
Shonosuke SugasawaKeio University · Faculty of Economics
Shonosuke Sugasawa
Ph.D. (Economics)
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137
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Introduction
Publications
Publications (137)
Shrinkage priors are a popular Bayesian paradigm to handle sparsity in high-dimensional regression. Still limited, however, is a flexible class of shrinkage priors to handle grouped sparsity, where covariates exhibit some natural grouping structure. This paper proposes a novel extension of the $R^2$-induced Dirichlet Decomposition (R2D2) prior to a...
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. This paper proposes a new Bayesian approach to quantile regression for count data, which provides a mor...
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, particularly for complex latent variable models. To address this issue, we propose a novel method for quantify...
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 data. To address this issue, we introduce an effective method specifically designed to discover the pot...
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, wherein the estimator achieves consistency as long as either the propensity score or outcomes is correctl...
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 dependency through the stick-breaking representation of the original Indian buffet process (IBP) and latent...
This paper presents 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 performance in complex scenarios. We formulate the covariate-dependent weights through combinations of bas...
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 has not been fully discussed. This work employs an entropic tilting method to modify the posterior di...
While K-means is known to be a standard clustering algorithm, it 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 address these practical limitations of the standard K-means algorithm. We introduce a redundant error component for ea...
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 lead to poor statistical results (e.g., biased inference on model parameters and inaccurate prediction...
Meta‐analysis is an essential tool to comprehensively synthesize and quantitatively evaluate results of multiple clinical studies in evidence‐based medicine. In many meta‐analyses, the characteristics of some studies might markedly differ from those of the others, and these outlying studies can generate biases and potentially yield misleading resul...
This study proposes a method for aggregating/synthesizing global and local sub‐models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub‐model, while the generalized product‐of‐experts method was used to aggregate thes...
We conducted a survey experiment with a sample of university-based start-up researchers to investigate the effects of patents on innovations. The real-world news about the patent waiver of COVID-19 vaccines was exploited in our random information manipulation study. A new ensemble method for causal inference, Bayesian causal synthesis, was employed...
This study argues that the spatiotemporal geostatistical model for real estate prices, which accounts for and incorporates spatial autocorrelation, can be estimated successfully using the Bayesian Markov Chain Monte Carlo (MCMC) estimation. While this procedure often encounters difficulty in calculating probabilistic densities in the Metropolis–Has...
Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In this study, we propose a Bayesian quantile trend filtering method to estimate the non-stationary trend of quantiles. We introduce general shrinkage priors to induce local...
Regression discontinuity design (RDD) is widely adopted for causal inference under intervention determined by a continuous variable. While one is interested in treatment effect heterogeneity by subgroups in many applications, RDD typically suffers from small subgroup-wise sample sizes, which makes the estimation results highly instable. To solve th...
We introduce a new small area predictor when the Fay-Herriot normal error model is fitted to a logarithmically transformed response variable, and the covariate is measured with error. This framework has been previously studied by Mosaferi et al. (2023). The empirical predictor given in their manuscript cannot perform uniformly better than the direc...
Since the outbreak of COVID-19, governments and academia have made tremendous efforts to predict the course of the pandemic and implement prevention measures by monitoring various indicators. These indicators are obtained daily or weekly, and typically constitute time series count data over multiple sub-regions of a country, where groups of sub-reg...
Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. This paper proposes a new Bayesian method with global‐local shrinkage priors for estimating monotone function values. Specifically, we introduce half shrinkage priors for p...
Random partitioned distribution is a powerful tool for model-based clustering. However, the implementation in practice can be challenging for functional spatial data such as hourly observed population data observed in each region. The reason is that high dimensionality tends to yield excess clusters, and spatial dependencies are challenging to repr...
Network meta-analysis has played an important role in evidence-based medicine for assessing the comparative effectiveness of multiple available treatments. The prediction interval has been one of the standard outputs in recent network meta-analysis as an effective measure that enables simultaneous assessment of uncertainties in treatment effects an...
Despite increasing accessibility to function data, effective methods for flexibly estimating underlying functional trend are still scarce. We thereby develop a functional version of trend filtering for estimating trend of functional data indexed by time or on general graph by extending the conventional trend filtering, a powerful nonparametric tren...
We propose a novel Bayesian methodology to mitigate misspecification and improve estimating treatment effects. A plethora of methods to estimate -- particularly the heterogeneous -- treatment effect have been proposed with varying success. It is recognized, however, that the underlying data generating mechanism, or even the model specification, can...
Robust Bayesian linear regression is a classical but essential statistical tool. Although novel robustness properties of posterior distributions have been proved recently under a certain class of error distributions, their sufficient conditions are restrictive and exclude several important situations. In this work, we revisit a classical two-compon...
Sampling from matrix generalized inverse Gaussian (MGIG) distributions is required in Markov Chain Monte Carlo (MCMC) algorithms for a variety of statistical models. However, an efficient sampling scheme for the MGIG distributions has not been fully developed. We here propose a novel blocked Gibbs sampler for the MGIG distributions, based on the Ch...
The proliferation of mobile devices has led to the collection of large amounts of population data. This situation has prompted the need to utilize this rich, multidimensional data in practical applications. In response to this trend, we have integrated functional data analysis (FDA) and factor analysis to address the challenge of predicting hourly...
We introduce a novel methodology for robust Bayesian estimation with robust divergence (e.g., density power divergence or γ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document...
It is important to find and select significant variables in regression in the linear mixed models.
As introduced in Chap. 4, the basic small area models are based on normality assumption for the response variables.
Statistical inference in the general linear mixed models is explained in the previous chapters. As basic models used in small area estimation, in this chapter, we treat two most standard models, known as the Fay–Herriot model and the nested error regression model, which have been extensively used for analyzing area-level and unit-level regional or...
In Chap. 4, we introduced two famous small area models, Fay–Herriot and nested error regression models, and provided basic theory of parameter estimation and EBLUP.
Linear mixed models are widely used in a variety of scientific areas such as small area estimation (Rao and Molina 2015), longitudinal data analysis (Verbeke and Molenberghs 2006), and meta-analysis (Boreinstein et al. 2009), and estimation of variance components plays an essential role in fitting the models. In this chapter, we provide the general...
An important aspect of small area estimation is the assessment of accuracy of the predictors. Under the frequentist approach, this will be complicated due to the additional fluctuation induced by estimating unknown parameters in models. We here focus on two methods that are widely adopted in this context: estimators of mean squared error and confid...
The flexibility of the two basic small area models described in Chap. 4 can be limited for practical applications.
This study attempts to predict and forecast the future heterogeneous increase in the vacant house ratio among prefectures in Japan using spatial panel models with unobserved dynamic spatiotemporal effects. The study formulated models with autoregressive and random-walk spatiotemporal effects, referring to the dynamic spatiotemporal effects (DSE) mo...
ANOVA-based estimators of variance components for nested-error regression models are always constructed based on moment equations through residual variance. We consider moment equations associated with residual covariance and construct improved ANOVA-based estimators. The proposed estimators have closed-form analytic expressions, which enables easy...
We introduce a new deal of kernel density estimation using an exponentiated form of kernel density estimators. The density estimator has two hyperparameters flexibly controlling the smoothness of the resulting density. We tune them in a data-driven manner by minimizing an objective function based on the Hyv\"arinen score to avoid the optimization i...
We consider modeling and prediction of Capelin distribution in the Barents Sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixin...
Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In this paper, we propose a Bayesian quantile trend filtering method to estimate non-stationary trend of quantiles. We introduce general shrinkage priors to induce locally a...
In this paper, we introduce a new and efficient data augmentation approach to the posterior inference of the models with shape parameters when the reciprocal gamma function appears in full conditional densities. Our approach is to approximate full conditional densities of shape parameters by using Gauss’s multiplication formula and Stirling’s formu...
This paper proposes a flexible Bayesian approach to multiple imputation using conditional Gaussian mixtures. We introduce novel shrinkage priors for covariate-dependent mixing proportions in the mixture models to automatically select the suitable number of components used in the imputation step. We develop an efficient sampling algorithm for poster...
Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. This paper proposes a new Bayesian method with global-local shrinkage priors for estimating monotone function values. Specifically, we introduce half shrinkage priors for p...
In Japan, the Housing and Land Survey (HLS) provides grouped data on household incomes at the municipality level. Although this data could serve for effective local policy-making, there are some challenges in analysing the HLS data, such as the scarcity of information due to the grouping, the presence of the non-sampled areas and the very low frequ...
Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such an assumption may not be realistic when there is potential heterogeneity in regression...
In genetic association studies, rare variants with extremely low allele frequencies play a crucial role in complex traits. Therefore, set‐based testing methods that jointly assess the effects of groups of single nucleotide polymorphisms (SNPs) were developed to increase the powers of the association tests. However, these powers are still insufficie...
Mixture modeling, which considers the potential heterogeneity in data, is widely adopted for classification and clustering problems. Mixture models can be estimated using the Expectation-Maximization algorithm, which works with the complete estimating equations conditioned by the latent membership variables of the cluster assignment based on the hi...
Due to developments in instruments and computers, functional observations are increasingly popular. However, effective methodologies for flexibly estimating the underlying trends with valid uncertainty quantification for a sequence of functional data (e.g. functional time series) are still scarce. In this work, we develop a locally adaptive smoothi...
Linear regression that employs the assumption of normality for the error distribution may lead to an undesirable posterior inference of regression coefficients due to potential outliers. A finite mixture of two components, one with thin and one with heavy tails, is considered as the error distribution in this study. For the heavily-tailed component...
In various applications, we deal with high-dimensional positive-valued data that often exhibits sparsity. This paper develops a new class of continuous global-local shrinkage priors tailored to analyzing positive-valued data where most of the underlying means are concentrated around a certain value. Unlike existing shrinkage priors, our new prior i...
Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to capture with a single model. Significant levels of model uncertainty -- arising from these characteristics -- cannot be resolved by model selection or simple ensemble methods, as performances are not homogeneous. We address this issue by...
In this paper, we introduce a new and efficient data augmentation approach to the posterior inference of the models with shape parameters when the reciprocal gamma function appears in full conditional densities. Our approach is to approximate full conditional densities of shape parameters by using Gauss's multiplication formula and Stirling's formu...
Quantiles are useful characteristics of random variables that can provide substantial information of distributions compared with commonly used summary statistics such as means. In this paper, we propose a Bayesian quantile trend filtering method to estimate non-stationary trend of quantiles on graphs. We introduce general shrinkage priors for graph...
We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on γ-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a...
This study investigates the relationship between access to domestic and international communication and economic development. It does so by constructing two indices of linguistic distance, domestic and international, capturing language acquisition costs, which are higher when acquiring linguistically more distant languages. The domestic linguistic...
We consider a model for predicting the spatio-temporal distribution of a marine species based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity...
This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used estimators such as the restricted maximum likelihood estimator. We derive the asymptotic covariance matrices and second-order biases under general estimating equations withou...
Although robust divergence, such as density power divergence and γ-divergence, is helpful for robust statistical inference in the presence of outliers, the tuning parameter that controls the degree of robustness is chosen in a rule-of-thumb, which may lead to an inefficient inference. We here propose a selection criterion based on an asymptotic app...
Although parametric empirical Bayes confidence intervals of multiple normal means are fundamental tools for compound decision problems, their performance can be sensitive to the misspecification of the parametric prior distribution (typically normal distribution), especially when some strong signals are included. We suggest a simple modification of...
Sugasawa, ShonosukeHashimoto, ShintaroWe introduce a new robust Bayesian change-point analysis in the presence of outliers. We employ an idea of general posterior based on density power divergence combined with horseshoe prior for differences of underlying signals. A posterior computation algorithm is proposed using Markov chain Monte Carlo. The pr...
We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on $\gamma$-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tun...
While robust divergence such as density power divergence and $\gamma$-divergence is helpful for robust statistical inference in the presence of outliers, the tuning parameter that controls the degree of robustness is chosen in a rule-of-thumb, which may lead to an inefficient inference. We here propose a selection criterion based on an asymptotic a...
Count data with zero inflation and large outliers are ubiquitous in many scientific applications. However, the posterior analysis under a standard statistical model such as Poisson or negative binomial distribution is sensitive to such contamination. This paper introduces a novel framework for Bayesian modeling of counts robust to both zeros inflat...
We introduce a methodology for robust Bayesian estimation with robust divergence (e.g., density power divergence or {\gamma}-divergence), indexed by a single tuning parameter. It is well known that the posterior density induced by robust divergence gives highly robust estimators against outliers if the tuning parameter is appropriately and carefull...
Spatial regression and geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a region. In contrast, relationships between response and auxiliary variables are expected to exhibit complex spatial patterns in many applications. This paper proposes a n...
This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used estimators such as the restricted maximum likelihood estimator. We derive the asymptotic covariance matrices and second-order biases under general estimating equations withou...
Despite increasing accessibility to function data, effective methods for flexibly estimating underlying trend structures are still scarce. We thereby develop locally adaptive smoothing methods for both functional time series and spatial data by extending trend filtering that is a powerful nonparametric trend estimation technique for scalar data. We...
Statistical inference with nonresponse is quite challenging, especially when the response mechanism is nonignorable. In this case, the validity of statistical inference depends on untestable correct specification of the response model. To avoid the misspecification, we propose semiparametric Bayesian estimation in which an outcome model is parametr...
The original article can be found online.
This study is concerned with estimating the inequality measures associated with the underlying hypothetical income distribution from the times series grouped data on the income proportions. We adopt the Dirichlet likelihood approach where the parameters of the Dirichlet likelihood are set to the differences between the Lorenz curve of the hypotheti...
Various multivariate extensions to the well-known Fay–Herriot model have been proposed in the small area estimation literature. Such multivariate models are quite effective in combining information through correlations among small area survey estimates of related variables or historical survey estimates of the same variable or both. Though the lite...
Spatial regression or geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a region, while relationships between response and auxiliary variables are expected to exhibit complex spatial patterns in many applications. In this paper, we propose a new...
Meta-analyses of diagnostic test accuracy (DTA) studies have been gathering attention in research in clinical epidemiology and health technology development, and bivariate random-effects model is becoming a standard tool. However, standard inference methods usually underestimate statistical errors and possibly provide highly overconfident results u...
The multivariate Fay-Herriot model is quite effective in combining information through correlations among small area survey estimates of related variables or historical survey estimates of the same variable or both. Though the literature on small area estimation is already very rich, construction of second-order efficient confidence intervals from...
Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily straightforward. We here propose a Bayesian approach to robust inference on linear regression models...
Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such assumption is not realistic when there are potential heterogeneity in regression coeff...
Japan has observed a surge in the number of confirmed cases of the coronavirus disease (COVID-19) that has caused a serious impact on the society especially after the declaration of the state of emergency on April 7, 2020. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling based on...
Linear regression with the classical normality assumption for the error distribution may lead to an undesirable posterior inference of regression coefficients due to the potential outliers. This paper considers the finite mixture of two components with thin and heavy tails as the error distribution, which has been routinely employed in applied stat...
The number of confirmed cases of the coronavirus disease (COVID-19) in Japan has been increasing day by day and has had a serious impact on the society especially after the declaration of the state of emergency on April 7, 2020. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling to...
The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large‐scale genetic and molecular st...
Mixture modeling that takes account of potential heterogeneity in data is widely adopted for classification and clustering problems. However, it can be sensitive to outliers especially when the mixture components are Gaussian. In this paper, we introduce the robust estimating methods using the weighted complete estimating equations for robust fitti...
Small area estimation is recognized as an important tool for producing reliable estimates under limited sample information. This paper reviews techniques of small area estimation using mixed models, covering from basic to recently proposed advanced ones. We first introduce basic mixed models for small area estimation, and provide several methods fo...
We introduce a new class of distributions named log-adjusted shrinkage priors for the analysis of sparse signals, which extends the three parameter beta priors by multiplying an additional log-term to their densities. The key feature of the proposed prior is that its density tail is extremely heavy and heavier than even that of the Cauchy distribut...
Two-stage hierarchical models have been widely used in small area estimation to produce indirect estimates of areal means. When the areas are treated exchangeably and the model parameters are assumed to be the same over all areas, we might lose the efficiency in the presence of spatial heterogeneity. To overcome this problem, we consider a two-stag...
For estimating area-specific parameters such as poverty indicators in a finite population, estimators based only on the area-specific samples have typically high variability due to small sample sizes, and model-based methods are recognized to be useful to increase the accuracy of the estimation by borrowing information from related areas. This arti...
Estimating income distributions plays an important role in the measurement of inequality and poverty over space. The existing literature on income distributions predominantly focuses on estimating an income distribution for a country or a region separately and the simultaneous estimation of multiple income distributions has not been discussed in sp...
Regression models are fundamental tools in statistics, but they typically suffer from outliers. While several robust methods have been proposed based on frequentist approaches, Bayesian methods would be more preferable in terms of easiness of uncertainty quantification of estimation results. In this article, we propose a robust Bayesian method for...
Statistical inference with nonresponse is quite challenging, especially when the response mechanism is nonignorable. Although existing methods often require correct model specifications for response models, the models cannot be verified based on the observed data and misspecification of the response models can lead to a seriously biased inference....
The development of molecular diagnostic tools to achieve individualized medicine requires accurate estimation of individual treatment effects (ITEs). Although several effective data analytic strategies have been proposed for this purpose, they have limitations when it comes to flexibly capturing the complex relationships between clinical outcome an...