[140]  A new integrated discrimination improvement index via odds. Kenichi Hayashi, Shinto Eguchi, Statistical Papers  (2024).

[139] MT Method for Anomaly Detection and Classification using EM-λ Algorithm.  K Tateishi, H Iwamoto, S Eguchi, Y Nagata. Quality Innovation Prosperity 18, 1, 1-14 (2024)

[138] Robust minimum divergence estimation in a spatial Poisson point process. Y Saigusa, S Eguchi, O Komori.  Ecological Informatics 81, (2024), 102569 

[137] The T-method with the application of sparse modeling. R Asano, M Ohkubo, S Eguchi, Y Nagata. Total Quality Science 9 (1), 1-7 (2023)

[136] Statistical learning for species distribution models in ecological studies.  Komori, O., Saigusa, Y., & Eguchi, S. (2023).  Japanese Journal of Statistics and Data Science, 1-24. 

[135] Mahalanobis-Taguchi Method for Anomaly Detection and Classification. K Honma, M Ohkubo, S Eguchi, Y Nagata. (2022). Total Quality Science 8 (1), 1-13

[134] Robust self-tuning semiparametric PCA for contaminated elliptical distribution. Hung Hung, Su-Yun Huang & Shinto Eguchi  (2022). IEEE Transactions on Signal Processing 70, 5885-5897.

[133] AUC-optimized synthesis of prediction models from a meta-analytical perspective. Daisuke Yoneoka, Katsuhiro Omae, Masayuki Henmi & Shinto Eguhi (2022). Research Synthesis Methods 14, 2, 234-246.

[132] Minimum information divergence of Q-functions for dynamic treatment resumes. Shinto Eguchi.  (2022).  Information Geometry Information Geometry 7, 229-249.

[131]  Active learning by query by committee with robust divergences.  Hideitsu Hino & Shinto Eguchi. (2022). To appear in Information Geometry.

[130]  Minimum Divergence Methods in Statistical Machine Learning: From an Information Geometric Viewpoint.  Shinto Eguchi & Osamu Komori. (2022).  Springer Nature.  

[129]  Generalized quasi-linear mixed-effects model. Yusuke Saigusa, Shinto Eguchi, Osamu Komori. (2022). Statistical Methods in Medical Research, 31 (7), 1280–1291.

 [128]  Copula-based measures of asymmetry between the lower and upper tail probabilities. Kato, S., Yoshiba, T., & Eguchi, S. (2022). Statistical Papers, 63:1907–1929.

[127]  Pythagoras theorem in information geometry and applications to generalized linear models.  Eguchi, S. (2021).  In Handbook of Statistics (Vol. 45, pp. 15-42). Elsevier. 

[126]  Novel robust time series analysis for long-term and short-term prediction.   Okamura, H., Osada, Y., Nishijima, S., & Eguchi, S. Scientific reports, 11 (1), 1-8. (2021).

 [125]  A unified formulation of k-means, fuzzy c-means and Gaussian mixture model by the Kolmogorov–Nagumo average. Komori O. & Eguchi S.  Entropy, 23 (5), 518, 2021.

 [124]  Quasi-linear Cox proportional hazards model with cross-L 1 penalty. K. Omae, & S. Eguchi. BMC medical research methodology 20 (1), 1-12, 2020.

[123] Sampling bias correction in species distribution models by quasi-linear Poisson point process. Komori O., Eguchi S., Saigusa Y., Kusumoto B. & Kubota Y.  Ecological Informatics, 55, 101015, 2020.

[122] Strong model dependence in statistical analysis: goodness of fit is not enough for model choice. J. Copas, S. Eguchi.  Annals of the Institute of Statistical Mathematics,  72 (2), 329-352, 2020.

[121] Statistical Methods for Imbalanced Data in Ecological and Biological Studies. Komori O. and Eguchi S. SpringerBriefs in Statistics. Springer, Tokyo, 2019.

[120] Predicting precision matrices for color matching problem. T. Nakamoto, R. Nishii and S. Eguchi. International Journal of Mathematics for Industry,  11 (01), 1950002, 2019.

[119] The power‐integrated discriminant improvement: An accurate measure of the incremental predictive value of additional biomarkers. K. Hayashi and S. Eguchi. Statistics in Medicine, 2019, 38, 14, 2589-2604.

[118] Information Geometry Associated with Generalized Means. S. Eguchi, O. Komori, A. Ohara. Information Geometry and its Applications IV, 279-295. Springer Cham, 2018.

[117] Information Geometry of Predictor Functions in a Regression Model. S. Eguchi, K. Omae. International Conference on Geometric Science of Information, 561-568. Springer, Cham, 2017.

[116] Spontaneous Learning for Data Distributions via Minimum Divergence. S. Eguchi, A. Notsu, O. Komori. Chapter, Computational Information Geometry, Part of the series Signals and Communication Technology, 2017, 79-99 Springer.

[115] Diurnal Transcriptome and Gene Network Represented Through Sparse Modeling in Brachypodium distachyon. S. Koda, Y. Onda, H. Matsui, K. Takahagi, Y. Yamaguchi-Uehara, M. Shimizu, K. Inoue, T. Yoshida, T. Sakurai, H. Honda, S. Eguchi, R. Nishii, K. Mochida. Frontiers in Plant Science, 2017, 8: 2055.

[114] Robust bias correction model for estimation of global stock status in fishery. O. Komori, S. Eguchi, Y. Saigusa, H. Okamura and M. Ichinokawa. Ecosphere, 2017, 8, 12 e02038 .

[113] Target-based catch-per-unit-effort standardization in multispecies fisheries. H. Okamura, S. Hotta Morita, T. Funamoto, M. Ichinokawa, S. Eguchi. Canadian Journal of Fisheries and Aquatic Sciences, 2017, 999: 1-12.

[112] Quasi-linear score for capturing heterogeneous structure in biomarkers. K. Omae, O. Komori and S. Eguchi. BMC Bioinformatics (2017) 18:308

[111] Reproducible detection of disease-associated markers from gene expression data. K. Omae, O. Komori and S. Eguchi. BMC Medical Genomics (2016) 9:53.

[110] Robust clustering method in the presence of scattered observations. A. Notsu and S. Eguchi. Neural Computation (2016) 28 (6), 1141-1162.

[109] Risk assessment of radioisotope contamination for aquatic living resources in and around Japan. Okamura, H., Ikeda, S., Morita, T., & Eguchi, S. (2016). Proceedings of the National Academy of Sciences, 113.14 (2016): 3838-3843.

[108] Path connectedness on a space of probability density functions. Eguchi, S. and O. Komori. Geometric Science of Information. Springer International Publishing, 2015. 615-624.

[107] An asymmetric logistic regression model for ecological data. O. Komori, S. Eguchi, S. Ikeda, H. Okamura, M. Ichinokawa and S. Nakayama. Methods in Ecology and Evolution, , 7, 2, (2016) 249-260.

[106] Binary classification with pseudo exponential model and its application for multi-task learning. T. Takenouchi, O. Komori and S. Eguchi. Entropy 17, 8, (2015) 5673-5694.

[105] Robust estimation of location and concentration parameters for the von Mises-Fisher distribution. S. Kato and S. Eguchi. Statistical Papers 57.1 (2016): 205-234.

[104] Generalized t-statistics for two-group classification. O. Komori, S. Eguchi and J. Copas. Biometrics, 71, 2 (2015) 404-416.

[103] Statistical and Machine-Learning Methods for Class Prediction in High Dimension. O. Komori and S. Eguchi. Chapter 14 in Design and Analysis of Clinical Trials for Predictive Medicine. Eds S. Matsui, M. Buyse, R. Simon. Chapman & Hall/CRC Biostatistics Series 2015.

[102] Maximum power entropy method for ecological data analysis. O. Komori, S. Eguchi. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MAXENT 2014) 1641, 337-344. AIP Publishing 2015.

[101] A novel boosting algorithm for multi-task learning based on the Itakuda-Saito divergence. T. Takenouchi, O. Komori, S. Eguchi. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MAXENT 2014) 1641, 230-237. AIP Publishing 2015.

[100] Duality in a maximum generalized entropy model. S. Eguchi, O. Komori, A. Ohara. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MAXENT 2014) 1641, 297-304. AIP Publishing 2015.

[99] Individualized prostate-specific antigen threshold values to avoid overdiagnosis of prostate cancer and reduce unnecessary biopsy in elderly men. Kanao K., Komori O., Nakashima J., Ohigashi T., Kikuchi E., Miyajima A., Nakagawa K., Eguchi S. and Oya M. (2014). Japanese Journal of Clinical Oncology, 44 (9) 852 - 859.

[98] Failure Prediction Method for Long Life Photoconductor Based on Statistical Machine Learning. Y. Nakazato, M. Imazeki, O. Komori, S. Eguchi. NIP & Digital Fabrication Conference 2014 (1), 375-378

[97] Duality of maximum entropy and minimum divergence. S. Eguchi, O. Komori and A. Ohara. Entropy 16, 7 (2014) 3552-3572.

[96] Spontaneous clustering via minimum gamma-divergence. A. Notsu, O. Komori and S. Eguchi. Neural Computation 26, 2 (2014) 421-448.

[95] Geometry on positive definite matrices deformed by V-potentials and its submanifold structure. Ohara, A. and Eguchi, S. In Geometric Theory of Information; Nielsen, F., Eds.; Springer: New York, NY, USA, 2014; Chapter 2, pp. 31-55.

[94] Group invariance of information geometry on q-Gaussian distributions induced by beta-divergence. A. Ohara and S. Eguchi. Entropy 15, 11 (2013) 4732-4747.

[93] Detection of heterogeneous structures on the Gaussian copula model using projective power entropy. A. Notsu, Y. Kawasaki and S. Eguchi. ISRN Probability and Statistics, Volume 2013 (2013), Article ID 787141, 10 pages.

[92] Multiple suboptimal solutions for prediction rules in gene expression data. O. Komori, M. Prichard and S. Eguchi. Computational and Mathematical Methods in Medicine 2013 (2013), 14 pages.

[91] Robust independent component analysis via minimum gamma-divergence estimation. P-W. Chen, H. Hung , O. Komori, S-Y. Huang and S. Eguchi. IEEE Journal of Selected Topics in Signal Processing, 7, 4 (2013) 614-624.

[90] Density estimation with minimization of U-divergence. K. Naito and S. Eguchi. Machine Learning, 90, (2013) 29-57.

[89] Editorial note on special issue "Statistical Analysis of Biomarkers for Personalized Medicine," by S. Eguchi, S. Matsui, S-Y. Huang and C. K. Hsiao. Computational and Mathematical Methods in Medicine. 2013, Article ID 467420, 2 pages.

[88] Geometry on Positive Definite Matrices Induced from V-Potential Function. A. Ohara and S. Eguchi. Geometric Science of Information. Lecture Notes in Computer Science 8085, 2013, 621-629.

[87] An extension of the Receiver Operating Characteristic curve and AUC-optimal classification. T. Takenouchi, O. Komori and S. Eguchi. Neural Computation 24, 10 (2012) 2789-2824.

[86] Boosting learning algorithm for pattern recognition and beyond. O. Komori and S. Eguchi. IEICE Transactions on Information and Systems, E94-D, 10 (2011) 1863-1869.

[85] Projective power entropy and maximum Tsallis entropy distributions. S. Eguchi, O. Komori and S. Kato. Entropy 13, 10 (2011) 1746-1764.

[84] Comment on `Riemann manifold Langevin and Hamiltonian Monte Carlo methods'. M. Girolami, B. Calderhead, J Royal Statistical Society B 73, 2 (2011) 123-214

[83] A boosting method for maximizing the partial area under the ROC curve. O. Komori and S. Eguchi. BMC Bioinformatics 11:314 (2010).

[82] Robust QTL analysis by minimum $beta$-divergence method. M. N. H. Mollah and S. Eguchi. International Journal of Data Mining and Bioinformatics, 4, 4 (2010) 471-485

[81] AUC maximization method in credit scoring. K. Miura, S. Yamashita and S. Eguchi. J. Risk Model Validation, 4, 2 (2010) 3-25.

[80] Entropy and divergence associated with power function and the statistical application. S. Eguchi and S. Kato. Entropy 12, 2 (2010) 262-274.

[79] Robust extraction of local structures by the minimum beta-divergence method. N. H. Mollah, N. Sultana, M. Minami and S. Eguchi. Neural Networks 23, 2 (2010) 226-238.

[78] Likelihood for statistically equivalent models. J. Copas and S. Eguchi. J. Royal Statistical Society B, 72, 2 (2010) 193-217.

[77] Maximum regularized likelihood estimator of finite mixtures with a structural model. S. Eguchi and K. Yoshioka. Communications in Statistics 39: 8 (2010) 1498-1510.

[76] Robust kernel principal component analysis. S-Y. Huang, Y-R. Yeh and S. Eguchi. Neural Computation, 21, 11 (2009) 3179-3213.

[75] SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip. H. Fujisawa, Y. Horiuchi, Y. Harushima, T. Takada, S. Eguchi, T. Mochizuki, T. Sakaguchi, T. Shiroishi, and N. Kurata. BMC Bioinformatics (2009) 10: 131.

[74] Extension of ROC curve. Takenouchi, T. and Eguchi, S. Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP, 1-9 (2009).

[73] On the bound of statistical inference for observational data. Invited Session: Statistical Inference for Observational Studies, International Association for Statistical Computing (IASC) 411-415, 2008.

[72] Robust hierarchical clustering for gene expression data analysis. M. N. H. Mollah, M. Pritchard, O. Komori and S. Eguchi. Communications of SIWN, 6 (2009) 118-122.

[71] Boosting method for local learning in statistical pattern recognition. M. Kawakita and S. Eguchi. Neural Computation, 20, 11 (2008) 2792-2838.

[70] Robust parameter estimation with a small bias against heavy contamination. H. Fujisawa and S. Eguchi. J. Multivariate Analysis, 99, 9 (2008) 2053-2081.

[69] Robust boosting algorithm against mislabeling in multi-class problems. T. Takenouchi, S. Eguchi, N. Murata and T. Kanamori. Neural Computation 20, 6 (2008) 1596-1630.

[68] Robust composite interval mapping for QTL analysis by minimumβ-divergence method. Md. N. H. Mollah and S. Eguchi. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 08), 115-120 (2008).

[67] Asymptotical improvement of maximum likelihood estimators on Kullback-Leibler loss. S. Eguchi and T. Yanagimoto. J. Statist. Plan. Infer. 138, 11 (2008) 3502-3511.

[66] Information divergence geometry and the application to statistical machine learning. S. Eguchi. Information Theory and Statistical Learning, chapter 13, 309-332. (2008) Eds. F. Emmert-Streib and M. Dehmer, Springer USA.

[65] Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancer. M. Ushijima, S. Miyata, S. Eguchi, M. Kawakita, M. Yoshimoto, T. Iwase, F. Akiyama, G. Sakamoto, K. Nagasaki, Y. Miki, T. Noda, Y. Hoshikawa and M. Matsuura. Cancer Informatics, 3 (2007) 285-293.

[64] Importance sampling via the estimated sampler. M. Henmi, R. Yoshida and S. Eguchi. Biometrika 94, 4 (2007) 985-991 .

[63] Identifying haplotype block structure by using ancestor-derived model. H. Fujisawa, M. Isomura, S. Eguchi, M. Ushijima, S. Miyata, Y. Miki, M. Matsuura. J. Human Genetics 52, 9 (2007) 738-746.

[62] Robust loss functions for boosting. T. Kanamori, T. Takenouchi, S. Eguchi and N. Murata. Neural Computation, 19, 8 (2007) 2183-2244.

[61] Confidence intervals and P-values for meta analysis with publication bias. M. Henmi, J. Copas and S. Eguchi. Biometrics, 63, 2 (2007) 475-482.

[60] Robust prewhitening for ICA by minimizing beta-divergence and its application to FastICA. M. N. H. Mollah, M. Minami and S. Eguchi. Neural Processing Letters, 25, 2 (2007) 91-110.

[59] GroupAdaBoost: accurate prediction and selection of important genes. T. Takenouchi, M. Ushijima and S.Eguchi. IPSJ Transactions on Bioinformatics 3 (2007) 1-8.

[58] Identification of biomarkers from mass spectrometry data using a "common" peak approach. T. Fushiki, H. Fujisawa and S. Eguchi. BMC Bioinformatics (2006) 7: 358.

[57] Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma. S. Eguchi and J. Copas. J. Multivariate Analysis, 97, 9 (2006) 2034-2040.

[56] Image classification based on Markov random field models with Jeffreys divergence. R. Nishii and S. Eguchi. J. Multivariate Analysis, 97, 9 (2006) 1997-2008.

[55] Supervised image classification of multispectral images based on statistical machine learning. Nishii, R. and Eguchi, S. "Signal and Image Processing for Remote Sensing", Edited by C.H. Chen, 346-370, 2006, Taylor and Francis Books.

[54] Exploring latent structure of mixture ICA models by the minimum beta-divergence method. M. N. H. Mollah, M. Minami and S. Eguchi. Neural Computation, 18, 1 (2006) 166-190.[53] Robust estimation in the normal mixture model. H. Fujisawa and S. Eguchi. J. Statist. Plan. Infer., 136, 11 (2006) 3989-4011.

[52] Information geometry and statistical pattern recognition. S. Eguchi. Sugaku Expositions, Amer. Math. Soc, 19 (2006) 197-216.

[51] Local likelihood density estimation when the bandwidth is large. B. U. Park, Y. K. Lee, T. Y. Kim, C. Park and S. Eguchi. J. Statist. Plan. Infer., 136, 3 (2006) 839-859.

[50] Spatio-temporal contextual image classification based on spatial AdaBoost. Nishii, R and Eguchi, S. IEEE International Geoscience & Remote sensing Symposium,(Vol. 1, pp. 4-pp) 2005.

[49] An introduction to the predictive technique AdaBoost with a comparison to generalized additive models. M. Kawakita, M. Minami, S. Eguchi and C. E. Lennert-Cody. Fisheries Research, 76, 3 (2005) 328-343

[48] Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods. R. Nishii and S. Eguchi. IEEE Tran. on Geoscience and Remote Sensing, 43, 11 (2005) 2547-2554.

[47] Local model uncertainty and incomplete data bias (with discussion). J. Copas and S. Eguchi. J. Royal Statistical Society B, 67, 4 (2005) 459-513.

[46] Modeling late entry bias in survival analysis. M. Matsuura and S. Eguchi. Biometrics, 61, 2 (2005) 559-566.

[45] GroupAdaBoost for selecting important genes. T. Takenouchi, M. Ushijima, S. Eguchi, Fifth IEEE Symposium on Bioinformatics and Bioengineering, 218-221, 2005.

[44] The most robust loss function for boosting. T. Kanamori, T. Takenouchi, S. Eguchi and N. Murata. Lecture Notes in Computer Science 3316, 496-501 (2004) Springer.

[43] Local Parametric Modeling via U-Divergence. S. Eguchi. Invited Program Meeting 22. Organizer: B. U. Park: Discussant: Irene Gijbels, 55th Session of International Statistical Institute at Sydney, April, 2005.

[42] Robust supervised image classifiers by spatial AdaBoost based on robust loss functions. Ryuei Nishii and Shinto Eguchi. Image and Signal Processing for Remote Sensing XI, edited by L. Bruzzone. Proceedings of SPIE, 59820D-59820D-8, 2005.

[41] Local likelihood regression of acoustic logging data with adaptive selection of multiple bandwidth. S. Watanabe, M. Minami and S. Eguchi. Geophisical Explanation, 57, 5 (2004) 535-544.

[40] A paradox concerning nuisance parameters and projected estimating functions. M. Henmi and S. Eguchi. Biometrika, 91, 4 (2004) 929-941.

[39] Robust principal component analysis with adaptive selection for tuning parameters. I. Higuchi and S. Eguchi. J. Machine Learning Research, 5 (2004) 453-471.

[38] Information geometry of U-Boost and Bregman divergence. N. Murata, T. Takenouchi, T. Kanamori and S. Eguchi. Neural Computation, 16, 7 (2004) 1437-1481.

[37] Genotyping of single nucleotide polymorphism using model-based clustering. H. Fujisawa, S. Eguchi, M. Ushijima, S. Miyata, Y. Miki, T. Muto and M. Matsuura. Bioinformatics, 20, 5 (2004) 718-726.

[36] Supervised image classification based on AdaBoost with contextual weak classifiers. R. Nshii and S. Eguchi. Proc. of 2004 IEEE International Geoscience and Remote Sensing Symposium, II, 1467-1470, Anchorage.

[35] Robustifying AdaBoost by adding the naive error rate. T. Takenouchi and S. Eguchi. Neural Computation, 16, 4 (2004) 767-787.

[34] Local likelihood method: a bridge over parametric and nonparametric regression. S. Eguchi, T-Y. Kim and B. U. Park. J. Nonparametric Statistics, 15, 6 (2003) 665-683.

[33] Adaptive selection for minimum beta-divergence method. M. Minami and S. Eguchi. Fourth International Symposium on Independent Component Analysis and Blind Signal Separation, (2003) 475-480.

[32] Robust blind source separation by beta-divergence. M. Minami and S. Eguchi, Neural Computation, 14, 8 (2002) 1859-1886.

[31] A class of logistic-type discriminant functions. S. Eguchi and J. Copas, Biometrika, 89, 1 (2002) 1-22.

[30] Local sensitivity approximation for selectivity bias. J. Copas and S. Eguchi, J. Royal Statistical Society B, 63, 4 (2001) 871-895.

[29] A class of robust principal component vectors. H. Kamiya and S. Eguchi, J. Multivariate Analysis, 77, 2 (2001) 239-269.

[28] Recent developments in discriminant analysis from an information geometric point of view. J. Korean Statist. Soc. 30 (2001) 247-264. S. Eguchi and J. Copas.

[27] A comparison of methods for estimating individual pharmacokinetic parameters. T. Amisaki and S. Eguchi, J. Pharmacokinetics and Biopharmaceutics, 27, 1 (1999) 103-121.

[26] A class of local likelihood methods and near-parametric asymptotics.  S. Eguchi and J. Copas, J. Royal Statistical Society B, 60, 4 (1998) 709-724.

[25] The influence function of principal component analysis by self-organizing rule.  I. Higuchi and S. Eguchi, Neural Computation, 10, 6 (1998) 1435-1444.

[24] Sensitivity Approximations for Selectivity Bias in Observational Data Analysis. J. B. Copas and Eguchi, S. Technical Report 312 (1997), Department of Statistics, University of Warwick.

[23] Pharmacokinetic parameter estimations by minimum relative entropy method. T. Amisaki and S. Eguchi, J. Pharmacokinetics and Biopharmaceutics, 23, 5 (1995) 479-494.

[22] Improvement on the relative entropy risk of the MLE by gradient. S. Eguchi. Metrika 42, 235-237, 1995.

[21] Recent developments of the theory of statistical inference, Kubokawa, T., Eguchi, S., Takemura, A. and Konishi, S. Japan J. Statist. Soc. 22, (1993), 275-312

[20] Further discussion of second order efficiency for estimation. S. Eguchi. Questio 17 (1993) 347-364.

[19] Statistical inference from observations with censoring and grouping for exponential families. S. Eguchi. In Statistical Sciences and Data Analysis, eds. K. Matusita et al. (1993) 291-299. VSP, Utrecht.

[18] Geometry of minimum contrast. S. Eguchi. Hiroshima Math. J., 22, 3 (1992) 631-647.

[17] Inverse problem in kinetic model of petrole generation, Eguchi, S., Amisaki, T. and Suzuki, N. Mem. Fac. Sci, Shimane University 26 (1992), 29-38.

[16] The projection method for accelerated life test model in bivariate exponential distributions. S. Eguchi. Hiroshima Math. J., 22, 1 (1992) 185-193.

[15] A geometric look at nuisance parameter effect of local powers in testing hypothesis. S. Eguchi. Ann. Inst. Statist. Math., 43, 2 (1991) 245-260.

[14] A modification of the Newton method from a viewpoint of statistical testing methods, Morita, T. and Eguchi, S. Mem. Fac. Sci, Shimane University 25 (1991), 15-19.

[13] Testing the Hardy-Weinberg equilibrium in the HLA system. S. Eguchi and M. Matsuura, Biometrics, 46, 2 (1990) 415-426.

[12] Estimation of gene frequency and test for Hardy-Weinberg equilibrium in the HLA system. M. Matsuura and S. Eguchi, Environmental Health Perspectives, 87 (1990) 149-155.[11] A class of tests for general covariance structure. H. Wakaki, S. Eguchi and Y. Fujikoshi. J. Multivariate Analysis, 32, 2 (1990) 313-325.

[10] Minimum contrast statistic with Bartlett factor. S. Eguchi. Hiroshima Statistical Research Group Technical Report 266 (1990).

[9] A unified approach to improper solutions of maximum likelihood estimates. S. Eguchi. J. Japan Statist. Soc. 19 (1989) 67-82.

[8] Contrast from one probability measure to another. S. Eguchi. RIMS Koukuroku 623 (1987) 79-86.

[7] Geomeric view of asymptotic distributions of test statistics. S. Eguchi. Proceeding of Third Pacific Area Statistical Conference (1986), 47-51.

[6] A projection method of estimation for a subfamily of exponential families. S. Eguchi. Ann. Inst. Statist. Math. 38 A, 1 (1986) 385-398.

[5] A differential geometric approach to statistical inference on the basis of contrast functionals. S. Eguchi. Hiroshima Math. J., 15, 2 (1985) 341-391.

[4] A characterization of second order efficiency in a curved exponential family. S. Eguchi. Ann. Inst. Statist. Math., 36 A, 1 (1984) 199-206.

[3] Model-fidelity of maximum likelihood estimator in a curved exponential family. S. Eguchi. Statistical Theory and Data Analysis, ed. K. Matusita (1985), 207-223, North Holland.

[2] Characterization of second order efficiency for estimators. S. Eguchi. RIMS Koukyuroku 507 (1983), 1-15.

[1] Second order efficiency of minimum contrast estimators in a curved exponential family. S. Eguchi. Ann. Statist., 11, 3 (1983) 793-803.