Statistics and Computing最新文献

The rapid advancements of scalable methodologies have opened new avenues for analyzing complex spatio-temporal data, which is crucial in understanding dynamic environmental phenomena. This paper introduces a likelihood-based methodology for detecting abrupt changes in time in spatio-temporal processes, a field where traditional time series methods fall short. Unlike recent approaches, we do not make the unrealistic assumption that data is independent across changepoints. Instead, we use a recently proposed family of covariance models that allows nonstationarity in time, and we propose a Markov approximation to reduce the computational burden of calculating likelihoods under this model. We apply our method to two years of daily wind speed data from various synoptic weather stations in Ireland, identifying a significant changepoint on July 24, 2021, which aligns with a major shift in weather patterns. This application not only demonstrates the method's utility in handling spatio-temporal datasets but also showcases its potential in broader environmental and climatic studies, offering a scalable solution for analyzing changing patterns in spatial data over time.

In high-dimensional regression models, variable selection becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective methods for variable selection in $p>n$ scenarios provided the shrinkage priors are configured adequately. We propose an empirical Bayes configuration using checks for prior-data conflict: tests that assess whether there is disagreement in parameter information provided by the prior and data. We apply our proposed method to the Bayesian LASSO and spike-and-slab shrinkage priors in the linear regression model and assess the variable selection performance of our prior configurations through a high-dimensional simulation study. Additionally, we apply our method to proteomic data collected from patients admitted to the Albany Medical Center in Albany NY in April of 2020 with COVID-like respiratory issues. Simulation results suggest our proposed configurations may outperform competing models when the true regression effects are small.

Supplementary information: The online version contains supplementary material available at 10.1007/s11222-025-10582-1.

This study introduces a novel p-value-based multiple testing approach tailored for generalized linear models. Despite the crucial role of generalized linear models in statistics, existing methodologies face obstacles arising from the heterogeneous variance of response variables and complex dependencies among estimated parameters. Our aim is to address the challenge of controlling the false discovery rate (FDR) amidst arbitrarily dependent test statistics. Through the development of efficient computational algorithms, we present a versatile statistical framework for multiple testing. The proposed framework accommodates a range of tools developed for constructing a new model matrix in regression-type analysis, including random row permutations and Model-X knockoffs. We devise efficient computing techniques to solve the encountered non-trivial quadratic matrix equations, enabling the construction of paired p-values suitable for the two-step multiple testing procedure proposed by Sarkar and Tang (Biometrika 109(4): 1149-1155, 2022). Theoretical analysis affirms the properties of our approach, demonstrating its capability to control the FDR at a given level. Empirical evaluations further substantiate its promising performance across diverse simulation settings.

Supplementary information: The online version contains supplementary material available at 10.1007/s11222-025-10600-2.

A finite mixture of distributions is a popular statistical model, which is especially meaningful when the population of interest may include distinct subpopulations. This work is motivated by analysis of protein expression levels quantified using immunofluorescence immunohistochemistry assays of human tissues. The distributions of cellular protein expression levels in a tissue often exhibit multimodality, skewness and heavy tails, but there is a substantial variability between distributions in different tissues from different subjects, while some of these mixture distributions include components consistent with the assumption of a normal distribution. To accommodate such diversity, we propose a mixture of 4-parameter Tukey's g- &-h distributions for fitting finite mixtures with both Gaussian and non-Gaussian components. Tukey's g- &-h distribution is a flexible model that allows variable degree of skewness and kurtosis in mixture components, including normal distribution as a particular case. Since the likelihood of the Tukey's g- &-h mixtures does not have a closed analytical form, we propose a quantile least Mahalanobis distance (QLMD) estimator for parameters of such mixtures. QLMD is an indirect estimator minimizing the Mahalanobis distance between the sample and model-based quantiles, and its asymptotic properties follow from the general theory of indirect estimation. We have developed a stepwise algorithm to select a parsimonious Tukey's g- &-h mixture model and implemented all proposed methods in the R package QuantileGH available on CRAN. A simulation study was conducted to evaluate performance of the Tukey's g- &-h mixtures and compare to performance of mixtures of skew-normal or skew-t distributions. The Tukey's g- &-h mixtures were applied to model cellular expressions of Cyclin D1 protein in breast cancer tissues, and resulting parameter estimates evaluated as predictors of progression-free survival.

In this paper, we introduce a novel approach that integrates Bayesian additive regression trees (BART) with the composite quantile regression (CQR) framework, creating a robust method for modeling complex relationships between predictors and outcomes under various error distributions. Unlike traditional quantile regression, which focuses on specific quantile levels, our proposed method, composite quantile BART, offers greater flexibility in capturing the entire conditional distribution of the response variable. By leveraging the strengths of BART and CQR, the proposed method provides enhanced predictive performance, especially in the presence of heavy-tailed errors and non-linear covariate effects. Numerical studies confirm that the proposed composite quantile BART method generally outperforms classical BART, quantile BART, and composite quantile linear regression models in terms of RMSE, especially under heavy-tailed or contaminated error distributions. Notably, under contaminated normal errors, it reduces RMSE by approximately 17% compared to composite quantile regression, and by 27% compared to classical BART.

Motif discovery is gaining increasing attention in the domain of functional data analysis. Functional motifs are typical "shapes" or "patterns" that recur multiple times in different portions of a single curve and/or in misaligned portions of multiple curves. In this paper, we define functional motifs using an additive model and we propose funBIalign for their discovery and evaluation. Inspired by clustering and biclustering techniques, funBIalign is a multi-step procedure which uses agglomerative hierarchical clustering with complete linkage and a functional distance based on mean squared residue scores to discover functional motifs, both in a single curve (e.g., time series) and in a set of curves. We assess its performance and compare it to other recent methods through extensive simulations. Moreover, we use funBIalign for discovering motifs in two real-data case studies; one on food price inflation and one on temperature changes.

Supplementary information: The online version contains supplementary material available at 10.1007/s11222-024-10537-y.

While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they are often plagued by missing data and dropouts, presenting issues for estimation algorithms. This research presents a family of hidden Markov models that compensate for the issues that arise in panel data. A modified expectation–maximization algorithm capable of handling missing not at random data and dropout is presented and used to perform model estimation.

As a specific application of survival analysis, one of main interests in medical studies aims to analyze the patients’ survival time of a specific cancer. Typically, gene expressions are treated as covariates to characterize the survival time. In the framework of survival analysis, the accelerated failure time model in the parametric form is perhaps a common approach. However, gene expressions are possibly nonlinear and the survival time as well as censoring status are subject to measurement error. In this paper, we aim to tackle those complex features simultaneously. We first correct for measurement error in survival time and censoring status, and use them to develop a corrected Buckley–James estimator. After that, we use the boosting algorithm with the cubic spline estimation method to iteratively recover nonlinear relationship between covariates and survival time. Theoretically, we justify the validity of measurement error correction and estimation procedure. Numerical studies show that the proposed method improves the performance of estimation and is able to capture informative covariates. The methodology is primarily used to analyze the breast cancer data provided by the Netherlands Cancer Institute for research.

In data assimilation (DA) schemes, the form representing the processes in the evolution models are pre-determined except some parameters to be estimated. In some applications, such as the contaminant solute transport model and the gas reservoir model, the modes in the equations within the evolution model cannot be predetermined from the outset and may change with the time. We propose a framework of sequential DA method named Reversible Jump Ensemble Filter (RJEnF) to identify the governing modes of the evolution model over time. The main idea is to introduce the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method to the DA schemes to fit the situation where the modes of the evolution model are unknown and the dimension of the parameters is changing. Our framework allows us to identify the modes in the evolution model and their changes, as well as estimate the parameters and states of the dynamic system. Numerical experiments are conducted and the results show that our framework can effectively identify the underlying evolution models and increase the predictive accuracy of DA methods.

This work focuses on dimension-reduction techniques for modelling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections of an input random vector. In this context, the estimation of projection directions is examined, as approached by the extreme partial least squares (EPLS) method—an adaptation of the original partial least squares (PLS) method tailored to the extreme-value framework. Further, a novel interpretation of EPLS directions as maximum likelihood estimators is introduced, utilizing the von Mises–Fisher distribution applied to hyperballs. The dimension reduction process is enhanced through the Bayesian paradigm, enabling the incorporation of prior information into the projection direction estimation. The maximum a posteriori estimator is derived in two specific cases, elucidating it as a regularization or shrinkage of the EPLS estimator. We also establish its asymptotic behavior as the sample size approaches infinity. A simulation data study is conducted in order to assess the practical utility of our proposed method. This clearly demonstrates its effectiveness even in moderate data problems within high-dimensional settings. Furthermore, we provide an illustrative example of the method’s applicability using French farm income data, highlighting its efficacy in real-world scenarios.