21 citations · 90 across the 97 of their papers we have counts for
10 papers · 1 filter
Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score
Shunsuke Imai, Takuya Koriyama, Shouto Yonekura +2
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 th…
Fast and Locally Adaptive Bayesian Quantile Smoothing using Calibrated Variational Approximations
Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In…
Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes
Shonosuke Sugasawa, Jae Kwang Kim, Kosuke Morikawa
This paper proposes a flexible Bayesian approach to multiple imputation using conditional Gaussian mixtures. We introduce novel shrinkage priors for covariate-dependent mixing prop…
Locally Adaptive Bayesian Isotonic Regression using Half Shrinkage Priors
Ryo Okano, Yasuyuki Hamura, Kaoru Irie +1
Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. Th…
Spatio-temporal smoothing, interpolation and prediction of income distributions based on grouped data
Genya Kobayashi, Shonosuke Sugasawa, Yuki Kawakubo
The Housing and Land Survey (HLS) of Japan provides municipality-level grouped data on household incomes. Although such data can be invaluable for effective local policymaking, the…
Functional Horseshoe Smoothing for Functional Trend Estimation
Tomoya Wakayama, Shonosuke Sugasawa
Due to developments in instruments and computers, functional observations are increasingly popular. However, effective methodologies for flexibly estimating the underlying trends w…
Sparse Bayesian inference on gamma-distributed observations using shape-scale inverse-gamma mixtures
Yasuyuki Hamura, Takahiro Onizuka, Shintaro Hashimoto +1
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 prior…
Bayesian Spatial Predictive Synthesis
Danielle Cabel, Shonosuke Sugasawa, Masahiro Kato +2
Due to spatial dependence -- often characterized as complex and non-linear -- model misspecification is a prevalent and critical issue in spatial data analysis and prediction. As t…
On Data Augmentation for Models Involving Reciprocal Gamma Functions
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
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…
Locally Adaptive Spatial Quantile Smoothing: Application to Monitoring Crime Density in Tokyo
Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Spatial trend estimation under potential heterogeneity is an important problem to extract spatial characteristics and hazards such as criminal activity. By focusing on quantiles, w…