Computational Statistics

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Dynamic Link Library for Statistical Analysis and Its Excel Interface
Computational Statistics - - 2002
Akinobu Takeuchi, Hiroshi Yadohisa, Kazunori Yamaguchi, Michiko Watanabe, Chooichiro Asano
Unsupervised learning on U.S. weather forecast performance
Computational Statistics - Tập 38 - Trang 1193-1213 - 2023
Chuyuan Lin, Ying Yu, Lucas Y. Wu, Jiguo Cao
Nowadays, climate events and weather predictions have a huge impact on human activities. To understand the accuracy of weather prediction, we applied the functional principal component analysis (FPCA) method to investigate the main pattern of variance within the U.S. weather prediction error over a period of 3 years. We further grouped the states in the U.S. based on their similarity in weather fo...... hiện toàn bộ
Bayesian inference on longitudinal-survival data with multiple features
Computational Statistics - Tập 32 - Trang 845-866 - 2016
Tao Lu
The modeling of longitudinal and survival data is an active research area. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a joint model that concurrently accounting for longitudinal-survival data with multiple features. Specifically, our joint model handles skewness, limit of detection, m...... hiện toàn bộ
Row–column interaction models, with an R implementation
Computational Statistics - Tập 29 - Trang 1427-1445 - 2014
Thomas W. Yee, Alfian F. Hadi
We propose a family of models called row–column interaction models (RCIMs) for two-way table responses. RCIMs apply some link function to a parameter (such as the cell mean) to equal a row effect plus a column effect plus an optional interaction modelled as a reduced-rank regression. What sets this work apart from others is that our framework incorporates a very wide range of statistical models, e...... hiện toàn bộ
Computations for the familial analysis of binary traits
Computational Statistics - Tập 20 - Trang 439-448 - 2005
Harry Joe, A. H. M. Mahbub-ul Latif
For familial aggregation of a binary trait, one method that has been used is the GEE2 (generalized estimating equation) method corresponding to a multivariate logit model. We solve the complex estimating equations for the GEE2 method using an automatic differentiation software which computes the derivatives of a function numerically using the chain rule of the calculus repeatedly on the elementary...... hiện toàn bộ
Bayesian sparse convex clustering via global-local shrinkage priors
Computational Statistics - Tập 36 - Trang 2671-2699 - 2021
Kaito Shimamura, Shuichi Kawano
Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted $$L_1$$ norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and redu...... hiện toàn bộ
An algorithm for construction of multiple hypothesis testing
Computational Statistics - - 2019
Koon-Shing Kwong
Recently, the Simes method for constructing multiple hypothesis tests involving multivariate distributions of the test statistics with a particular form of positive dependence has been proved to strongly control the Type I familywise error rate. In this paper, an algorithm is provided so that distributions of ordered test statistics with certain correlation structures can be exactly and efficientl...... hiện toàn bộ
New classes of tests for the Weibull distribution using Stein’s method in the presence of random right censoring
Computational Statistics - - 2022
Elzanie Bothma, James S. Allison, I. J. H. Visagie
Empirical wavelet analysis of tail and memory properties of LARCH and FIGARCH models
Computational Statistics - Tập 25 Số 1 - Trang 163-182 - 2010
A Jach, Piotr Kokoszka
Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering
Computational Statistics - Tập 38 - Trang 2015-2051 - 2023
Massimo Bilancia, Michele Di Nanni, Fabio Manca, Gianvito Pio
In this paper, we formulate a hierarchical Bayesian version of the Mixture of Unigrams model for text clustering and approach its posterior inference through variational inference. We compute the explicit expression of the variational objective function for our hierarchical model under a mean-field approximation. We then derive the update equations of a suitable algorithm based on coordinate ascen...... hiện toàn bộ
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