Professor HONDA, Toshio

[Education]
	1987   B.A. (Engineering), University of Tokyo
	1989   M.A. (Engineering), University of Tokyo
	1992   Ph.D. (Engineering), University of Tokyo
[Employment History]
	1992   Associate Researcher, Mitsubishi Research Institute, Inc.
	1994   Assistant Professor, Institute of Social Sciences, University of Tsukuba
	1999   Associate Professor, Institute of Social Sciences, University of Tsukuba
	2004   Professor, Graduate School of Economics, Hitotsubashi University

[Fields of Specialization]

   Statistics, Econometrics

[Present Research Themes]

   Nonparametric estimation, Semiparametric estimation, Survival analysis, High-dimensional problems

[Language]

   Japanese, English

[Major Publications]

• Minimax estimators in the Manova model for arbitrary quadratic loss and unknown covariance matrix. Journal of Multivariate Analysis, 36(1991), 113-120.
• Estimation of the mean by three stage procedure. Sequential Analysis, 11(1992), 73-89.
• Construction of a confidence interval by triple sampling. Sequential Analysis, 11(1992), 273-287.
• The effect of heteroscedasticity on the actual size of the Chow test. Journal of the Japan Statistical Society, 26 (1996), 127-134. (co-authored by A. Takemura) Available at https://doi.org/10.14490/jjss1995.26.127
• The CUSUM tests with nonparametric regression residuals. Journal of the Japan Statistical Society, 27 (1997), 45-63. Available at https://doi.org/10.14490/jjss1995.27.45
• Testing the goodness of fit of a linear model by kernel regression. Communications in Statistics-Theory and Methods, 27 (1998), 529-546.
• Sequential estimation of the marginal density function for a strongly mixing process. Sequential Analysis, 17(1998), 239-251.
• Root-n-consistent semiparametric estimation of partially linear models for weakly dependent observations. Communications in Statistics-Theory and Methods, 28 (1999), 2001-2020.
• Nonparametric estimation of a conditional quantile for α-mixing processes. Annals of the Institute of Statistical Mathematics, 52 (2000), 459-470.
• Nonparametric density estimation for a long-range dependent linear process. Annals of the Institute of Statistical Mathematics, 52 (2000), 599-611.
• Nonparametric estimation of the conditional median function for long-range dependent processes. Journal of the Japan Statistical Society, 30 (2000), 129-142. Available at https://doi.org/10.14490/jjss1995.30.129
• Quantile regression in varying coefficient models. Journal of Statistical Planning and Inference, 121(2004), 113-125.
• Nonparametric regression with current status data. Annals of the Institute of Statistical Mathematics, 56(2004), 49-72.
• Nonparametric regression in proportional hazards models. Journal of the Japan Statistical Society, 34(2004), 1-17.
• Estimation in additive Cox models by marginal integration. Annals of the Institute of Statistical Mathematics, 57(2005), 403-423. Available at https://hdl.handle.net/10086/14605
• Estimation in Partial Linear Models under Long-Range Dependence. Discussion Papers #2007-7, Graduate School of Economics, Hitotsubashi University. Available at https://hdl.handle.net/10086/17011
• Nonparametric density estimation for linear processes with infinite variance. Annals of the Institute of Statistical Mathematics, 61(2009), 413-439. Available at https://hdl.handle.net/10086/16959
• A limit theorem for sums of bounded functional of linear processes without finite mean. Probability and Mathematical Statistics, 29(2009), 337-351.
• Nonparametric regression for dependent data in the errors-in-variables problem. Journal of Statistical Planning and Inference, 140 (2010), 3409-3424.
• Nonparametric estimation of conditional medians for linear and related processes. Annals of the Institute of Statistical Mathematics, 62 (2010), 995-1021.
• Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors. Annals of the Institute of Statistical Mathematics, 65 (2013), 23-47.
  (Global COE Hi-Stat Discussion Paper Series 157, Hitotsubashi University (2010). Available at https://hdl.handle.net/10086/18811)
• Nonparametric LAD Cointegrating Regression. Journal of Multivariate Analysis, 117 (2013), 150-162.
  (Global COE Hi-Stat Discussion Paper Series 207, Hitotsubashi University (2011). Available at https://hdl.handle.net/10086/19400)
• Variable selection in Cox regression models with varying coefficients. (jointly worked with Wolfgang Karl Härdle), Journal of Statistical Planning and Inference, 148 (2014), 67-81. (SFB 649 Discussion Paper 2012-061, Humboldt-Universität zu Berlin, Germany (2012). Available at http://sfb649.wiwi.hu-berlin.de/fedc/discussionPapers.php)
• Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data. (jointly worked with Ming-Yen Cheng, Jialiang Li, and Heng Peng), Annals of Statistics, 42 (2014), 1819-1849. (Available at http://arxiv.org/abs/1308.3942)
• Discussion on “Varying Coefficient Regression Models: A Review and New Developments by B.U.Park et al.”, International Statistical Review, 83 (2015), 68-70. (Available at http://onlinelibrary.wiley.com/doi/10.1111/insr.v83.1/issuetoc)
• Forward variable selection for sparse ultra-high dimensional varying coefficient models. (jointly worked with CHENG Ming-Yen, ZHANG Jin-Ting), Journal of the American Statistical Association, 111 (2016), 1209-1221. (Available at http://www.tandfonline.com/doi/full/10.1080/01621459.2015.1080708) (Hitotsubashi Invited Fellow Program Discussion Paper Series 27, Hitotsubashi University (2015). Available at https://hdl.handle.net/10086/26895)
• Efficient estimation in semivarying coefficient models for longitudinal/clustered data. (jointly worked with CHENG Ming-Yen, LI Jialiang), Annals of Statistics (http://imstat.org/aos/), 44(5) (2016), 1988-2017. (Available at http://www1.econ.hit-u.ac.jp/honda/AOS1506-015R1A0.pdf)
• Variable selection and structure identification for varying coefficient Cox models. (jointly worked with Ryota Yabe), Journal of Multivariate Analysis, 161(2017), 103-122. (Discussion Papers #2016-05. Available at https://hdl.handle.net/10086/28268)
• Adaptively weighted group Lasso for semiparametric quantile regression models. (jointly worked with ING Ching-Kang, WU Wei-Ying), Bernoulli, 25(2019), 3311-3338. Available at http://dx.doi.org/10.3150/18-BEJ1091 (Discussion Papers #2017-04. Available at https://hdl.handle.net/10086/28459)
• The de-biased group Lasso estimation for varying coefficient models. Annals of the Institute of Statistical Mathematics, 73(2021), 3-29. Available at http://dx.doi.org/10.1007/s10463-019-00740-4 (Discussion Papers #2018-04. Available at https://hdl.handle.net/10086/30520)
• Forward Variable Selection for Sparse Ultra-High Dimensional Generalized Varying Coefficient Models. (jointly workd with Lin Chien-Tong), Japanese Journal of Statistics and Data Science, 4 (2021), 151-179. Available at http://dx.doi.org/10.1007/s42081-020-00090-z (Discussion Papers #2020-01, Graduate School of Economics, Hitotsubashi University. Available at https://hdl.handle.net/10086/30969)
• Forward variable selection for ultra-high dimensional quantile regression models. (jointly worked with Lin Chien-Tong), Annals of the Institute of Statistical Mathematics, 75(2023), 393-424. Available at https://link.springer.com/article/10.1007/s10463-022-00849-z
• Sparse quantile regression via 𝓁₀-penalty. Discussion Papers #2023-03, Graduate School of Economics, Hitotsubashi University. Available at https://hdl.handle.net/10086/84419

[Editorial Services]

   Annals of the Institute of Statistical Mathematics, Co-editor (2011-2015), Associate editor (2003-2011,2015-)
   Japanese Journal of Statistics and Data Science, Coordinating editor (2023-2026)