[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)
[53] Koyama, K., Kawashima, T. and Fujisawa, H. (2024). Sparse Modal Regression with Mode-Invariant Skew Noise. Transactions on Machine Learning Research, https://openreview.net/forum?id=63r6M1JkXm. (9 Sep 2024)
[54] Sasai, T. and Fujisawa, H. (2025). Outlier Robust and Sparse Estimation of Linear Regression Coefficients. Journal of Machine Learning Research, Vol.26, No.93, 1-79. (16 Jun 2025)
[55] Ishizuka, H. and Fujisawa, H. (2025). Robust Parameter Estimation of Non-linear State Space Models Using a Divergence-based Estimator. IEEE Transactions on Signal Processing, Vol.73, 2433-2447. (14 Aug 2025)
[56] Sasai, T. and Fujisawa, H. (2025). Sparse Linear Regression When Noises and Covariates Are Heavy-Tailed and Contaminated by Outliers. Electronic Journal of Statistics, Vol. 19, No. 2, 5171-5215. (18 Oct 2025)
Takada, M. and Fujisawa, H. (2023). Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective. (31 Aug 2023) arXiv:2308.15838