Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 25 for “"Bayesian statistical decision theory"”.
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Topics in Bayesian sample size determination and Bayesian model selection.
This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the …
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Selected topics in statistical discriminant analysis.
… consists of three selected topics in statistical discriminant analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly preserves …
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Bayesian adaptive designs for non-inferiority and dose selection trials.
… a way that can be used as the trial progresses. Bayesian methods offer a highly flexible means of using such information yielding inferences and decisions that are consistent with the laws of probability and consequently admit ease of interpretation. Bayesian adaptive sampling methods offer the …
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Bayesian Model Selection in terms of Kullback-Leibler discrepancy
… model assessment and selection methods for Bayesian models, when we anticipate that a promising approach should be objective enough to accept, easy enough to understand, general enough to apply, simple enough to compute and coherent enough to interpret. We mainly restrict attention to the …
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Using a Bayesian model for bankruptcy prediction : a comparative approach
… three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used …
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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data
We develop new methods of Bayesian inference, largely in the context of analysis of neuroscience data. The work is broken into several parts. In the first part, we introduce a novel class of joint probability distributions in which exact inference is tractable. Previously it has been difficult to …
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Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications
Solving optimization problems with sparse or low-rank optimal solutions has been an important topic since the recent emergence of compressed sensing and its matrix extensions such as the matrix rank minimization and robust principal component analysis problems. Compressed sensing enables one to …
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Constructing a web application for identification, detection and analysis of pattern based fraud
The rapid development and popularization of the e-commerce business in the past few decades have made online purchasing of products preferable and more comfortable shopping method for buyers. As credit / debit cards became most common payment method for both online and regular purchases, the number …
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High dimensional information processing
… methods based on Gaussian process nonparametric Bayesian techniques for estimating these two-dimensional rate maps. These techniques offer a number of advantages: the estimates may be computed efficiently, come equipped with natural errorbars, adapt their smoothness automatically to the local …
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Self-controlled methods for postmarketing drug safety surveillance in large-scale longitudinal data
… In the second part of this work we propose the Bayesian multiple SCCS approach, which deals with high dimensionality and can provide a sparse solution via a Laplacian prior. We present details of the model and optimization procedure, as well as results of empirical investigations. SCCS is based …
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Bayesian and maximum likelihood methods for some two-segment generalized linear models.
… regression. We focus on maximum likelihood and Bayesian methods.
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Bayesian approach to inference and variable selection for misclassified and under-reported response models.
… addressed but in less detail. We then develop a Bayesian approach to select significant covariates while accounting for the partial missingness. Examples of response partial missingness in which the variable selection method is applied include determining the factors that contribute to whether or …
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On some aspects of portfolio management
We study the on-line portfolio and the stochastic portfolio investment algorithms and test them with historical data sets. With regard to the stochastic portfolio we develop an optimal formula to manage the portfolio with daily trading in terms of the weights that are assigned to the different …
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Dynamic Targeted Pricing in B2B Settings
This research models the impact of firm pricing decisions on different facets of the customer purchasing process in business-to-business (B2B) contexts and develops an integrated framework for inter-temporal targeted pricing to optimize long-term profitability for the firm. Pricing decisions in B2B …
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Stromerzeugung in Deutschland unter den Rahmenbedingungen von Klimapolitik und liberalisiertem Strommarkt : Bewertung von Kraftwerksinvestitionen mit Bayes’schen Einflussdiagrammen
Mit der Liberalisierung des Strommarkts, den unsicheren Aussichten in der Klimapolitik und stark schwankenden Preisen bei Brennstoffen, Emissionsrechten und Kraftwerkskomponenten hat bei Kraftwerksinvestitionen das Risikomanagement an Bedeutung gewonnen. Dies äußert sich im vermehrten Einsatz …
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Dealing with Sparse Rater Scoring of Constructed Responses within a Framework of a Latent Class Signal Detection Model
… of a latent class version of signal detection theory (LC-SDT) that has been previously used for rater scoring. This approach provides a model for rater cognition in CR scoring (DeCarlo, 2005; 2008; 2010) and offers measures of rater reliability and various rater effects. The following potential …
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Bayesian Multidimensional Scaling Model for Ordinal Preference Data
The model within the present study incorporated Bayesian Multidimensional Scaling and Markov Chain Monte Carlo methods to represent individual preferences and threshold parameters as they relate to the influence of survey items popularity and their interrelationships. The model was used to …
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Conservatism in a Bayesian Probability Situation as a Function of the Sex of the Subject
… to examine whether the conservatism present in a Bayesian probability situation could be partially attributable to the sex of the subjects performing the task. The experimental design required that the subjects estimate the probabilities of occurrence of two independent events. They were then …
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A Bayesian Multidimensional Scaling Model for Partial Rank Preference Data
… scaling, however he did not implement a Bayesian technique. In 2010, Fong, DeSarbo, Park, and Scott proposed a new Bayesian vector Multidimensional Scaling (MDS) model which was applied to data from a five-point Likert scale survey. This paper focused on Bayesian approach choice behavior …
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