Usual estimation methods for the parameters of extreme values distribution employ only a few valu... more Usual estimation methods for the parameters of extreme values distribution employ only a few values, wasting a lot of information. More precisely, in the case of the Gumbel distribution, only the block maxima values are used. In this work, we propose a method to seize all the available information in order to increase the accuracy of the estimations. This intent can be achieved by taking advantage of the existing relationship between the parameters of the baseline distribution, which generates data from the full sample space, and the ones for the limit Gumbel distribution. In this way, an informative prior distribution can be obtained. Different statistical tests are used to compare the behaviour of our method with the standard one, showing that the proposed method performs well when dealing with very shortened available data. The empirical effectiveness
In the parameter estimation of limit extreme value distributions, most employed methods only use ... more In the parameter estimation of limit extreme value distributions, most employed methods only use some of the available data. Using the peaks-over-threshold method for Generalized Pareto Distribution (GPD), only the observations above a certain threshold are considered; therefore, a big amount of information is wasted. The aim of this work is to make the most of the information provided by the observations in order to improve the accuracy of Bayesian parameter estimation. We present two new Bayesian methods to estimate the parameters of the GPD, taking into account the whole data set from the baseline distribution and the existing relations between the baseline and the limit GPD parameters in order to define highly informative priors. We make a comparison between the Bayesian Metropolis–Hastings algorithm with data over the threshold and the new methods when the baseline distribution is a stable distribution, whose properties assure we can reduce the problem to study standard distrib...
2018 26th European Signal Processing Conference (EUSIPCO), 2018
Addressing dependent data as independent has become usual for Parkinson's Disease (PD) detection ... more Addressing dependent data as independent has become usual for Parkinson's Disease (PD) detection by using features extracted from replicated voice recordings. A binary regression model with an Asymmetric Student t (AST) distribution as link function has been developed in a classification context by taking into account the within-subject dependence. This opens the possibility of handling situations in which the probabilities of the binary response approach 0 and 1 at different rates. The computational issue has been addressed by proposing and using a representation based on a mixture of normal distributions for the AST distribution. This allows to include latent variables to derive a Gibbs sampling algorithm that is used to generate samples from the posterior distribution. The applicability of the proposed approach has been tested with a simulation-based experiment and has been applied to a real dataset for PD detection.
Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 2012
In this work, a novel classification method is proposed. The method uses a Bayesian regression mo... more In this work, a novel classification method is proposed. The method uses a Bayesian regression model in a pairwise comparison framework. As a result, we obtain an automatic classification tool that allows new cases to be classified without the interaction of the user. The differences with other classification methods, are the two innovative relevance feedback tools for an iterative classification process. The first one is the information obtained from user after validating the results of the automatic classification. The second difference is the continuous adaptive distribution of the model's parameters. It also has the advantage that can be used with problems with both a large number of characteristics and few number of elements. The method could be specially helpful for those professionals who have to make a decision based on images classification, such as doctors to determine the diagnosis of patients, meteorologists, traffic police to detect license plate, etc.
Motivated by a longitudinal oral health study, the Signal-Tandmobiel R study, a Bayesian approach... more Motivated by a longitudinal oral health study, the Signal-Tandmobiel R study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulationbased example and the analysis of the motivating study.
Computer Aided Systems Theory - EUROCAST 2003, 2003
We explore the possibilities of Markov Chain Monte Carlo simulation methods to solve sequential d... more We explore the possibilities of Markov Chain Monte Carlo simulation methods to solve sequential decision processes evolving stochastically in time. The application areas of such processes are fairly wide, embedded typically in the Decision Analysis framework, such as preventive maintenance of systems, where we shall find our illustrative examples.
Factors like multiple uncertainty sources, multiple objectives, time-effects over preferences and... more Factors like multiple uncertainty sources, multiple objectives, time-effects over preferences and the hierarchical nature of the planning process increase the complexity of reservoir management problems. We describe developments in a methodology for reservoir operations and its implementation in an intelligent decision support system.
Bayes decision problems require subjective elicitation of inputs: beliefs and preferences. Elicit... more Bayes decision problems require subjective elicitation of inputs: beliefs and preferences. Elicitation methods are far from simple, and elicited quantities cannot perfectly represent the judgements of the Decision Maker. Several foundations suggest overlaying this problem using robust approaches. In these models, beliefs are modelled by a class of probability distributions, and preferences by a class of loss functions. Then we have a Pareto order. So, the solution concept is an efficient set. In this paper, we deal with the problem of existence of a solution. Moreover, we study the relations between Bayes actions and nondominated ones.
Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs.... more Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the subject, one that is accessible to statisticians outside the field. Recent developments in the area are also reviewed, though with very uneven emphasis. The topics to be covered are as follows:
Many important problems in Operations Research and Statistics require the computation of nondomin... more Many important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or-constrained problems, or discretizations-based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation-based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size.
The evolution of image techniques in medicine has improved decision making based on physicians' e... more The evolution of image techniques in medicine has improved decision making based on physicians' experience by means of computer-aided diagnosis (CAD). This paper focuses on the development of content-based image retrieval (CBIR) and CAD techniques applied to bronchoscopies and according to different pathologies. A novel pairwise comparison method based on binary logistic regression is developed to determine those images must alike to a new image from incomplete property information, after accounting for the physicians' appreciation of the image similarity. This method is particularly useful when problems with both a large number of features and few images are involved. Keywords Computer vision • CBIR • Logistic regression • Computer-aided diagnosis • Similarity 1 Introduction Research into image retrieval has steadily gained high recognition over the past few years as a result of the great
2014 22nd European Signal Processing Conference (EUSIPCO), 2014
Subject-based approaches are proposed to automatically discriminate healthy people from those wit... more Subject-based approaches are proposed to automatically discriminate healthy people from those with Parkinson's Disease (PD) by using speech recordings. These approaches have been applied to one of the most used PD datasets, which contains repeated measurements in an imbalanced design. Most of the published methodologies applied to perform classification from this dataset fail to account for the dependent nature of the data. This fact artificially increases the sample size and leads to a diffuse criterion to define which subject is suffering from PD. The first proposed approach is based on data aggregation. This reduces the sample size, but defines a clear criterion to discriminate subjects. The second one handles repeated measurements by introducing latent variables in a Bayesian logistic regression framework. The proposed approaches are conceptually simple and easy to implement.
Bayesian decision theory plays a significant role in a large number of applications that have as ... more Bayesian decision theory plays a significant role in a large number of applications that have as main aim decision making. At the same time, negotiation is a process of making joint decisions that has one of its main foundations in decision theory. In this context, an important issue involved in industrial and commercial applications is product reliability/quality demonstration. The goal is, among others, product commercialization with the best possible price. This paper provides a Bayesian sequential negotiation model in the context of sale of a product based on two characteristics: product price and reliability/quality testing. The model assumes several parties, a manufacturer and different consumers, who could be considered adversaries. In addition, a general setting for which the manufacturer offers a product batch to the consumers is taken. Both the manufacturer and the consumers have to use their prior beliefs as well as their preferences. Sometimes, the model will require to update the previous beliefs. This can be made through the corresponding posterior distribution. Anyway, the main aim is that at least one consumer accepts the product batch based on either product price or product price and reliability/quality. The general model is solved from the manufacturer viewpoint. Thus a general approach that allows us to calculate an optimal price and sample size for testing is provided. Finally, two applications show how the proposed technique can be applied in practice.
Este trabajo propone un metodo general para el analisis Bayesiano de las familias exponenciales... more Este trabajo propone un metodo general para el analisis Bayesiano de las familias exponenciales naturales con varianza cuadratica en presencia de distintas fuentes de informaci on a priori. La informacion obtenida de cada fuente se representa como una distribuci on a priori conjugada. Posteriormente, se propone un modelo de mixtura para expresar un consenso entre las fuentes. Se considera el caso en el que los pesos son desconocidos y estos se calculan utilizando un metodo basado en distancias de Kullback-Leibler (KL). Una ventaja es que el procedimiento conduce a una solucion analtica. Ademas, es posible una implementacion directa para todas las familias de la clase. Finalmente, se analizan las discrepancias entre la decision nal y las decisiones individuales usando distancias de KL. Estas distancias se estiman mediante un procedimiento de bajo coste computacional basado en el metodo Montecarlo.
Motivated by a study tracking the progression of Parkinson's disease (PD) based on features e... more Motivated by a study tracking the progression of Parkinson's disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.
Usual estimation methods for the parameters of extreme value distributions only employ a small pa... more Usual estimation methods for the parameters of extreme value distributions only employ a small part of the observation values. When block maxima values are considered, many data are discarded, and therefore a lot of information is wasted. We develop a model to seize the whole data available in an extreme value framework. The key is to take advantage of the existing relation between the baseline parameters and the parameters of the block maxima distribution. We propose two methods to perform Bayesian estimation. Baseline distribution method (BDM) consists in computing estimations for the baseline parameters with all the data, and then making a transformation to compute estimations for the block maxima parameters. Improved baseline method (IBDM) is a refinement of the initial idea, with the aim of assigning more importance to the block maxima data than to the baseline values, performed by applying BDM to develop an improved prior distribution. We compare empirically these new methods ...
Vocal fold nodules are recognized as an occupational disease for all collective of workers perfor... more Vocal fold nodules are recognized as an occupational disease for all collective of workers performing activities for which maintained and continued use of voice is required. Computer-aided systems based on features extracted from voice recordings have been considered as potential noninvasive and low cost tools to diagnose some voice-related diseases. A Bayesian decision analysis approach has been proposed to classify university lectures in three levels of risk: low, medium, and high, based on the information provided by acoustic features extracted from healthy controls and people suffering from vocal fold nodules. The proposed risk groups are associated with different treatments. The approach is based on the calculation of posterior probabilities of developing vocal fold nodules and considers utility functions that include the financial cost and the probability of recovery for the corresponding treatment. Maximization of the expected utilities is considered. By using this approach, the risk of having vocal fold nodules is identified for each university lecturer, so he/she can be properly assigned to the right treatment. The approach has been applied to university lecturers according to the Disease Prevention Program of the University of Extremadura. However, it can also be applied to other voice professionals (singers, speakers, coaches, actors…).
Computer methods and programs in biomedicine, 2017
In the scientific literature, there is a lack of variable selection and classification methods co... more In the scientific literature, there is a lack of variable selection and classification methods considering replicated data. The problem motivating this work consists in the discrimination of people suffering Parkinson's disease from healthy subjects based on acoustic features automatically extracted from replicated voice recordings. A two-stage variable selection and classification approach has been developed to properly match the replication-based experimental design. The way the statistical approach has been specified allows that the computational problems are solved by using an easy-to-implement Gibbs sampling algorithm. The proposed approach produces an acceptable predictive capacity for PD discrimination with the considered database, despite the fact that the sample size is relatively small. Specifically, the accuracy rate, sensitivity and specificity are 86.2%, 82.5%, and 90.0%, respectively. However, the most important fact is that there is an improvement in the interpret...
Usual estimation methods for the parameters of extreme values distribution employ only a few valu... more Usual estimation methods for the parameters of extreme values distribution employ only a few values, wasting a lot of information. More precisely, in the case of the Gumbel distribution, only the block maxima values are used. In this work, we propose a method to seize all the available information in order to increase the accuracy of the estimations. This intent can be achieved by taking advantage of the existing relationship between the parameters of the baseline distribution, which generates data from the full sample space, and the ones for the limit Gumbel distribution. In this way, an informative prior distribution can be obtained. Different statistical tests are used to compare the behaviour of our method with the standard one, showing that the proposed method performs well when dealing with very shortened available data. The empirical effectiveness
In the parameter estimation of limit extreme value distributions, most employed methods only use ... more In the parameter estimation of limit extreme value distributions, most employed methods only use some of the available data. Using the peaks-over-threshold method for Generalized Pareto Distribution (GPD), only the observations above a certain threshold are considered; therefore, a big amount of information is wasted. The aim of this work is to make the most of the information provided by the observations in order to improve the accuracy of Bayesian parameter estimation. We present two new Bayesian methods to estimate the parameters of the GPD, taking into account the whole data set from the baseline distribution and the existing relations between the baseline and the limit GPD parameters in order to define highly informative priors. We make a comparison between the Bayesian Metropolis–Hastings algorithm with data over the threshold and the new methods when the baseline distribution is a stable distribution, whose properties assure we can reduce the problem to study standard distrib...
2018 26th European Signal Processing Conference (EUSIPCO), 2018
Addressing dependent data as independent has become usual for Parkinson's Disease (PD) detection ... more Addressing dependent data as independent has become usual for Parkinson's Disease (PD) detection by using features extracted from replicated voice recordings. A binary regression model with an Asymmetric Student t (AST) distribution as link function has been developed in a classification context by taking into account the within-subject dependence. This opens the possibility of handling situations in which the probabilities of the binary response approach 0 and 1 at different rates. The computational issue has been addressed by proposing and using a representation based on a mixture of normal distributions for the AST distribution. This allows to include latent variables to derive a Gibbs sampling algorithm that is used to generate samples from the posterior distribution. The applicability of the proposed approach has been tested with a simulation-based experiment and has been applied to a real dataset for PD detection.
Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 2012
In this work, a novel classification method is proposed. The method uses a Bayesian regression mo... more In this work, a novel classification method is proposed. The method uses a Bayesian regression model in a pairwise comparison framework. As a result, we obtain an automatic classification tool that allows new cases to be classified without the interaction of the user. The differences with other classification methods, are the two innovative relevance feedback tools for an iterative classification process. The first one is the information obtained from user after validating the results of the automatic classification. The second difference is the continuous adaptive distribution of the model's parameters. It also has the advantage that can be used with problems with both a large number of characteristics and few number of elements. The method could be specially helpful for those professionals who have to make a decision based on images classification, such as doctors to determine the diagnosis of patients, meteorologists, traffic police to detect license plate, etc.
Motivated by a longitudinal oral health study, the Signal-Tandmobiel R study, a Bayesian approach... more Motivated by a longitudinal oral health study, the Signal-Tandmobiel R study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulationbased example and the analysis of the motivating study.
Computer Aided Systems Theory - EUROCAST 2003, 2003
We explore the possibilities of Markov Chain Monte Carlo simulation methods to solve sequential d... more We explore the possibilities of Markov Chain Monte Carlo simulation methods to solve sequential decision processes evolving stochastically in time. The application areas of such processes are fairly wide, embedded typically in the Decision Analysis framework, such as preventive maintenance of systems, where we shall find our illustrative examples.
Factors like multiple uncertainty sources, multiple objectives, time-effects over preferences and... more Factors like multiple uncertainty sources, multiple objectives, time-effects over preferences and the hierarchical nature of the planning process increase the complexity of reservoir management problems. We describe developments in a methodology for reservoir operations and its implementation in an intelligent decision support system.
Bayes decision problems require subjective elicitation of inputs: beliefs and preferences. Elicit... more Bayes decision problems require subjective elicitation of inputs: beliefs and preferences. Elicitation methods are far from simple, and elicited quantities cannot perfectly represent the judgements of the Decision Maker. Several foundations suggest overlaying this problem using robust approaches. In these models, beliefs are modelled by a class of probability distributions, and preferences by a class of loss functions. Then we have a Pareto order. So, the solution concept is an efficient set. In this paper, we deal with the problem of existence of a solution. Moreover, we study the relations between Bayes actions and nondominated ones.
Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs.... more Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the subject, one that is accessible to statisticians outside the field. Recent developments in the area are also reviewed, though with very uneven emphasis. The topics to be covered are as follows:
Many important problems in Operations Research and Statistics require the computation of nondomin... more Many important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or-constrained problems, or discretizations-based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation-based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size.
The evolution of image techniques in medicine has improved decision making based on physicians' e... more The evolution of image techniques in medicine has improved decision making based on physicians' experience by means of computer-aided diagnosis (CAD). This paper focuses on the development of content-based image retrieval (CBIR) and CAD techniques applied to bronchoscopies and according to different pathologies. A novel pairwise comparison method based on binary logistic regression is developed to determine those images must alike to a new image from incomplete property information, after accounting for the physicians' appreciation of the image similarity. This method is particularly useful when problems with both a large number of features and few images are involved. Keywords Computer vision • CBIR • Logistic regression • Computer-aided diagnosis • Similarity 1 Introduction Research into image retrieval has steadily gained high recognition over the past few years as a result of the great
2014 22nd European Signal Processing Conference (EUSIPCO), 2014
Subject-based approaches are proposed to automatically discriminate healthy people from those wit... more Subject-based approaches are proposed to automatically discriminate healthy people from those with Parkinson's Disease (PD) by using speech recordings. These approaches have been applied to one of the most used PD datasets, which contains repeated measurements in an imbalanced design. Most of the published methodologies applied to perform classification from this dataset fail to account for the dependent nature of the data. This fact artificially increases the sample size and leads to a diffuse criterion to define which subject is suffering from PD. The first proposed approach is based on data aggregation. This reduces the sample size, but defines a clear criterion to discriminate subjects. The second one handles repeated measurements by introducing latent variables in a Bayesian logistic regression framework. The proposed approaches are conceptually simple and easy to implement.
Bayesian decision theory plays a significant role in a large number of applications that have as ... more Bayesian decision theory plays a significant role in a large number of applications that have as main aim decision making. At the same time, negotiation is a process of making joint decisions that has one of its main foundations in decision theory. In this context, an important issue involved in industrial and commercial applications is product reliability/quality demonstration. The goal is, among others, product commercialization with the best possible price. This paper provides a Bayesian sequential negotiation model in the context of sale of a product based on two characteristics: product price and reliability/quality testing. The model assumes several parties, a manufacturer and different consumers, who could be considered adversaries. In addition, a general setting for which the manufacturer offers a product batch to the consumers is taken. Both the manufacturer and the consumers have to use their prior beliefs as well as their preferences. Sometimes, the model will require to update the previous beliefs. This can be made through the corresponding posterior distribution. Anyway, the main aim is that at least one consumer accepts the product batch based on either product price or product price and reliability/quality. The general model is solved from the manufacturer viewpoint. Thus a general approach that allows us to calculate an optimal price and sample size for testing is provided. Finally, two applications show how the proposed technique can be applied in practice.
Este trabajo propone un metodo general para el analisis Bayesiano de las familias exponenciales... more Este trabajo propone un metodo general para el analisis Bayesiano de las familias exponenciales naturales con varianza cuadratica en presencia de distintas fuentes de informaci on a priori. La informacion obtenida de cada fuente se representa como una distribuci on a priori conjugada. Posteriormente, se propone un modelo de mixtura para expresar un consenso entre las fuentes. Se considera el caso en el que los pesos son desconocidos y estos se calculan utilizando un metodo basado en distancias de Kullback-Leibler (KL). Una ventaja es que el procedimiento conduce a una solucion analtica. Ademas, es posible una implementacion directa para todas las familias de la clase. Finalmente, se analizan las discrepancias entre la decision nal y las decisiones individuales usando distancias de KL. Estas distancias se estiman mediante un procedimiento de bajo coste computacional basado en el metodo Montecarlo.
Motivated by a study tracking the progression of Parkinson's disease (PD) based on features e... more Motivated by a study tracking the progression of Parkinson's disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.
Usual estimation methods for the parameters of extreme value distributions only employ a small pa... more Usual estimation methods for the parameters of extreme value distributions only employ a small part of the observation values. When block maxima values are considered, many data are discarded, and therefore a lot of information is wasted. We develop a model to seize the whole data available in an extreme value framework. The key is to take advantage of the existing relation between the baseline parameters and the parameters of the block maxima distribution. We propose two methods to perform Bayesian estimation. Baseline distribution method (BDM) consists in computing estimations for the baseline parameters with all the data, and then making a transformation to compute estimations for the block maxima parameters. Improved baseline method (IBDM) is a refinement of the initial idea, with the aim of assigning more importance to the block maxima data than to the baseline values, performed by applying BDM to develop an improved prior distribution. We compare empirically these new methods ...
Vocal fold nodules are recognized as an occupational disease for all collective of workers perfor... more Vocal fold nodules are recognized as an occupational disease for all collective of workers performing activities for which maintained and continued use of voice is required. Computer-aided systems based on features extracted from voice recordings have been considered as potential noninvasive and low cost tools to diagnose some voice-related diseases. A Bayesian decision analysis approach has been proposed to classify university lectures in three levels of risk: low, medium, and high, based on the information provided by acoustic features extracted from healthy controls and people suffering from vocal fold nodules. The proposed risk groups are associated with different treatments. The approach is based on the calculation of posterior probabilities of developing vocal fold nodules and considers utility functions that include the financial cost and the probability of recovery for the corresponding treatment. Maximization of the expected utilities is considered. By using this approach, the risk of having vocal fold nodules is identified for each university lecturer, so he/she can be properly assigned to the right treatment. The approach has been applied to university lecturers according to the Disease Prevention Program of the University of Extremadura. However, it can also be applied to other voice professionals (singers, speakers, coaches, actors…).
Computer methods and programs in biomedicine, 2017
In the scientific literature, there is a lack of variable selection and classification methods co... more In the scientific literature, there is a lack of variable selection and classification methods considering replicated data. The problem motivating this work consists in the discrimination of people suffering Parkinson's disease from healthy subjects based on acoustic features automatically extracted from replicated voice recordings. A two-stage variable selection and classification approach has been developed to properly match the replication-based experimental design. The way the statistical approach has been specified allows that the computational problems are solved by using an easy-to-implement Gibbs sampling algorithm. The proposed approach produces an acceptable predictive capacity for PD discrimination with the considered database, despite the fact that the sample size is relatively small. Specifically, the accuracy rate, sensitivity and specificity are 86.2%, 82.5%, and 90.0%, respectively. However, the most important fact is that there is an improvement in the interpret...
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Papers by Jacinto Martín