Researcher's Publications

Nospare所属研究者発表論文一覧

2024

Kurisu, D., Kato, K., and Shao, X. (2024). Gaussian approximation and spatially dependent wild bootstrap for high-dimensional spatial data. Journal of the American Statistical Association in press. ⇨ 研究紹介記事 (空間データ分析のためのブートストラップ法を開発)
Kurisu, D. and Otsu, T. (2024). Subsampling inference for nonparametric extremal conditional quantiles. Econometric Theory in press.

2023

Kawakubo, Y. and Kobayashi, G. (2023). Small area estimation of general finite-population parameters based on grouped data. Computational Statistics & Data Analysis, 184, 107741.
Wakayama, T. and Sugasawa, S. (2023). Trend filtering for functional data. Stat 12, e590.
Yonekura, S. and Sugasawa, S. (2023). Adaptation of the tuning parameter in general Bayesian inference with robust divergence. Statistics and Computing 33, article number: 39.
Hamura, Y., Irie, K. and Sugasawa, S. (2023). On data augmentation for models involving reciprocal gamma functions. Journal of Computational and Graphical Statistics 32, 908-916.
Ito, T. and Sugasawa, S. (2023). Grouped generalized estimating equations for longitudinal data analysis. Biometrics 79, 1868-1879. (潜在的なクラスター構造を想定した経時データ解析の方法を開発)
Muto, S., Sugasawa, S. and Suzuki, M. (2023). Forecasting the housing vacancy rate in Japan using dynamic spatiotemporal effects models. Japanese Journal of Statistics and Data Science 6, 21-44.
Sugasawa, S., Nakagawa, T., Solvang, H. K., Subby, S. and Alrabeei, S. (2023). Dynamic spatio-temporal zero-inflated Poisson models for predicting Capelin distribution in the Barents sea. Japanese Journal of Statistics and Data Science 6, 1-20.

‍

2022

Ishihara, T. (2022). Panel data quantile regression for treatment effect models. Journal of Business & Economic Statistics 41, 720-736. (処置効果を推定するためのパネル分位点回帰の方法を開発)
Yonekura, S. and Beskos, A. (2022). Online smoothing for diffusion processes observed with noise. Journal of Computational and Graphical Statistics 4, 1344-1360. (拡散過程に対する平滑化の方法を開発)
Sugasawa, S. and Noma, H. (2022). Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling. Biometrical Journal 64, 1142-1152. (疾患関連遺伝子領域を効率的にスクリーニングする方法を開発)
Kurisu, D. (2022). Nonparametric regression for locally stationary functional time series. Electronic Journal of Statistics 16, 3973-3995. ⇨ 研究紹介記事 (関数時系列データに対するノンパラメトリックな回帰分析手法を開発)
Kurisu, D. and Otsu, T. (2022). On linearization of nonparametric deconvolution estimators for repeated measurements model. Journal of Multivariate Analysis 189, 104921.
Kurisu, D. (2022). Nonparametric regression for locally stationary random fields under stochastic sampling design. Bernoulli 28, 1250-1275. (空間データに対するノンパラメトリック回帰の理論的性質を解明)
Kurisu, D. and Otsu, T. (2022). On the uniform convergence of deconvolution estimators from repeated measurements. Econometric Theory 38, 172-193. (繰り返し観測データに対する畳み込み推定量の理論)
Sugasawa, S. and Kobayashi, G. (2022). Robust fitting of mixture models using weighted complete estimating equations. Computational Statistics & Data Analysis 174, 107526. (外れ値に対して頑健に有限混合モデルを当てはめる方法論を開発)
Hamura, H., Irie, K. and Sugasawa, S. (2022). Log-regularly varying scale mixture of normals for robust regression. Computational Statistics & Data Analysis 173, 107517. ⇨ 研究紹介記事 (超裾厚な分布によって外れ値に対して頑健なベイズ分析を実行する方法論を開発)
Sugasawa, S. and Murakami, D. (2022). Adaptively robust geographically weighted regression. Spatial Statistics 48, 100623. (局所的な外れ値に対して頑健な空間回帰手法を開発)
Kobayashi, G., Yamauchi, Y., Kakamu, K., Kawakubo, Y. and Sugasawa, S. (2022). Bayesian approach to Lorenz curve using time series grouped data. Journal of Business and Economic Statistics 40, 897-912. (時間変化するローレンツ曲線を推定する状態空間モデリングを開発)
Hamura, H., Irie, K. and Sugasawa, S. (2022). On global-local shrinkage priors for count data. Bayesian Analysis 17, 545-564. (カウントデータに対する柔軟な事前分布を開発)
Sugasawa, S., Morikawa, K. and Takahata, K. (2022). Bayesian semiparametric modeling of response mechanism for nonignorable missing data. TEST 31, 101-107. (無視できない欠測データを柔軟に補完する手法を開発)

‍

2021

Yonekura, S., Beskos, A. and Singh, S. S. (2021). Asymptotic analysis of model selection criteria for general hidden Markov models. Stochastic Processes and their Applications 132, 164-191. (隠れマルコフモデルに対するモデル選択基準を導出)
Ishihara, T. (2021). Partial identification of nonseparable models using binary instruments. Econometric Theory 37, 817-848. (分離不可なモデルに対する部分識別理論)
Kubokawa, T., Sugasawa, S., Tamae, H. and Chaudhuri, S. (2021). General unbiased estimating equations for variance components in linear mixed models. Japanese Journal of Statistics and Data Science 4, 841-859.
Kobayashi, G., Roh, T., Lee, J. and Choi, T. (2021). Flexible Bayesian quantile curve fitting with shape restrictions under the Dirichlet process mixture of the generalized asymmetric Laplace distribution. Canadian Journal of Statistics 49, 698-730. (形状制約がある分位点回帰関数のベイズ推定法を開発)
Sugasawa, S. and Yonekura, S. (2021). On selection criteria for the tuning parameter in robust divergence. Entropy 23, 1147. (ダイバージェンスのチューニングパラメータをデータドリブンに選択するための規準を導出)
Sugasawa, S. and Murakami, D. (2021). Spatially clustered regression. Spatial Statistics 44, 100525. ⇨ 研究紹介記事 (空間クラスタリングを取り入れた空間回帰分析手法を開発)
Sugasawa, S. and Hashimoto, S. (2021). Robust Bayesian changepoint analysis in the presence of outliers. Proceedings of the 13th KES-IDT Conference on Intelligent Decision Technologies, 469-478.
Sugasawa, S. (2021). Grouped heterogeneous mixture modeling for clustered data. Journal of the American Statistical Association 116, 999-1010. (有限混合モデルを用いたクラスターデータに対する柔軟かつ簡便な分析手法を開発)
Sugasawa, S. and Noma, H. (2021). Efficient screening of predictive biomarkers for individual treatment selection. Biometrics 77, 249-257. (異質な処置効果を予測するバイオマーカーの効率的なスクリーニング手法の開発)
Ito, T. and Sugasawa, S. (2021). Improved confidence regions in meta-analysis of diagnostic test accuracy. Computational Statistics & Data Analysis 153, 107068.
Sugasawa, S. and Noma, H. (2021). A unified method for improved inference in random-effects meta-analysis. Biostatistics 22, 114-130. (サンプルサイズが小さい状況で正確にメタアナリシスを実行するための手法を開発)

‍

2020

Kato, K. and Kurisu, D. (2020). Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations. Stochastic Processes and their Applications 130, 1159-1205.
Kobayashi, G., Sugasawa, S., Tamae, H. and Ozu, T. (2020). Predicting intervention effect for COVID-19 in Japan: state space modeling approach. BioScience Trends 14, 174-181. ⇨ 千葉大学との共同プレスリリース (疫学モデルをベースにした状態空間モデルによって感染者数を予測)
Adusumilli, K., Kurisu, D., Otsu, T. and Whang, Y. J. (2020). Inference on distribution functions under measurement error. Journal of Econometrics 215, 131-164.
Ishihara, T. (2020). Identification and estimation of time-varying nonseparable panel data models without stayers. Journal of Econometrics 215, 184-208.
Sugasawa, S. and Kubokawa, T. (2020). Small area estimation with mixed models: a review. Japanese Journal of Statistics and Data Science 3, 693-720. (小地域推定の包括的なレビュー)
Hashimoto, S. and Sugasawa, S. (2020). Robust Bayesian regression with synthetic posterior distributions. Entropy 22, 661.
Sugasawa. S. (2020). Small area estimation of general parameters: Bayesian transformed spatial prediction approach. Japanese Journal of Statistics and Data Science 3, 167-181.
Sugasawa, S., Kobayashi, G. and Kawakubo, Y. (2020). Estimation and inference for area-wise spatial income distributions from grouped data. Computational Statistics & Data Analysis 145, 106904.
Sugasawa, S. (2020). Robust empirical Bayes small area estimation with density power divergence. Biometrika 107, 467-480.
Sugasawa, S., Kawakubo, Y. and Ogasawara, K. (2020). Small area estimation with spatially varying natural exponential families. Journal of Statistical Computation and Simulation 90, 1039-1056.

‍

Researcher's Books

Nospare所属研究者出版書籍リスト‍

標準ベイズ統計学

Hoff P. 著『A First Course in Bayesian Statistical Methods』の訳本。基礎からベイズ統計学を学ぶために最適な入門書となっている。

極値現象の統計分析 -裾の重い分布のモデリング-

Resnick S.I. 著『Heavy-Tail Phenomena: Probabilistic and Statistical Modeling』の訳本。ファイナンスや自然災害で現れる極端なデータを分析するための方法論について解説している。