hirokazu yanagihara

Last Updated :2024/02/01

Affiliations, Positions
Graduate School of Advanced Science and Engineering, Professor
E-mail
yanagi-hirohiroshima-u.ac.jp

Basic Information

Major Professional Backgrounds

  • 2003/04/01, 2006/06/30, University of Tsukuba, Department of Social System and Management, Graduate School of Systems and Information Engineering, Assistant Professor
  • 2001/04/01, 2003/03/31, The Institute of Statistical Mathematics, Department of Statistical Methodology, Division of Multidimensional Analysis, Assistant Professor

Educational Backgrounds

  • Hiroshima University, Graduate School of Science, Department of Mathematics, Japan, 1998/04, 2001/03

Academic Degrees

  • Doctor of Science, Hiroshima University

Research Fields

  • Informatics;Principles of Informatics;Statistical science

Research Keywords

  • Bias correction
  • Nonnormality
  • Sample Distribution

Affiliated Academic Societies

  • The International Biometric Society, 2003/04
  • The International Psychometric Society, 2004/04
  • Japanese Society of Applied Statistics, 2006/04
  • Mathematical Society of Japan, 2006/07
  • Japan Statistical Society, 1997/04
  • The Behaviormetric Society of Japan, 2004/04
  • The Biometric Society of Japan, 2001/04
  • Statistical Society of Canada, 2010/04

Educational Activity

Course in Charge

  1. 2023, Liberal Arts Education Program1, 4Term, Fundamental Data Science
  2. 2023, Graduate Education (Doctoral Program) , 4Term, Pattern Recognition and Machine Learning
  3. 2023, Undergraduate Education, First Semester, Special Study of Mathematics and Informatics for Graduation
  4. 2023, Undergraduate Education, Second Semester, Special Study of Mathematics and Informatics for Graduation
  5. 2023, Undergraduate Education, 4Term, Data Science
  6. 2023, Liberal Arts Education Program1, 1Term, Introductory Seminar for First-Year Students
  7. 2023, Undergraduate Education, 1Term, Inferential Statistics
  8. 2023, Undergraduate Education, 1Term, Data Science Seminar I
  9. 2023, Undergraduate Education, 2Term, Data Science Seminar II
  10. 2023, Undergraduate Education, Second Semester, Graduation Thesis
  11. 2023, Graduate Education (Master's Program) , 2Term, Mathematical Omnibus
  12. 2023, Graduate Education (Master's Program) , Academic Year, Mathematical Statistics Seminar
  13. 2023, Graduate Education (Master's Program) , Academic Year, Mathematical Statistics Seminar
  14. 2023, Graduate Education (Master's Program) , 1Term, Probability and Mathematical Statistics D
  15. 2023, Graduate Education (Master's Program) , 4Term, Topics in Probability and Mathematical Statistics D
  16. 2023, Graduate Education (Master's Program) , Academic Year, Exercises in Mathematics
  17. 2023, Graduate Education (Master's Program) , First Semester, Exercises in Mathematics A
  18. 2023, Graduate Education (Master's Program) , Second Semester, Exercises in Mathematics B
  19. 2023, Graduate Education (Master's Program) , Academic Year, Seminar in Mathematics
  20. 2023, Graduate Education (Doctoral Program) , Academic Year, Seminar in Mathematics

Research Activities

Academic Papers

  1. SAS/IML Program for Computing Probabilities Related to Maximum Contrast Methods, Japanese Journal of Biometrics, 24(2), 57-70, 20040312
  2. On Avoidance of the Over-fitting in the B-Spline Non-parametric Regression Model, Ouyou toukeigaku, 33(1), 51-69, 20040825
  3. A Bias-Corrected C_p Criterion for Optimizing Ridge Parameters in Multivariate Generalized Ridge Regression, Ouyou toukeigaku, 38(3), 151-172, 20091225
  4. Coordinate descent algorithm of generalized fused Lasso logistic regression for multivariate trend filtering, Japanese Journal of Statistics and Data Science, 5(2), 535-551, 202212
  5. Bias-corrected AIC for selecting variables in multinomial logistic regression models, Linear Algebra and Its Applications, 436(11), 4329-4341, 201206
  6. Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods, Hiroshima Mathematical Journal, 42(3), 301-324, 201211
  7. Bias-corrected AIC for selecting variables in Poisson regression models, Communications in Statistics Theory and Methods, 42(11), 1911-1921, 201306
  8. Adjustment on an asymptotic expansion of the distribution function with chi-squared approximation, Hiroshima Mathematical Journal, 33(1), 15-25, 200303
  9. Bridging the gap between B-spline and polynomial regression model, Communications in Statistics Simulation and Computation, 32(1), 179-190, 200304
  10. Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models, Annals of the Institute of Statistical Mathematics, 55(3), 537-553, 200304
  11. A family of regression models having partially additive and multiplicative covariate structure, Bulletin of Informatics and Cybernetics, 37, 49-64, 200504
  12. The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance structures under normal assumption, Journal of Multivariate Analysis, 96(2), 237-264, 200510
  13. ★, Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition, Journal of Multivariate Analysis, 97(9), 1965-1975, 200610
  14. Asymptotic expansions of the null distributions of test statistics for multivariate linear hypothesis under nonnormality, Hiroshima Mathematical Journal, 32(1), 17-50, 200203
  15. A mathematical estimation of true cancer incidence using data from population-based cancer registries, Japan Journal of Clinical Oncology, 37(2), 150-155, 200701
  16. ★, A family of estimators for multivariate kurtosis in a nonnormal linear regression model, Journal of Multivariate Analysis, 98(1), 1-29, 200701
  17. ★, Conditions for robustness to nonnormality on test statistics in a GMANOVA model, Journal of the Japan Statistical Society, 37(1), 261-281, 20070601
  18. A class of population covariance matrices in the bootstrap approach to covariance structure analysis, Multivariate Behavioral Research, 42(2), 261-281, 20070901
  19. ★, Corrected version of AIC for selecting mulrivariate normal linear regression models in a general nonnormal case, Journal of Multivariate Analysis, 97(9), 1965-1975, 20060501
  20. Bias corrections of some criteria for selecting multivariate linear models in a general nonnormal case., American Journal of Mathematical and Management Sciences, 25(1), 221-268, 20050501
  21. Probability estimation of snow damage on sugi (Cryptomeria japonica) forest stands by logistic regression model in Toyama prefecture, Japan., Journal of Forest Science, 24(3), 137-142, 20081201
  22. Economic analysis of snow damage on sugi (Cryptomeria japonica) forest stands in Japan within the forest stand optimization framework, Journal of Forest Science, 24(3), 143-149, 20081201
  23. Second-order bias-corrected AIC in multivariate normal linear models under nonnormality, The Canadian Journal of Statistics, 39(1), 126-146, 20110201
  24. Variable selection in multivariate linear regression models with fewer observations than the dimension, Japanese Journal of Applied Statistics, 39(1), 1-19, 20100401
  25. Ordering municipalities by medical cost efficiency under the Japanese national health insurance system using the stochastic cost frontier model, American Journal of Mathematical and Management Sciences, 29(3-4), 371-392, 20091201
  26. Asymptotic expansion of the null distribution of one-way ANOVA test statistic for heteroscedastic case under nonnormality, Communications in Statistics Theory and Methods, 29(2), 463-476, 2000
  27. Bias correction of AIC in logistic regression models, Journal of Statistical Planning and Inference, 115(2), 349-360, 20030801
  28. Testing the equality of several covariance matrices with fewer observations than the dimension, Journal of Multivariate Analysis, 101(6), 1319-1329, 201007
  29. Improvement of the quality of the chi-square approximation for the ADF test on a covariance matrix with a linear structure, Journal of Statistical Planning and Inference, 141(4), 1535-1542, 201104
  30. A non-iterative optimization method for smoothness in penalized spline regression, Statistics and Computing, 22(2), 527-544, 201203
  31. Iterative bias correction of the cross-validation criterion, Scandinavian Journal of Statistics, 39(1), 116-130, 201203
  32. A class of cross-validatory model selection criteria, Hiroshima Mathematical Journal, 43(2), 149-177, 201307
  33. Simple formula for calculating bias-corrected AIC in generalized linear models, Scandinavian Journal of Statistics, 41(2), 535-555, 201406
  34. Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification, Linear Algebra and Its Applications, 455, 82-106, 201408
  35. Selecting a shrinkage parameter in structural equation modeling with a near singular covariance matrix by the GIC minimization method, Hiroshima Mathematical Journal, 44(3), 315-326, 201411
  36. Consistency of high-dimensional AIC-type and Cp-type criteria in multivariate linear regression, Journal of Multivariate Analysis, 123, 184-200, 201401
  37. Empirical correction to the likelihood ratio statistic for structural equation modeling with many variables, Psychometrika, 80(2), 379-405, 201506
  38. A consistency property of the AIC for multivariate linear models when the dimension and the sample size are large, The Electronic Journal of Statistics, 9(1), 869-897, 2015
  39. Temporal and geographical variation in body condition of common minke whales (Balaenoptera acutorostrata acutorostrata) in the Northeast Atlantic, Polar Biology, 40(3), 667-683, 201703
  40. High-dimensional asymptotic behavior of the difference between the log-determinants of two Wishart matrices, Journal of Multivariate Analysis, 157, 70-86, 201705
  41. A high-dimensionality-adjusted consistent Cp-type statistic for selecting variables in a normality-assumed linear regression with multiple responses, Procedia Computer Science, 96, 1096-1105, 2016
  42. Canonical correlation analysis for geographical and chronological responses, Procedia Computer Science, 96(96), 1351-1360, 2016
  43. A study on the bias-correction effect of the AIC for selecting variables in normal multivariate linear regression models under model misspecification, REVSTAT Statistical Journal, 15(3), 299-332, 201707
  44. Illustration of the varying coefficient model for a tree growth analysis from the age and space perspectives, FORMATH, 15, 1-9, 2016
  45. Explicit solution to the minimization problem of generalized cross-validation criterion for selecting ridge parameters in generalized ridge regression, Hiroshima Mathematical Journal, 48(2), 203-222, 201807
  46. Asymptotic null and non-null distributions of test statistics for redundancy in high-dimensional canonical correlation analysis, Random Matrices-Theory and Applications, 8(1), 201901
  47. Asymptotic approximations of the null distribution of the one-way ANOVA test statistic under nonnormality, Journal of the Japan Statistical Society, 29(2), 147-161, 1999
  48. Asymptotic expansions of the null distributions of three test statistics in a nonnormal GMANOVA model, Hiroshima Mathematical Journal, 31(2), 213-262, 2001
  49. A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion, Journal of Statistical Planning and Inference, 204, 187-205, 202001
  50. A consistent variable selection method in high-dimensional canonical discriminant analysis, Journal of Multivariate Analysis, 175, 202001
  51. A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables, Electronic Journal of Statistics, 14(1), 1386-1412, 2020
  52. Equivalence between adaptive Lasso and generalized ridge estimators in linear regression with orthogonal explanatory variables after optimizing regularization parameters, Annals of the Institute of Statistical Mathematics, 72(6), 1501-1516, 202012
  53. Strong consistency of log-likelihood-based information criterion in high-dimensional canonical correlation analysis, Sankhya Series A Mathematical Statistics and Probability, 83(1), 109-127, 202102

Invited Lecture, Oral Presentation, Poster Presentation

  1. A high-dimensionality-adjusted consistent Cp-type statistic for selecting variables in a normality-assumed linear regression with multiple responses, Hirokazu Yanagihara, 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 2016/09/06, With Invitation, English
  2. What is statistics?, Hirokazu Yanagihara, 2016/10/21, With Invitation, Japanese