Sakamoto, W. (2007). Extracting non-linear additive regression structure with power-additive smoothing splines. Journal of the Japanese Society of Computational Statistics, 20(1), 83-108.
Sakamoto, W. (2007). MARS: selecting basis functions and knots with an empirical Bayes method. Computational Statistics, 22(1), 583-597
Sakamoto, W. and Shirahata, S. (1999). Likelihood-based cross-validation score for selecting the smoothing parameter in maximum penalized likelihood estimation. Communications in Statistics:Theory and Methods,28(7), 1671-1698.
Sakamoto, W. and Shirahata, S. (1997). Simple calculation of likelihood-based cross-validation score in maximum penalized likelihood estimation of regression functions. Journal of the Japanese Society of Computational Statistics, 10(1), 27-40.
Yamaguchi, Y., Sakamoto, W., Goto, M., Staessen, J. A., Wang, J., Gueyffier, F. and Riley, R. D. (2014). Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data. Research Synthesis Methods, doi: 10.1002/jrsm.1119.
Yamaguchi, Y., Sakamoto, W., Shirahata, S. and Goto, M. (2013). An evaluation of treatment-covariate interaction in meta-analysis with marginalizing the missing individual patient data. Journal of the Japanese Society of Computational Statistics, 26(1), 1-16.
Thammapalo, S., Nagao, Y., Sakamoto, W., Saengtharatip, S., Tsujitani, M., Nakamura, Y., Coleman, P. G. and Davies, C. (2008). Relationship between transmission intensity and incidence of Dengue hemorrhagic fever in Thailand. PLoS Neglected Tropical Diseases, 2(7), e263 (13 pages).
Baba, M., Fujisawa, M., Sakamoto, W. and Goto M. (2003). Statistical evaluation of umbrella dose-response relationships. Journal of the Japanese Society of Computational Statistics, 15(2), 281-294.
国際会議発表 / Presentations in International Conferences
プロシーディングス論文（筆頭著者，査読付き）
Sakamoto, W. (2016). Cluster detection of disease mapping data based on latent Gaussian Markov random field models. Proceedings of COMPSTAT 2016: 22th International Conference on Computational Statistics, pp. 267-277 (2016.8, Oviedo, Spain)
Sakamoto, W. (2009). Selecting an appropriate transformation of responses for fitting a linear or additive mixed model. Proceedings of the 57th Session of the International Statistical Institute (ISI 2009) (2 pages). (2009.8, Durban, South Africa)
Sakamoto, W. (2008). Selecting an appropriate transformation of responses for fitting a semiparametric mixed model. International Joint Session of CSA, JSS and KSS. (2008.12, Taipei)
Sakamoto, W. (2008). Selecting an appropriate transformation of responses for fitting a semiparametric mixed model. IASC2008: Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis: Proceedings (CD-ROM: 6 pages). (2008.12, Yokohama, Japan)
Sakamoto, W. (2007). A simulation study on evaluating contribution of variables with empirical Bayes MARS. Bulletin of the International Statistical Institute 56th Session: Proceedings (CD-ROM: 4 pages). (2007.8, Lisbon, Portugal)
Sakamoto, W. (2006). MARS: selecting basis and knots with the empirical Bayes method. Compstat 2006: Proceedings in Computational Statistics (CD-ROM), pp. 1397-1404. (2006.8, Rome, Italy)
Sakamoto, W. (2005). MARS: selecting basis and knots with the empirical Bayes method. Proceedings of the 5th IASC Asian Conference on Statistical Computing, pp. 135-138. (2005.12, Hong Kong)
Sakamoto, W. (2003). Exploring nonlinear structure with nonparametric regression. Proceedings of the 54th Conference of the International Statistical Institute (CD-ROM: 2 pages). (2003.8, Berlin, Germany)
Sakamoto, W. (2002). Approximation of maximum marginal likelihood in non-Gaussian nonparametric regression models. Proceedings of the 4th ARS Conference of the IASC, pp. 22-25. (2002.12, Busan, Korea)
Sakamoto, W. and Shirahata, S. (1997). Simple calculation of likelihood-based cross-validation score in maximum penalized likelihood estimation. Multivariate Analysis and Computing: Proceedings of the Ninth Korea and Japan Joint Conference of Statistics (KJCS-97), pp. 267-272. (1997.12, Jeju-do, Korea)
プロシーディングス論文（筆頭著者以外，査読付き）
Yamaguchi, Y., Sakamoto, W., Shirahata, S. and Goto, M. (2011). A meta-analysis method based on simulated individual patient data. The 58th World Statistical Congress of the International Statistical Institute (ISI 2011): Proceedings (USB media, 6 pages). (2011.8, Dublin, Ireland)
Harahap, E., Sakamoto, W. and Nishi, H. (2010). Failure Prediction method for network management system by using Bayesian network and Shared database. 8th Asia-Pacific Symposium on Information and Telecommunication Technologies (APSITT 2010) : Proceedings (in e-media). (2010.6, Sarawak, Malaysia)
Baba, M., Sakamoto, W. and Goto, M. (2002). Statistical evaluation of umbrella dose-response relationships. Proceedings of the 4th ARS Conference of the IASC, pp.185-186. (2002.12, Busan, Korea)
プロシーディングス論文（筆頭著者，査読なし）
Sakamoto, W. (2016). An analysis of Japanese liver cancer mortality data with Bayesian age-period-cohort models. Proceedings of the International Conference for JSCS 30th Anniversary in Seattle (4 pages) (2016.10, Seattle, USA).
Sakamoto, W. (2005). Diagnosing non-linear regression structure with power additive smoothing splines. Proceedings of the ISM/KIER Joint Conference on Nonparametric and Semiparametric Statistics, pp. 249-262. (2005.3, Tokyo, Japan)
その他（アブストラクトのみ）
Sakamoto, W. (2016). Cluster detection of disease mapping data based on latent Gaussian Markov random field models. 2016 IASC-ARS Conference (2016.11, Daejeon, Korea) （招待セッション講演）
Sakamoto, W. (2016). Environmental and medical applications of latent Gaussian Markov random field models. Math and Stat Special Research Colloquium, Utah State University (2016.10, USU, USA)
Hagihara, S. and Sakamoto, W. (2015). Performance of Bayesian inference with integrated nested Laplace approximation in generalized linear mixed effect models. The 24th South Taiwan Statistical Conference (2015.6, Changhua, Taiwan)
Sakamoto, W. (2012). Selecting an optimal mixed effect model based on information criteria. ISBIS 2012: Abstracts (2012.6, Bangkok, Thailand) （招待セッション講演）
Sakamoto, W. (2011). Selecting variance structure in mixed effect models by information criteria based on Monte Carlo approximations. Joint Meeting of the 2011 Taipei International Statistical Symposium and 7th Conference of the Asian Regional Section of the IASC: Abstract, p.160. (2011.12, Taipei, Taiwan)
Sakamoto, W. (2010). Selecting an optimal mixed effect model based on information criteria. COMPSTAT'2010: Book of Abstracts, p.141. (2010.8, Paris, France)
Sakamoto, W. (2008). Selecting an appropriate transformation of responses for fitting a semiparametric mixed model. International Joint Session of CSA, JSS and KSS. (2008.12, Taipei) （招待セッション講演）
Sakamoto, W. (2006). Selecting basis and knots in MARS with an empirical Bayes method. Conference on Nonparametric Statistics and Related Topics: speakers’ abstracts (2006.9, Carleton University, Ottawa, Canada)
Sakamoto, W. (2005). Diagnosing non-linear regression structure with power additive smoothing splines. Abstracts of the 14th International Workshop on Matrices and Statistics, p.32. (2005.3, Auckland, NZ)