Computational Statistics
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A genetic algorithm for designing microarray experiments
Computational Statistics - Tập 31 - Trang 409-424 - 2015
Heuristic techniques of optimization can be useful in designing complex experiments, such as microarray experiments. They have advantages over the traditional methods of optimization, particularly in situations where the search space is discrete. In this paper, a search procedure based on a genetic algorithm is proposed to find optimal (efficient) designs for both one- and multi-factor experiments......
Dynamic Link Library for Statistical Analysis and Its Excel Interface
Computational Statistics - - 2002
Unsupervised learning on U.S. weather forecast performance
Computational Statistics - Tập 38 - Trang 1193-1213 - 2023
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......
Interacting with local and remote data repositories using the stashR package
Computational Statistics - Tập 24 Số 2 - Trang 247-254 - 2009
Bayesian inference on longitudinal-survival data with multiple features
Computational Statistics - Tập 32 - Trang 845-866 - 2016
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......
Investigating GQL-based inferential approaches for non-stationary BINAR(1) model under different quantum of over-dispersion with application
Computational Statistics - Tập 34 - Trang 1275-1313 - 2018
In particular, this paper addresses solutions to the computational challenges encountered in estimating parameters in non-stationary over-dispersed bivariate integer-valued autoregressive of order 1 (BINAR(1)) model with Negative Binomial (NB) innovations. In this BINAR(1) model, the cross-correlation is induced through the paired NB innovations which follows a recently introduced bivariate NB mod......
Row–column interaction models, with an R implementation
Computational Statistics - Tập 29 - Trang 1427-1445 - 2014
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......
Computations for the familial analysis of binary traits
Computational Statistics - Tập 20 - Trang 439-448 - 2005
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......
Bayesian sparse convex clustering via global-local shrinkage priors
Computational Statistics - Tập 36 - Trang 2671-2699 - 2021
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......
An algorithm for construction of multiple hypothesis testing
Computational Statistics - - 2019
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......
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