Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Sugasawa, Shonosuke"'
Although quantile regression has emerged as a powerful tool for understanding various quantiles of a response variable conditioned on a set of covariates, the development of quantile regression for count responses has received far less attention. Thi
Externí odkaz:
http://arxiv.org/abs/2410.23081
Autor:
Sugasawa, Shonosuke
Prior sensitivity analysis is a fundamental method to check the effects of prior distributions on the posterior distribution in Bayesian inference. Exploring the posteriors under several alternative priors can be computationally intensive, particular
Externí odkaz:
http://arxiv.org/abs/2409.19729
Factor analysis has been extensively used to reveal the dependence structures among multivariate variables, offering valuable insight in various fields. However, it cannot incorporate the spatial heterogeneity that is typically present in spatial dat
Externí odkaz:
http://arxiv.org/abs/2409.07018
The doubly robust estimator, which models both the propensity score and outcomes, is a popular approach to estimate the average treatment effect in the potential outcome setting. The primary appeal of this estimator is its theoretical property, where
Externí odkaz:
http://arxiv.org/abs/2409.06288
We develop a new stochastic process called spatially-dependent Indian buffet processes (SIBP) for spatially correlated binary matrices and propose general spatial factor models for various multivariate response variables. We introduce spatial depende
Externí odkaz:
http://arxiv.org/abs/2409.01943
Autor:
Wakayama, Tomoya, Sugasawa, Shonosuke
This study proposes a novel approach to ensemble prediction, called ``covariate-dependent stacking'' (CDST). Unlike traditional stacking methods, CDST allows model weights to vary flexibly as a function of covariates, thereby enhancing predictive per
Externí odkaz:
http://arxiv.org/abs/2408.09755
Benchmarking estimation and its risk evaluation is a practically important issue in small area estimation. While hierarchical Bayesian methods have been widely adopted in small area estimation, a unified Bayesian approach to benchmarking estimation h
Externí odkaz:
http://arxiv.org/abs/2407.17848
While K-means is known to be a standard clustering algorithm, its performance may be compromised due to the presence of outliers and high-dimensional noisy variables. This paper proposes adaptively robust and sparse K-means clustering (ARSK) to addre
Externí odkaz:
http://arxiv.org/abs/2407.06945
Linear mixed models (LMMs), which typically assume normality for both the random effects and error terms, are a popular class of methods for analyzing longitudinal and clustered data. However, such models can be sensitive to outliers, and this can le
Externí odkaz:
http://arxiv.org/abs/2407.01883
Functional time series data frequently appears in economic applications, where the functions of interest are subject to some shape constraints, including monotonicity and convexity, as typical of the estimation of the Lorenz curve. This paper propose
Externí odkaz:
http://arxiv.org/abs/2404.07586