Gaussian noise added to the coordinates of the image points, the actual error in the optimal solution is very close to the bound. In addition, we also use the CramCr-Rao bound to indicate the inherent instability of motion estimation from small image disparities, such as motion from optical flow. Finally, it is known that given the same nonlinear objective function and the same initial guess, different minimization methods may lead to different solutions. We investigate the performance difference between a batch least-squares technique (Levenberg-Marquardt) and a sequential least-squares technique (iterated extended Kalman filter) for this motion estimation problem, and the simulations showed that the former gives better results.

A price process is scale-invariant if and only if the returns distribution is independent of the price level. We show that scale invariance preserves the homogeneity of a pay-off function throughout the life of the claim and hence prove that standard price hedge ratios for a wide class of contingent claims are model-free. Since options on traded assets are normally priced using some form of scale-invariant process, e.g. a stochastic volatility, jump diffusion or Lévy process, this result has important implications for the hedging literature.

Ahtract-This paper is concerned with the problem of estimating the state of a linear dynamic system using noise-corrupted observations, when input disturbances and observation errors are onknown except for the fact that they belong to given bounded sets. The cases of both energy constraints and individual instantaneous constraints for the uncertain quantities are considered. In the former case, the set of possible system states compatible with the observations received is shown to be an ellipsoid, and equations for its center and weighting matrix are given, while in the latter case, equations describing a boonding ellipsoid to the set of possible states are derived. All three problems of filtering, prediction, and smoothing are examined by relating them to standard tracking problems of optimal control theory. The resulting estimators are similar in strnctnre and comparable in simplicity to the corresponding stochastic linear minimum-variance estimators, and it is shown that they provide distinct advantages over existing schemes for recursive estimation with a set-membership description of uncertainty.

In the present paper we review the stability and performance of the Generalized Minimum Variance Controller and we compare it with a modification: the Generalized Minimum Variance with Dynamic Pole Assignment Controller. We shall appreciate the characteristics of the modification, and its advantages when the process parameters are perturbed, in simulation and in a real plant.

This paper provides a tutorial introduction to the role of the time-delay or the interactor matrix in multivariate minimum variance control. Minimum variance control gives the lowest achievable output variance and thus serves as a useful benchmark for performance assessment. One of the major drawbacks of the multivariate minimum variance benchmark is the need for apriori knowledge of the multivariate time-delay matrix. A graphical method of multivariate performance assessment known as the Normalized Multivariate Impulse Response (NMIR), that does not require knowledge of the interactor, is proposed in this paper. The use of NMIR as a performance assessment tool is illustrated by application to two multivariate controllers. Two additional performance benchmarks are introduced as alternatives to the minimum variance benchmark, and their application is illustrated by a simulated example. A detailed performance evaluation of an industrial MPC controller is presented. The diagnosis steps in identifying the cause of poor performance, e.g. as due to model-plant mismatch, are illustrated on the same industrial case study.

A continuous-time mean-variance portfolio selection problem is studied where all the market coefficients are random and the wealth process under any admissible trading strategy is not allowed to be below zero at any time. The trading strategy under consideration is defined in terms of the dollar amounts, rather than the proportions of wealth, allocated in individual stocks. The problem is completely solved using a decomposition approach. Specifically, a (constrained) variance minimizing problem is formulated and its feasibility is characterized. Then, after a system of equations for two Lagrange multipliers is solved, variance minimizing portfolios are derived as the replicating portfolios of some contingent claims, and the variance minimizing frontier is obtained. Finally, the efficient frontier is identified as an appropriate portion of the variance minimizing frontier after the monotonicity of the minimum variance on the expected terminal wealth over this portion is proved and all the efficient portfolios are found. In the special case where the market coefficients are deterministic, efficient portfolios are explicitly expressed as feedback of the current wealth, and the efficient frontier is represented by parameterized equations. Our results indicate that the efficient policy for a mean-variance investor is simply to purchase a European put option that is chosen, according to his or her risk preferences, from a particular class of options.

The lack of open access to the pre-beamformed data of an ultrasound scanner has limited the research of novel imaging methods to a few privileged laboratories. To address this need, we have developed a pre-beamformed data acquisition (DAQ) system that can collect data over 128 array elements in parallel from the Ultrasonix series of research-purpose ultrasound scanners. Our DAQ system comprises three system-level blocks: 1) a connector board that interfaces with the array probe and the scanner through a probe connector port; 2) a main board that triggers DAQ and controls data transfer to a computer; and 3) four receiver boards that are each responsible for acquiring 32 channels of digitized raw data and storing them to the on-board memory. This system can acquire pre-beamformed data with 12-bit resolution when using a 40-MHz sampling rate. It houses a 16 GB RAM buffer that is sufficient to store 128 channels of pre-beamformed data for 8000 to 25 000 transmit firings, depending on imaging depth; corresponding to nearly a 2-s period in typical imaging setups. Following the acquisition, the data can be transferred through a USB 2.0 link to a computer for offline processing and analysis. To evaluate the feasibility of using the DAQ system for advanced imaging research, two proof-of-concept investigations have been conducted on beamforming and plane-wave B-flow imaging. Results show that adaptive beamforming algorithms such as the minimum variance approach can generate sharper images of a wire cross-section whose diameter is equal to the imaging wavelength (150 μm in our example). Also, planewave B-flow imaging can provide more consistent visualization of blood speckle movement given the higher temporal resolution of this imaging approach (2500 fps in our example).

Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Moreover, it is commonly accepted that estimation error in the sample mean is much larger than in the sample covariance matrix. For this reason, practitioners and researchers have recently focused on the minimum-variance portfolio, which relies solely on estimates of the covariance matrix, and thus, usually performs better out-of-sample. But even the minimum-variance portfolios are quite sensitive to estimation error and have unstable weights that fluctuate substantially over time. In this paper, we propose a class of portfolios that have better stability properties than the traditional minimum-variance portfolios. The proposed portfolios are constructed using certain robust estimators and can be computed by solving a single nonlinear program, where robust estimation and portfolio optimization are performed in a single step. We show analytically that the resulting portfolio weights are less sensitive to changes in the asset-return distribution than those of the traditional minimum-variance portfolios. Moreover, our numerical results on simulated and empirical data confirm that the proposed portfolios are more stable than the traditional minimum-variance portfolios, while preserving (or slightly improving) their relatively good out-of-sample performance.

We present a portfolio model of financial intermediation in which currency choice is determined by hedging decisions on both sides of a bank's balance sheet. We show that minimum variance portfolio (MVP) allocations provide a natural benchmark to estimate the scope for dollarization of assets and liabilities (financial dollarization) as a function of macroeconomic uncertainty. Within this benchmark, we find that financial dollarization displays high persistence whenever the expected volatility of the inflation rate remains high in relation to that of the real exchange rate, even after price stabilization has been achieved. The empirical evidence confirms that MVP dollarization approximates financial dollarization closely for a broad sample of countries. 

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This paper reviews the different indexes and benchmarks used in the control performance assessment field of industrial processes. They are usually implemented to detect and diagnose malfunctions and disturbances in industrial controllers. This survey is just an overview of the methods and tools used in the control performance assessment/monitoring (CPA/CPM) technology which has been deeply studied over the last two decades.

This paper is a perspective on the scientific contributions of the politically controversial New Zealand-born statistician Harold Silverstone . His scientific work -including a classic paper on minimum variance with A.C. Aitken -produced some key results in estimation theory. He also contributed significantly to the practical application of statistics, particularly the development of statistical consulting services in medicine.

This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to overcome the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar paramter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided for high signal-to-noise ratio (SNR) and low SNR scenarios. Finally, numerical examples are generated to compare the performances of the proposed approaches with those of the existing ones.

We examine the use of second-order stochastic dominance as both a technique for constructing portfolios and also as a way to measure performance. As a preference-free technique second-order stochastic dominance will suit any risk-averse investor, and it does not require normally distributed returns. Using in-sample data, we construct portfolios such that their second-order stochastic dominance over a benchmark is most probable. The empirical results based on 21 years of daily data suggest that this portfolio choice technique significantly outperforms the benchmark out-of-sample. Moreover, its performance is typically better -and frequently much better -than several alternative portfolio-choice approaches using equal weights, mean-variance optimization, or minimum-variance methods.

Dynamic hedging effectiveness for soybean farmers in Rondonópolis (MT) with futures contracts of BM&F is calculated through optimal hedge determination, using the bivariate GARCH BEKK model, which considers the conditional correlations of the prices series, comparing the results with the minimum variance model effectiveness, calculated by OLS, the unhedged and the naïve hedge positions. The financial effectiveness of the dynamic

Minimum-variance unbiased linear estimation theory has found many applications in the analysis of spatial rainfall and hydrogeologic data: estimation of point values or areal averages, calculation of variances of estimation error, and network design. However, the important problem of inferring the spatial structure or interdependence of the function of interest has received relatively less attention. Three methods for the estimation of the parameters of Matheron's polynomial generalized covariance function (a useful model for nonstationary functions) are presented and evaluated: Maximum likelihood (ML), minimum variance unbiased quadratic (MVUQ), and minimum norm (least squares). The estimation error covariance matrix of the parameters is calculated with each of these methods. ML and MVUQ estimation have very good properties and, for reasonably large samples, give very similar results. Minimum norm estimation is appropriate for preliminary data analysis. The application of these methodologies is illustrated with a set of rainfall data, and problems of implementation are discussed. It is shown that the estimation of the parameters of the covariance function involves sizeable errors. It is suggested that, in practice, it may be rather difficult to identify the parameters of all but simple models. It is important that future research evaluate the effects of parameter uncertainty in the circulation of estimation error variances of point or areal averages and in the design of hydrologic data collection networks.