Statistica Sinica

Computer experiments have been widely used in various fields.Among different design types, space-filling designs stand out as the most common choice for computer experiments due to their effectiveness in thoroughly exploring the experimental region.Extensive research has been conducted on the space-filling criteria.However, there are relatively few studies developing a systematic framework for re…

We present a novel Bayesian approach for high-dimensional grouped regression under sparsity.We leverage a sparse projection method that uses a sparsity-inducing map to induce a posterior on a lower-dimensional parameter space.Our method introduces three distinct projection maps based on popular penalty functions: the Group LASSO projection-posterior, the Group SCAD projection-posterior, and the A…

In this article, we introduce the mean independent component analysis for multivariate time series to reduce the parameter space.In particular, we seek for a contemporaneous linear transformation that detects univariate mean independent components so that each component can be modeled separately.The mean independent component analysis is flexible in the sense that no parametric model or distribut…

Estimating conditional density functions is a fundamental problem in statistics.This task is crucial for understanding the underlying relationships between variables and for making informed predictions in various applications.In this paper, we introduce a novel deep nonparametric approach for estimating conditional density functions from data.Our method leverages the flexibility and expressivenes…

Gaussian distributed sparsely sampled longitudinal data can be represented as Gaussian distributions of their functional principal component scores, conditional on the available data.Since these conditional distribu-tions reflect the entire information available about these scores and therefore about the unknown trajectories that constitute the realizations of the stochastic process that generate…

The accelerated failure time model has garnered attention due to its intuitive linear regression interpretation and has been successfully applied in fields such as biostatistics, clinical medicine, economics, and social sciences.This paper considers a weighted least squares estimation method with an 0-penalty based on right-censored data in a high-dimensional setting.For practical implementation,…

We propose a unified framework to draw inferences for regression coefficients in a generalized linear model (GLM) following Lasso-based variable selection.We adapt to non-Gaussian GLMs a recently developed parametric programming strategy for postselection inference in the linear model with a Gaussian response by drawing parallels between maximum likelihood estimation in GLMs and least squares est…

In this study, we introduce an inferential procedure for assessing the covariance difference between two-samples of large-scale functional data, utilizing a computationally efficient multiplier bootstrap approach.In contrast to the existing method that focuses exclusively on a testing procedure, our approach starts by establishing a confidence region for the covariance difference under fairly fle…

The estimation of conditional quantiles at extreme tails is of great interest in numerous applications.Various methods that integrate regression analysis with an extrapolation strategy derived from extreme value theory have been proposed to estimate extreme conditional quantiles in scenarios with a fixed number of covariates.However, these methods become less effective in high-dimensional setting…

In many physical and engineering experiments, the order in which a process is executed or components are added can have a marked impact on the response.Due to constraints on resources or feasibility, there are situations where only a subset of the components can be administered in practice and experimenters encounter a complicated task with the selection of components and the corresponding best o…

Regularization in fitting regression models has been a very active topic of research in the past few decades, but most of the existing methods are designed for particular situations, e.g. for the case of a sparse coefficient vector.We consider the problem of designing universally optimal regularized estimators in a given generalized linear model with fixed effects.First, we propose as a contender…