Search results for "Software"
showing 10 items of 7396 documents
A Bayesian Sequential Look at u-Control Charts
2005
We extend the usual implementation of u-control charts (uCCs) in two ways. First, we overcome the restrictive (and often inadequate) assumptions of the Poisson model; next, we eliminate the need for the questionable base period by using a sequential procedure. We use empirical Bayes(EB) and Bayes methods and compare them with the traditional frequentist implementation. EB methods are somewhat easy to implement, and they deal nicely with extra-Poisson variability (and, at the same time, informally check the adequacy of the Poisson assumption). However, they still need the base period. The sequential, full Bayes approach, on the other hand, also avoids this drawback of traditional u-charts. T…
Using mathematical morphology for unsupervised classification of functional data
2011
This paper is concerned with the unsupervised classification of functional data by using mathematical morphology. Different morphological operators are used to extract relevant structures of the functions (considered as sets through their subgraph representations). These operators can be considered as preprocessing tools whose outputs are also functional data. We explore some dissimilarity measures and clustering methods for the classification of the transformed data. Our approach is illustrated through a detailed analysis of two data sets. These techniques, which have mainly been used in image processing, provide a flexible and robust toolbox for improving the results in unsupervised funct…
An introduction to Bayesian reference analysis: inference on the ratio of multinomial parameters
1998
This paper offers an introduction to Bayesian reference analysis, often described as the more successful method to produce non-subjective, model-based, posterior distributions. The ideas are illustrated in detail with an interesting problem, the ratio of multinomial parameters, for which no model-based Bayesian analysis has been proposed. Signposts are provided to the huge related literature.
A model-based approach to Spotify data analysis: a Beta GLMM
2020
Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web-based data. This paper deals with the management of songs audio features from a statistical point of view. In particular, it explores the data catching mechanisms enabled by Spotify Web API and suggests statistical tools for the analysis of these data. Special attention is devoted to songs popularity and a Beta model, including random effects, is proposed in order to give the first answer to questions like: which are the determinants of popularity? The identification of a model able to describe this relationship, the determination within the set of char…
Cluster-Localized Sparse Logistic Regression for SNP Data
2012
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, th…
Multiple testing in candidate gene situations: a comparison of classical, discrete, and resampling-based procedures.
2011
In candidate gene association studies, usually several elementary hypotheses are tested simultaneously using one particular set of data. The data normally consist of partly correlated SNP information. Every SNP can be tested for association with the disease, e.g., using the Cochran-Armitage test for trend. To account for the multiplicity of the test situation, different types of multiple testing procedures have been proposed. The question arises whether procedures taking into account the discreteness of the situation show a benefit especially in case of correlated data. We empirically evaluate several different multiple testing procedures via simulation studies using simulated correlated SN…
TiFoSi: an efficient tool for mechanobiology simulations of epithelia
2020
[Motivation]: Emerging phenomena in developmental biology and tissue engineering are the result of feedbacks between gene expression and cell biomechanics. In that context, in silico experiments are a powerful tool to understand fundamental mechanisms and to formulate and test hypotheses.
Opportunities and challenges of combined effect measures based on prioritized outcomes
2013
Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. …
Sparse relative risk regression models
2020
Summary Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios…
An interest rates cluster analysis
2004
An empirical analysis of interest rates in money and capital markets is performed. We investigate a set of 34 different weekly interest rate time series during a time period of 16 years between 1982 and 1997. Our study is focused on the collective behavior of the stochastic fluctuations of these time-series which is investigated by using a clustering linkage procedure. Without any a priori assumption, we individuate a meaningful separation in 6 main clusters organized in a hierarchical structure.