Publication

Refereed Papers

[1] Fujisawa, H. (1995). A note on the maximum likelihood estimators for multivariate normal distribution with monotone data. Communications in Statistics - Theory and Methods, Vol.24, 1377-1382.

[2] Fujisawa, H. (1996). Estimation on random coefficient model with unbalanced data. Statistics & Probability Letters, Vol.28, 251-257.

[3] Fujisawa, H. (1996). The maximum likelihood estimators in a multivariate normal distribution with AR(1) covariance structure for monotone data. Annals of the Institute of Statistical Mathematics, Vol.48, 423-428.

[4] Fujisawa, H. (1997). Improvement on chi-squared approximation by monotone transformation. Journal of Multivariate Analysis, Vol.60, 84-89.

[5] Fujisawa, H. (1997). Likelihood ratio criterion for mean structure in the growth curve model with random effects, Journal of Multivariate Analysis, Vol.60, 90-98.

[6] Fujisawa, H. (1997). Efficient tests for mean structure in random effects models. Hiroshima Mathematical Journal, Vol.27, 487-512.

[7] Fujisawa, H. (1999). Effects of unpaired data for estimating an interclass correlation. Communications in Statistics - Theory and Methods, Vol.28, 245-254.

[8] Fujisawa, H. (2000). Variance stabilizing transformation and studentization for estimator of correlation coefficient. Statistics & Probability Letters, Vol.47, 213-217.

[9] Fujisawa, H. and Izumi, S. (2000). Inference about misclassification probabilities from repeated binary responses. Biometrics, Vol.56, 706-711.

[10] Fujisawa, H. (2002). On usefulness of maximun likelihood estimator using incomplete data. Measurements and Multivariate Analysis (eds. S. Nishisato et al.), Springer, 227-232.

[11] Fujisawa, H. (2003). Asymptotic properties of conditional maximum likelihood estimator in a certain exponential model. Journal of Multivariate Analysis, Vol.86, 126-142.

[12] Fujisawa, H., Eguchi, S., Ushijima, M., Miyata, S., Miki, Y., Muto, T., and Matsuura, M. (2004). Genotyping of single nucleotide polymorphism using model-based clustering. Bioinformatics, Vol.20, 718-726.

[13] Nakamura, T., Shoji, A., Fujisawa, H., and Kamatani, N. (2005). Cluster analysis and association study for structured multilocus genotype data. Journal of Human Genetics, Vol.50, 53-61.

[14] Kanari, Y., Clerici, M., Abe, H., Kawabata, H., Trabattoni, D., Caputo, S. L., Mazzotta, F., Fujisawa, H., Niwa, A., lshihara, C., Takei, Y. A., and Miyazawa, M. (2005). Genotypes at chromosome 22q12-13 are associated with HIV-1-exposed but uninfected status in Italians. AIDS, Vol.19, 1015-1024.

[15] Fujisawa, H. and Eguchi, S. (2006). Robust estimation in the normal mixture model. Journal of Statistical Planning and Inference, Vol.136, 3989-4011.

[16] Fushiki, T., Fujisawa, H., and Eguchi, S. (2006). Identification of biomarkers from mass spectrometry data using a "common" peak approach. BMC Bioinformatics, Vol.7, No.358.

[17] Ninomiya, Y. and Fujisawa, H. (2007). A conservative test for multiple comparison based on highly correlated test statistics. Biometrics, Vol.63, 1135-1142.

[18] Fujisawa, H., Isomura, M., Eguchi, S., Ushijima, M., Miyata, S., Miki, Y., and Matsuura, M. (2007). Identifying haplotype block structure by using ancestor-derived model. Journal of Human Genetics, Vol.52, 738-746.

[19] Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. Journal of Multivariate Analysis, Vol.99, 2053-2081. (This paper was ranked in the top 25 hottest articles (October to December 2008) and in the top 15 most cited articles published since 2008 within recent 5 years (Jul. 2013).)

[20] Fujisawa, H., Horiuchi, Y., Harushima, Y., Takada, T., Eguchi, S., Mochizuki, T., Sakaguchi, T., Shiroishi, T. and Kurata, N. (2009). SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip. BMC Bioinformatics, Vol.10, No.131.

[21] Horiuchi, Y., Harushima, Y., Fujisawa, H., Mochizuki, T., Kawakita, M., Sakaguchi, T. and Kurata, N. (2010). A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression arrays. BMC Genomics, Vol.11, No.315.

[22] Kumasaka, N., Fujisawa, H, Hosono, N., Okada, Y., Takahashi, A., Nakamura, Y. Kubo, M. and Kamatani, N. (2011). PlatinumCNV: a Bayesian Gaussian Mixture Model for Genotyping Copy Number Polymorphisms Using SNP Array Signal Intensity Data. Genetic Epidemiology, Vol.35, 831-844.

[23] Yanagihara, H. and Fujisawa, H. (2012). Iterative bias correction of the cross-validation criterion. Scandinavian Journal of Statistics, Vol.39, 116-130.

[24] Fujisawa, H. and Sakaguchi, T. (2012). Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed. TEST, Vol.21, 280-300.

[25] Yanagihara, H., Yuan, K.-H., Fujisawa, H. and Hayashi, K. (2013). A class of cross-validatory model selection criteria. Hiroshima Mathematical Journal, Vol.43, 149-177

[26] Takada, T., Ebata, T., Noguchi, H., Keane, T.M., Adams, D.J., Narita, T., Shin-I, T., Fujisawa, H., Toyoda, A., Abe, K., Obata, Y., Sakaki, Y., Moriwaki, K., Fujiyama, A., Kohara, Y. and Shiroishi, T. (2013). The ancestor of extant Japanese fancy mice contributed to the mosaic genomes of classical inbred strains.Genome Research, Vol.23, 1329-1338.

[27] Fujisawa, H. (2013). Normalized estimating equation for robust parameter estimation. Electronic Journal of Statistics, Vol. 7, 1587-1606.

[28] Kuriki, S. Harushima, Y., Fujisawa, H. and Kurata, N. (2014). Approximate tail probabilities of the maximum of a chi-square field on multi-dimensional lattice points and their applications to detection of loci interactions. Annals of the Institute of Statistical Mathematics, Vol. 66, 725-757.

[29] Kanamori, T. and Fujisawa, H. (2014). Affine invariant divergences associated with composite scoring rules and their applications. Bernoulli, Vol.29, 2278-2304.

[30] Oka, A., Takada, T., Fujisawa, H., and Shiroishi, T. (2014). Evolutionarily Diverged Regulation of X-chromosomal Genes as a Primal Event in Mouse Reproductive Isolation. PLoS Genetics, Vol.10, e1004301.

[31] Kanamori, T. and Fujisawa, H. (2015). Robust estimation under heavy contamination using unnormalized models. Biometrika, Vol.102, 559-572. 

[32] Fujisawa, H. and Abe, T. (2015). A family of skew distributions with mode-invariance through transformation of scale. Statistical Methodology, Vol.25, 89-98.

[33] Kawano, S., Fujisawa, H., Takada, T., and Shiroishi, T. (2015). Sparse principal component regression with adaptive loading. Computational Statistics and Data Analysis, Vol.89, 192-203. 

[34] Horiuchi, Y., Harushima, Y., Fujisawa, H., Mochizuki, T., Fujita, M., Ohyanagi, H. and Kurata, N. (2015). Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes. BMC Genomics, Vol.16, No.1099. 

[35] Chen, T.-L., Fujisawa, H., Huang, S.-Y. and Hwang, C.-R. (2016). On the weak convergence and central limit theorem of blurring and nonblurring processes with application to robust location estimation. Journal of Multivariate Analysis, Vol.143, 165-184. 

[36] Katayama, S. and Fujisawa, H. (2017). Sparse and robust linear regression: an optimization algorithm and its statistical properties. Statistica Sinica, Vol.27, 1243-1264. 

[37] Hirose, K., Fujisawa, H. and Sese, J. (2017). Robust sparse Gaussian graphical modeling. Journal of Multivariate Analysis, Vol.161, 172-190. 

[38] Kawashima, T. and Fujisawa, H. (2017). Robust and Sparse Regression via gamma-divergence. Entropy, Vol.19, No.608. 

[39] Takada, M., Suzuki, T. and Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. The 21st International Conference on Artificial Intelligence and Statistics (AISTATS). 

[40] Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2018). Sparse principal component regression for generalized linear models. Computational Statistics and Data Analysis, Vol.124, 180-196.

[41] Katayama, S., Fujisawa, H. and Drton, M. (2018).  Robust and sparse Gaussian graphical modeling under cell-wise contamination.  Stat, Vol.7, e181. 

[42] Tomita, H., Fujisawa, H. and Henmi, M. (2018). A bias-corrected estimator in multiple imputation for missing data. Statistics in Medicine, Vol.37, 3373-3386.

[43] Takada, M., Fujisawa, H. and Nishikawa, T. (2019). HMLasso: Lasso with High Missing Rate. The 28th International Joint Conference on Artificial Intelligence (IJCAI). 

[44] Kawashima, T. and Fujisawa, H. (2019). Robust and Sparse Regression in Generalized Linear Model by Stochastic Optimization. Japanese Journal of Statistics and Data Science, Vol.2, 465-489. 

[45] Abe, T. and Fujisawa, H. (2019). Multivariate skew distributions with mode-invariance through transformation of scale. Japanese Journal of Statistics and Data Science, Vol.2,  529–544. 

[46] Takada, M., Suzuki, T. and Fujisawa, H. (2020). Independently Interpretable Lasso for Generalized Linear Models. Neural Computation, Vol.32, 1168-1121. 

[47] Takada, M. and Fujisawa, H. (2020). Transfer Learning via l1 Regularization. The 34th Annual Conference on Neural Information Processing Systems (NeurIPS). arXiv:2006.14845 

[48] Abe, T., Fujisawa, H., Kawashima, T. and Ley, C. (2021). EM algorithm using overparameterization for multivariate skew-normal distribution. Econometrics and Statistics, Vol.19, 151-168. 

[49] Harada, K. and Fujisawa, H. (2021). Sparse Estimation of Linear Non-Gaussian Acyclic Model for Causal Discovery. Neurocomputing, Vol.459, 223-233. 

[50] Kawashima, T. and Fujisawa, H. (2023). Robust regression against heavy heterogeneous contamination. Metrika, Vol.88, 421–442.

[51] Harada, K. and Fujisawa, H. (2024). Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference. Statistica Sinica, Vol.34, 133-155.  (5 Jan 2024)

[52] Nagumo, R. and Fujisawa, H. (2024). Density Ratio Estimation with Doubly Strong Robustness. The 41st International Conference on Machine Learning (ICML). (2 May 2024)

Papers (preprint)

Sasai, T. and Fujisawa, H. (2020). Robust estimation with Lasso when outputs are adversarially contaminated. (13 Apr 2020) arXiv:2004.05990  

Sasai, T. and Fujisawa, H. (2021). Adversarial robust weighted Huber regression. (22 Feb 2021) arXiv:2102.11120  

Sasai, T. and Fujisawa, H. (2022). Outlier Robust and Sparse Estimation of Linear Regression Coefficients. (31 Aug 2022) arXiv:2208.11592 

Takada, M. and Fujisawa, H. (2023). Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective. (31 Aug 2023) arXiv:2308.15838