Multivariate

Use of multivariate models in genetic evaluation requires a multivariate method for detecting erroneous outliers that cannot be detected using univariate methods. A simple rule for detecting outliers based on an approximated Mahanalobis distance was applied to Jersey data from the routine Nordic genetic evaluation in dairy cattle. Application of such is simple to implement and increased the accuracy of predicted breeding values for animals that has one or more records edited. Potential biases in evaluations for contemporary animals were also reduced. Optimum editing rules can be determined using the same data structures as used in the standard INTERBULL test for model verification.

Genetic diversity of 27 chickpea genotypes was studied through Mahalanobis D2 and Principal Component analysis. The genotypes under study fall into five clusters. The cluster II contained the highest number of genotypes (11) and Cluster I contained the lowest. Cluster I produced the highest mean value for number of pods per plant. The inter cluster distances were much higher than the intra cluster distances. Cluster V exhibited the highest intra cluster distance while the lowest distance was observed in cluster I. The highest inter cluster distance was observed between cluster I and II while the lowest was between cluster III and V. Considering all the characters, it was suggested that the genotypes BD6549, BD6603, and BD6548 could used as parents for future breeding programs to develop high yielding varieties of chickpea. DOI: http://dx.doi.org/10.3329/bjar.v37i1.11184 Bangladesh J. Agril. Res. 37(1): 129-136, March 2012  

We tested the hypothesis that effects of predation by fish on epibiota are independent of the size of fish and the area foraged. We used cages with different sizes of mesh to exclude fish of different sizes. Sizes of mesh were chosen following observations that there were small (, 200 mm TL) and large (. 200 mm TL) predatory fish at the study site. Predation by fish was intense on oysters and directly or indirectly reduced the density of the gastropod, Bembicium auratum. The cover of algae was positively affected by predation, possibly because predation on oysters created more space for algae. Predation by small fish (toadfish) was intense, but the effects of large fish were negligible. Predation was, however, independent of the sizes of experimental panels (i.e. area foraged) over the range examined (5 3 5, 10 3 10, 20 3 20 cm). Our results highlight the importance of doing experiments to test hypotheses derived from known aspects of the biology of the predators and prey being studied.

The sources and source variations for aerosol at Mace Head, Ireland, were studied by applying positive matrix factorization (PMF), a variant of factor analysis, to a 5-yr data set for bulk aerosol. Signals for the following six sources were evident year round: (1) mineral dust, (2) sea salt, (3) general pollution, (4) a secondary SO\ }Se signal that is composed of both natural (marine) and pollution (coal) components, (5) ferrous industries, (6) and a second marine (possibly biogenic) source. Analyses of seasonally strati"ed data suggested additional sources for iodine and oil emissions but these were present only in certain seasons, respectively. The marine signal is particularly strong in winter. The main pollution transport from Europe to Mace Head occurs in May, but the in#uence of continental European emissions is evident throughout the year. Mineral aerosol evidently follows a transport pathway similar to that of pollution aerosol, i.e., recirculation via the westerlies brings pollutants mixed with dust to the site from nearby land, i.e., Ireland, the United Kingdom, and the Belgium, Netherlands, and Luxemburg (Benelux) region, with some inputs from Scandinavia, Western Europe, Eastern Europe, and even the Mediterranean region. Compared with Bermuda, aerosol at Mace Head has stronger marine sources (especially marine-derived secondary SO\ and Se) but weaker crustal and oil signals. Transport across the North Atlantic, especially in winter, cannot be ruled out.

. Vol. 34, No. 2,[111][112][113][114][115][116][117][118][119][120] The opium poppy (Papaver somniferum L.) is an important medicinal plant of great pharmacopoel uses. 101 germplasm lines of different eco-geographical origin maintained at National Botanical Research Institute, Lucknow were evaluated to study the genetic divergence for seed yield/plant, opium yield/plant and its 8 component traits following multivariate and canonical analysis. The genotypes were grouped in 13 clusters and confirmed by canonical analysis. Sixty eight percent genotypes (69/101) were genetically close to each other and grouped in 6 clusters (II, III, IV, V, VIII, XII) while apparent diversity was noticed for 32 percent (32/101) of the genotypes who diversed into rest 7 clusters (I, VI, VII, IX, X, XI, XIII). Inter cluster distance ranged from 47.28 to 234.55. The maximum was between IX and X followed by VII and IX (208.30) and IX and XI (205.53). The genotypes in cluster IX, X, XI, and XII had greater potential as breeding stock by virtue of high mean values of one or more component characters and high statistical distance among them. Based on findings of high cluster mean of component trait and inter-cluster distance among clusters, a breeding plan has been discussed.

Data from a longitudinal cohort study were used to directly compare the concurrent and predictive validity of four univariate typologic approaches with a multivariate approach in subtyping drug dependence. The four univariate typologies were based upon: (a) age-of-onset of drug abuse/dependence, (b) presence of drug abuse in first-degree relatives, (c) presence of antisocial personality disorder, and (d) sex. The multivariate typologic approach was based on indices of vulnerability, chronicity, consequences, and psychopathology, yielding the Type A/B dichotomy first demonstrated in alcohol dependence. Subtypes generated from the univariate typologies were then each compared with the multivariate typology on measures of concurrent and predictive validity, and the strength of association was compared statistically. There was evidence of significantly greater concurrent validity of the Type A/B typology compared with the univariate typologies across all the domains of validation (risk, substance use, psychopathology, personality, and overall functioning). The multivariate typology also fared better than the univariate ones in all three domains on which predictive validity was evaluated: substance use, psychopathology, and overall functioning, as well as the degree of change in several composite scores (drug, medical, legal, and psychiatric) and the global psychiatric symptom index. This direct method of comparison seemed to demonstrate the superior validity of the multivariate cluster-analytic approach over the univariate approaches to classifying subjects with drug dependence.

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.

We examined patterns of geographic variation within Polypedates leucomystax using multivariate statistical analyzes of 14 morphological characters measured from 146 specimens obtained from Sumatra, Java, Kalimantan, and Sulawesi. Discriminant analysis revealed ten morphological characters and found that the Sulawesi population is morphologically distinct from all others considered. Following the Evolutionary Species Concept, we describe the Sulawesi population as a new species in P. leucomystax species complex.

ALGUNAS ESPECTROSCOPIAS OPTICAS EXISTEN DESDE HACE MAS DE DIEZ DECADAS; SIN EMBARGO, EN RECIENTES ANOS ESTAS TECNICAS SE HAN APLICADO EN DISTINTAS AREAS DEL CONOCIMIENTO. POR OTRO LADO, LA MEDICINA Y LA BIOLOGIA SON DOS AREAS DE INVESTIGACION DONDE LA OPTICA PUEDE FACILITAR EL ESTUDIO Y CARACTERIZACION DE TEJIDOS Y MOLECULAS BIOLOGICAS CON EL FIN DE MEJORAR EL DIAGNOSTICO CLINICO. LAS ESPECTROSCOPIAS OPTICAS PUEDEN AYUDAR EN EL ESTUDIO Y DETECCION DE ALGUNAS ENFERMEDADES DE MANERA MAS RAPIDA QUE LAS TECNICAS ESTANDAR DE LABORATORIO. EN ESTE TRABAJO, MOSTRAMOS ALGUNAS APLICACIONES DE LA ESPECTROSCOPIA RAMAN Y DEL ANALISIS MULTIVARIANTE EN ALGUNOS PROBLEMAS ESPECIFICOS DEL AREA BIOMEDICA, POR EJEMPLO: DETECCION DE CANCER DE MAMA, DIAGNOSTICO DE LA TOXOPLASMOSIS E IDENTIFICACION DE ISOTIPOS (IGG E IGM). Abstract ALTHOUGH SOME OPTICAL SPECTROSCOPY METHODS WERE INTRODUCED MORE THAN TEN DECADES AGO, THEY ARE STILL FINDING NEW APPLICATIONS IN MANY AREAS OF CIENCE. SPECIFICALLY, MEDICIN AND...

We review aspects of extreme value modelling relevant to characterisation of ocean environments and the design of marine structures, summarising basic concepts, modelling with covariates and multivariate modelling (including conditional and spatial extremes). We outline Bayesian inference for extremes and reference software resources for extreme value modelling. Extreme value analysis is inherently different to other empirical modelling, in that estimating the tail (rather than the body) of a distribution from a sample of data, and extrapolation beyond the sample (rather than interpolation within) is demanded. Intuition accumulated from other areas of empirical modelling can be misleading. Careful consideration of the effects of sample size, measurement scale, threshold selection and serial dependence, associated uncertainties and implications of choices made is essential. Incorporation of covariate effects when necessary improves inference. Suitable tools (e.g. based on additive models, splines, random fields, spatial processes) have been developed, but their use is restricted in general to academia. Effective modelling of multivariate extremes will improve the specification of design conditions for systems whose response cannot be easily characterised in terms of one variable. Approaches such as the conditional extremes model are easily implemented, and provide generalisations of existing marine design approaches (e.g. for primary and associated variables). Software is available, but again generally only for academic use. Modelling spatial dependence rigourously will provide single extreme value models applicable to spatial neighbourhoods including complete ocean basins, avoiding the need for procedures such as site pooling. Indeed, once the model is established, the metocean engineer may not ever need to perform further extreme value analysis for that basin in principle. Spatial extremes is an area of active research in the statistics community. A limited number of appropriate models have been deployed (e.g. for precipitation, temperature and metocean applications). Software is available, but again for specialist use. Bayesian inference provides a consistent framework for inference and is rapidly becoming the standard approach in academia. It appears inevitable that, in time, Bayesian inference will also be regarded as the standard in ocean engineering applications. Implementation of Bayesian methods requires some expertise. Software is available, but again generally only used by statistical specialists.

Pragmatical, visually oriented methods for assessing and optimising bi-linear regression models are described, and ap-Ž . plied to PLS Regression PLSR analysis of multi-response data from controlled experiments. The paper outlines some ways to stabilise the PLSR method to extend its range of applicability to the analysis of effects in designed experiments. Two ways of passifying unreliable variables are shown. A method for estimating the reliability of the cross-validated prediction error RMSEP is demonstrated. Some recently developed jack-knifing extensions are illustrated, for estimating the reliability of the linear and bi-linear model parameter estimates. The paper illustrates how the obtained PLSR AsignificanceB probabilities are similar to those from conventional factorial ANOVA, but the PLSR is shown to give important additional overview plots of the main relevant structures in the multi-response data.

In this work parametric and non-parametric statistical methods are proposed to analyze Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise. We test this model using Monte Carlo (MC) simulations of DT-MRI experiments. The non-parametric approach proposed here is an implementation of bootstrap methodology that we call the DT-MRI bootstrap. It is used to estimate an empirical probability distribution of experimental DT-MRI data, and to perform hypothesis tests on them. The DT-MRI bootstrap is also used to obtain various statistics of DT-MRI parameters within a single voxel, and within a region of interest (ROI); we also use the bootstrap to study the intrinsic variability of these parameters in the ROI, independent of background noise. We evaluate the DT-MRI bootstrap using MC simulations and apply it to DT-MRI data acquired on human brain in vivo, and on a phantom with uniform diffusion properties. Published by Elsevier Science (USA).

This working paper assesses self-reported measures of subjective well-being in later life. In the first place, an overview of the theoretical background of a number of measures, focusing on those present in the English Longitudinal Study of Ageing (ELSA), is given. Secondly, the structure of these measurements and the interrelations between them are tested using confirmatory factor analysis. Thirdly, the cross-cultural measurement equivalence of the CASP-scale, a eudaimonic measure developed specifically for older adults, is testing using the Survey of Health, Ageing and Retirement in Europe (SHARE). These analyses reveal that it makes sense to distinguish affective, cognitive and eudaimonic measures of well-being empirically, but that these measures are more closely interrelated than one would expect on the base of theory alone. The analysis on CASP in SHARE reveals that the scale can be used to investigate differences in eudaimonic and hedonic subjective well-being across Europe, as partial scalar measurement equivalence is confirmed.

Different varieties of chili sauces that are commercially available in Malaysia were randomly collected and their physical and chemical parameters were determined. Initially, pattern recognition techniques were applied in order to categorize the chili sauce varieties. With the aid of principal component analysis and cluster analysis, it is possible to visualize the clustering tendencies of the different varieties of chili sauces where four major clusters, namely chili sauces, general chili ketchups, hot/ garlic added ketchups, and Thai ketchups have been successfully partitioned. Color and taste parameters have been found to be the most discriminating variables among them. With the K-nearest neighbor technique, a good prediction of chili sauces' categories could be achieved. With appropriate customer preference survey, it would be possible to map these characteristics to the preferences. This would certainly help producers of chili sauces in upgrading the quality and taste of their products.

The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982 International Journal of Forecasting 1985. During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

Introduzida por Karl Pearson em 1901. On lines and planes of closest fit to systems of point in space, Philosophical Magazine, Series 6, vol. 2, no. 11, pp. 559-572. EVERITT, B. An R and S-Plus companion to multivariate analysis, Springer-Verlag, London, 2007. FÁVERO, L.P.; BELFIORE, P.; SILVA, F.L.; CHAN, B.L. Análise de dados: modelagem multivariada para tomada de decisões, Campus, Rio de Janeiro, 2009. JOHNSON, R.A. & WICHERN, D.W. Applied Multivariate Statistical Analysis, 5th edition, Prentice Hall, New Jersey, 2002. LATTIN, J.; CARROLL, J.D.; GREEN, P.E. Análise de dados multivariados, Cengage learning, São Paulo, 2011. LEBART, L.; MORINEAU, A.; PIRON, M. Statistique Exploratoire Multidimensionelle , 3e édition, Dunod, Paris, 2000. MINGOTI, S.A. Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada, Editora UFMG, Belo Horizonte, 2005. SOUSA, V.D.; OLIVEIRA, V.N. Análise do desempenho dos atacantes do campeonato brasileiro 2013 por meio da análise de componentes principais, Projeto Final, UERJ, 2014.

Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology.

In this paper, we discuss the quality of different hedging strategies that use the Mexican Stock Market Index Futures, traded in the Mexican Derivatives Market (MexDer) contract to minimize the impact of stock price fluctuations on the value of a diversified portfolio, for a period from January 3, 2000, through November 11, 2014.
We review six possible hedging strategies: a) No Hedge; b) Constant Hedge based on an OLS regression using the HAC Newey Covariance Matrix Estimation; and c) four hedging strategies based on Dynamic Conditional Correlation Multivariate GARCH models with variations.
Finally, we evaluate the quality of all the strategies using Kupiec’s (1995) technique. Our results indicate that the different Dynamic Conditional Correlation M- GARCH models eliminate risk in a more efficient manner. More specifically, the CCC model and the Diagonal VEC, both with asymmetric responses, produce the best possible results. However, in terms of final returns, the best performing strategy is the No-Hedge strategy.

The present study compared the fit of several IRT models to two personality assessment instruments. Data from 13,059 individuals responding to the US-English version of the Fifth Edition of the Sixteen Personality Factor Questionnaire (16PF) and 1,770 individuals responding to Goldberg's 50 item Big Five Personality measure were analyzed. Various issues pertaining to the fit of the IRT models to personality data were considered. We examined two of the most popular parametric models designed for dichotomously scored items (i.e., the two-and three-parameter logistic models) and a parametric model for polytomous items (Samejima's graded response model). Also examined were Levine's nonparametric maximum likelihood formula scoring models for dichotomous and polytomous data, which were previously found to provide good fits to several cognitive ability tests . The two-and threeparameter logistic models fit some scales reasonably well but not others; the graded response model generally did not fit well. The nonparametric formula scoring models provided the best fit of the models considered. Several implications of these findings for personality measurement and personnel selection were described.

A study of vegetation and different parameters of soil and water of Lahore District was carried out in this investigation. Vegetation data was evaluated by using multivariate analysis methods i.e., Tow-way Indicator Species Analysis (TWINSPAN) and Canonical Correspondence Analysis (CCA). About seventy eight species belonging to thirty seven families were recorded from forty quadrats in Lahore district. Two major (plant associations) and six sub plant communities (sub associations) were recognized in the study area. The study also considered the relationship of vegetation structure to certain environmental aspects. This relationship was determined by CANOCO analysis. The distribution of plant species with respect to environmental variables indicated that water pH, soil EC, water content and water table showed a great influence upon species distribution. This study also gives useful information to protect and improve the present vegetation cover for conservation of local flora.

The paper reports on both methodological and substantive findings. It presents a method for generating simplified representations for regional urban populations, their geographical sub-populations and communities. the method generates greatly simplified high-resolution socio-economic profiles of populated geographical areas from complex large census data sets.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

The key to sustained business performance for any organisation lies undoubtedly with the customer loyalty towards that organisation. But to keep them remain loyal towards the organisation is an important challenge for most of the businesses today across the globe and across the sectors like hotels, banks, etc. The present study views a hotel as an integrated system that needs to be managed and measured to enhance the customer's overall satisfaction to enhance customer loyalty to maximise the hotel's performance. The present study tries to explore: 1) how the overall consumption experience has direct impact on satisfaction; 2) satisfaction in-turn has direct impact on brand perception; 3) how both the satisfaction and brand perception have combined effect on loyalty. Hence, an attempt was made to study the explicit linkages that extend from internal processes to customer satisfaction to customer loyalty. The framework of research is designed to be: multivariate, disaggregated and cross-sectional where the derivation of multivariate models depicts significant strategic value for business unit managers where it acknowledges that key drivers of customer satisfaction may differ across chain affiliated hotels.

Μηραήι Θεόδωξνο Σζαγξήο ΓΙΑΣΡΙΒΗ Πνπ ππνβιήζεθε ζην Σκήκα ηαηηζηηθήο ηνπ Οηθνλνκηθνύ Παλεπηζηεκίνπ ΑΘελώλ ωο κέξνο ηωλ απαηηήζεωλ γηα ηελ απόθηεζε Μεηαπηπρηαθνύ Γηπιώκαηνο Δηδίθεπζεο ζηε ηαηηζηηθή ABSTRACT Michael Tsagris Multivariate outliers, the Forward Search and the Cronbach's reliability coefficient May 2010 Multivariate outliers are of very interest due to the nature of th e data. While in the univariate case, things are straightforward, when moving to more than one variables things can be very difficult. In this thesis, multivariate outlier detection methods are discussed and the Forward Search is also implemented. Robust estimates of scatter and location is the key feature for the detection of outliers. Finally, the Cronbach's reliability coefficient is discussed and applied to the Forward Search as a monitoring statistic. V Ι VI Ι ΠΕΡΙΛΗΨΗ Μηραήι Σζαγξήο Πολςμεταβλητέρ ακπαίερ τιμέρ, η Βηματική Ανίσνεςση και ο σςντελεστήρ αξιοπιστίαρ τος Cronbach Μάηνο 2010 Οη πνιπκεηαβιεηέο αθξαίεο ηηκέο έρνπλ κεγάιν ελδηαθέξνλ ιόγω ηεο θύζεο ηωλ δεδνκέλωλ. Δλώ ζηε κνλνκεηαβιεηή πεξίπηωζε ηα πξάγκαηα είλαη μεθάζαξα, θαζώο πεξλάκε ζε πεξηζζόηεξεο από κία κεηαβιεηή ηα πξάγκαηα κπνξεί λα γίλνπλ πνιύ δύζθνια. ε απηή ηε δηαηξηβή, κέζνδνη αλίρλεπζεο πνιπκεηαβιεηώλ αθξαίωλ ηηκώλ είλαη ππό ζπδήηεζε θαη πινπνηείηαη ε Βεκαηηθή Αλίρλεπζε. Οη εύξωζηεο εθηηκήζεηο ηεο δηαζπνξάο θαη ηεο ζέζεο είλαη ην βαζηθό ραξαθηεξηζηηθό γηα ηελ αλίρλεπζε ηωλ αθξαίωλ ηηκώλ. Σέινο, ν ζπληειεζηήο αμηνπηζηίαο ηνπ Cronbach είλαη ππό ζπδήηεζε θαη εθαξκόδεηαη ζηε Βεκαηηθή Αλίρλεπζε ωο ζηαηηζηηθό monitoring. VII Ι VIII 1

Most algorithms for highly robust estimators of multivariate location and scatter start by drawing a large number of random subsets. For instance, the FASTMCD algorithm of Rousseeuw and Van Driessen (1999) starts in this way, and then takes so-called concentration steps to obtain a more accurate approximation to the MCD. The FASTMCD algorithm is affine equivariant but not permutation invariant. In this article we present a deterministic algorithm, denoted as DetMCD, which does not use random subsets and is even faster. It computes a small number of deterministic initial estimators, followed by concentration steps. DetMCD is permutation invariant and very close to affine equivariant. We compare it to FASTMCD and to the OGK estimator of . We also illustrate it on real and simulated data sets, with applications involving principal component analysis, classification and time series analysis. Supplemental material (Matlab code of the DetMCD algorithm and the data sets) is available online.

The need for small area estimates is increasingly felt in both the public and private sectors in order to formulate their strategic plans. It is now widely recognized that direct small area survey estimates are highly unreliable owing to large standard errors and coefficients of variation. The reason behind this is that a survey is usually designed to achieve a specified level of accuracy at a higher level of geography than that of small areas. Lack of additional resources makes it almost imperative to use the same data to produce small area estimates. For example, if a survey is designed to estimate per capita income for a state, the same survey data need to be used to produce similar estimates for counties, subcounties and census divisions within that state. Thus, by necessity, small area estimation needs explicit, or at least implicit, use of models to link these areas. Improved small area estimates are found by "borrowing strength" from similar neighboring areas.

This study aimed at using a multivariate approach to describe the body structure of Djallonke sheep in northern Ghana and to determine which approach explains better the variation in body composition. Live weight (LW) and linear body measurements including heart girth (HG), neck girth (NG), chest depth (CD), height at withers (HW), rump height (RH), body length (BL) and pin-bone width (PBW) were obtained from 172 sheep aged between two and three years. The fixed effects of sex and age were tested using the general linear model (GLM) while the Nearest Neighbor method of Hierarchical Cluster Analysis was used to group body traits into clusters. Principal Component Factor Analysis was used to describe the variation in body traits where extracted factors were varimax rotated to enhance interpretability. The analysis of variance revealed significant (P < 0.01) differences in the morphological traits of the two sexes with higher values recorded for the male in all traits examined except in PBW, which was insignificant (P > 0.05). Age had no significant influence (P > 0.05) on the body traits. The sheep weighed 26.92 ± 0.89 kg averagely and had averages of other body measurements to be: 71.74 ± 1.23, 40.52 ± 0.79, 27.73 ± 0.52, 60.72 ± 0.86, 59.61 ± 0.87, 58.87 ± 1.06 and 12.81 ± 0.23 cm for HG, NG, CD, HW, RH, BL and PBW, respectively. The product moments of correlation were positive and significant (P < 0.05, 0.01; r = 0.18-0.99) for all pairs of traits. The body traits were categorized mainly into two clusters with the first cluster comprising the HG, HW, RH and BL while NG, CD and PBW formed the second cluster. The grouping of the traits was slightly different in Factor analysis where two underlying principal components (PC) were extracted to discern the variance structure of the Djallonke sheep. The first principal component which consisted of CD, HW, RH, BL and PBW explained 61.26 percent and the second, 12.92 percent thereby giving a maximum of 74.17 percent generalized variance in body measurements. The traits loaded on the first principal component are closely associated with bone growth hence describing the general body size conformation while the traits (HG and NG) on the second component seem to describe only the thoracic region. It can be concluded that both the Hierarchical Cluster analysis and the Factor analysis grouped body traits similarly but the later is to be recommended because of the additional ability of indicating the amount of variation explained by the developed factors.

The effects of changes in the landscape and alteration of natural flow process on aquatic macroinvertebrate assemblages were investigated in 67 small-to-medium sized (15-526 km 2 ) upland streams in the northeastern United States. Environmental characteristics that were found to be important in determining macroinvertebrate-assemblage composition include urbanization and concomitant changes in natural streamflow patterns. In particular, hydrologic attributes accounted for a significant proportion of the variability and were important in driving modifications to assemblage structure after natural environmental variability was extracted. For example, mean April flow accounted for the greatest amount of assemblage variability in any single multiple linear regression (MLR) model (65%) and duration of high flows accounted for a significant portion of the assemblage variability in the five, four and one-variable models (25, 26, and 23%, respectively). Seasonal predictability of low flow consistently accounted for a significant proportion of the assemblage variability in all but the two-variable (MLR) model. Significant (p < 0Ð05) bivariate flow-ecology response relations were established, especially for hydrologic measures that account for the frequency, duration, and magnitude of flow events, and these relations generally followed increasing or decreasing trends that would be expected given changes in stream hydrology. This study demonstrates that there are likely specific negative consequences to stream biotic integrity in northeastern streams as the result of hydrologic alteration associated with basin urbanization. Understanding the relations between hydrologic modification and aquatic assemblages will help efforts to set sustainable flow standards for protection of aquatic assemblages while providing water for human needs. Published in

The aim was to develop a valid and reliable measurement tool for mobbing among educators. This study’s subscales include aggressive acts that can trigger mobbing, causes of mobbing and coping strategies against mobbing. The sample study were teachers (n=596) and school administrators (n=381) from public elementary schools and teachers (n=593) and school administrators (n=272) from private elementary schools. While teachers were 61% female and 39% male; school administrators were 35% male and 65% female. Data was gathered using the ‘mobbing questionnaire’ that was specifically developed in order to determine factor structure of the scale by applying exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). For reliability of the scale, Cronbach Alfa coefficients of the subscales were calculated. Results reveal that the mobbing scale is valid and reliable. The analysis of its application provides empirical insights about how educators can be mobbed in the Turkish educational system. The results indicate that female educators were mobbed more than their male counterparts. Since the mobbing scale has been proved to be valid and reliable, it can be used for other professions too. While it has been developed for Turkish culture, researchers, workers and managers can customise the instrument to detect similar mobbing situations in other cultures. Thus this paper includes the development of a valid and reliable measurement tool to determine negative acts that can trigger mobbing, reasons for mobbing and coping strategies against mobbing.

For the last five years, the variants of the Newton's method with cubic convergence have become popular iterative methods to find approximate solutions to the roots of non-linear equations. These methods both enjoy cubic convergence at simple roots and do not require the evaluation of second order derivatives. In this paper, we investigate about the relationship between these methods which are in fact based on the approximation of the second order derivative present in the third order limited Taylor expansion. We also prove that they are different forms of the Halley method and are all contractive iterative methods in a common neighbourhood. We extend some of these variants to multivariate cases and prove their respective local cubic convergence from their corresponding linear models.

We define a distribution on the unit sphere S d−1 called the elliptically symmetric angular Gaussian distribution. This distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian distribution closely analogous to the Kent subfamily of the general Fisher–Bingham distribution. Like the Kent distribution, it has ellipse-like contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe.

This study represents the first application of multi-way calibration by N-PLS and multi-way curve resolution by PARAFAC to 2D diffusion-edited 1H NMR spectra. The aim of the analysis was to evaluate the potential for quantification of lipoprotein main- and subfractions in human plasma samples. Multi-way N-PLS calibrations relating the methyl and methylene peaks of lipoprotein lipids to concentrations of the