Welcome to my home page!!
My name is Hirokazu Yanagihara.

Last Modified: March 6, 2024
(since April 17, 2001).
Sorry. Japanese only.

統計相談再開しました.


(2019.6.2.撮影)
  蛹エ 宏和(やなぎはら ひろかず)

広島大学 大学院先進理工系科学研究科
数学プログラム 教授
情報科学部 担当



おいたち

1972.8 福岡県福岡市に生まれる
1991.3 山口県立山口高等学校卒業
1996.3 広島大学学校教育学部小学校教員養成課程卒業
1998.3 広島大学大学院理学研究科数学専攻博士課程前期修了
2001.3 広島大学大学院理学研究科数学専攻博士課程後期修了
博士 (理学) を取得
2001.4 統計数理研究所 調査実験解析研究系 助手
2003.4 筑波大学 社会工学系 講師
2004.4 筑波大学 大学院システム情報工学研究科 講師
2006.7 広島大学 大学院理学研究科 助教授
2007.4 広島大学 大学院理学研究科 准教授
2017.3 広島大学 大学院理学研究科 教授
2020.4〜 広島大学 大学院先進理工系科学研究科 教授

ひじょうきん, そのた

2002.4〜2003.2 千葉大学 法経学部 非常勤講師
2003.2〜2003.3 Visiting Assistant Professor, Laboratory for
Social Research, University of Notre Dame (USA)
2004.3〜2004.5 Visiting Assistant Professor, Laboratory for
Social Research, University of Notre Dame (USA)
2007.6〜2020.9 (株) 東京カンテイ 技術指導員
2010.3〜2010.10 Guest Researcher, Department of Statistics,
University of Toronto (Canada)
2013.9 大阪府立大学 大学院工学研究科 非常勤講師
2014.4 〜2017.3 広島大学 統計科学研究拠点 統計理論グループ メンバー
2015.10〜2018.10 広島大学 優秀若手研究者 (Distinguished Researcher)
2020.4 〜2024.3 放射線影響研究所 統計部 専門委員
2020.9 〜2024.3 統計数理研究所 リスク解析戦略研究センター 客員教授
2020.10〜2024.3 広島大学 AI・データイノベーション教育研究センター
データ解析部門 部門長



けんきゅう

1. 漸近理論とその応用について
2. 非正規分布にもとづく検定統計量の分布の近似とその改良について
3. ノンパラメトリック回帰モデルを用いた平滑化について
4. モデル選択問題における情報量規準の特性と改良について
5. 回帰モデルを用いたマンション価格分析について

Key Words:

Asymptotic expansion; Bias correction; Central limiting theory; Covariance structural model; Cross validation; Edgeworth expansion; Generalized linear model; GMANOVA model; Growth curve model; High-dimensional asymptotic theory; Improving transformation; Information criterion; Model selection; Multivariate analysis; Non-normality; Non-parametric regression model; Regression analysis; Sample dsitribution.

公表論文
---査読付き論文---
[1] Fujikoshi, Y., Ohmae, M. & Yanagihara, H. (1999).
Asymptotic approximations of the null distribution of the one-way ANOVA test statistic under nonnormality.
J. Japan Statist. Soc., Vol. 29 (2), 147-161.
DOI:10.14490/jjss1995.29.147
[2] Yanagihara, H. (2000).
Asymptotic expansion of the null distribution of one-way ANOVA test statistic for heteroscedastic case
under nonnormality.
Comm. Statist. Theory Methods, Vol. 29 (2), 463-476.
DOI:10.1080/03610920008832495
[3] Yanagihara, H. (2001).
Asymptotic expansions of the null distributions of three test statistics in a nonnormal GMANOVA model.
Hiroshima Math. J., Vol. 31 (2), 213-262.
DOI:10.32917/hmj/1151105700
[4] Wakaki, H., Yanagihara, H. & Fujikoshi, Y. (2002).
Asymptotic expansions of the null distributions of test statistics for multivariate linear hypothesis
under nonnormality.
Hiroshima Math. J., Vol. 32 (1), 17-50.
DOI:10.32917/hmj/1151007641
[5] Yanagihara, H. (2003).
Asymptotic expansion of the null distribution of test statistic for linear hypothesis in nonnormal linear model.
J. Multivariate Anal., Vol. 84 (2), 222-246.
DOI:10.1016/S0047-259X(02)00049-0
[6] Yanagihara, H. & Tonda, T. (2003).
Adjustment on an asymptotic expansion of the distribution function with χ2-approximation.
Hiroshima Math. J., Vol. 33 (1), 15-25.
DOI:10.32917/hmj/1150997864
[7] Satoh, K., Yanagihara, H. & Ohtaki, M. (2003).
Bridging the gap between B-spline & polynomial regression model.
Comm. Statist. Simulation Comput., Vol. 32 (1), 179-190.
DOI:10.1081/SAC-120013120
[8] Yanagihara, H., Sekiguchi, R. & Fujikoshi, Y. (2003).
Bias correction of AIC in logistic regression models.
J. Statist. Plann. Inference, Vol. 115 (2), 349-360.
(This paper was in the top 5 of the most downloaded articles January - December 2003)
(This paper was in the top 17 of the most downloaded articles January - August 2004)
(This paper was in the top 1 of the most requested articles April 2002 - April 2004)
DOI:10.1016/S0378-3758(02)00167-2
[9] 蛹エ宏和・吉本 敦 (2003).
一般化多変量分散分析モデルの林木直径成長分析への適用可能性.
統計数理 (特集: 森林資源統計学), Vol. 51 (1), 19-35.
DOI:10.4005/jjfs.87.504
[10] Yanagihara, H. & Ohtaki, M. (2003).
Knot-placement to avoid over fitting in B-spline scedastic smoothing.
Comm. Statist. Simulation Comput., Vol. 32 (3), 771-785.
DOI:10.1081/SAC-120017861
[11] Fujikoshi, Y., Noguchi, T., Ohtaki, M. & Yanagihara, H. (2003).
Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models.
Ann. Inst. Statist. Math., Vol. 55 (3), 537-553.
DOI:10.1007/BF02517806
[12] 西山 智・蛹エ宏和・吉村 功 (2004).
最大対比法を活用するためのSAS/IMLプログラム.
計量生物学, Vol. 24 (2), 57-70.
DOI:10.5691/jjb.24.57
[13] Yanagihara, H., Matsumoto, C. & Tonda, T. (2004).
Asymptotic expansion of the null distribution of the modified normal likelihood ratio criterion for testing
Σ=Σ0 under nonnormality.
Hiroshima Math. J., Vol. 34 (1), 81-100.
DOI:10.32917/hmj/1150998072
[14] 蛹エ宏和・大瀧 慈 (2004).
B-スプラインノンパラメトリック回帰モデルにおける過剰適合の回避について.
応用統計学 (特集号: ノンパラメトリック統計モデルとその応用), Vol. 33 (1), 51-69.
DOI:10.5023/jappstat.33.51
[15] Satoh, K., Yanagihara, H. & Ohtaki, M. (2004).
Clustering method by connected neighborhoods & its application.
Adv. Appl. Stat., Vol. 4 (2), 223-231.
[16] 蛹エ宏和 (2005).
非正規データにおける情報量規準を用いた共分散構造モデルの選択問題
—「日本人の国民性調査」データへの適用 —.
統計数理 (特集号: 日本人の国民性調査 50 年), Vol. 53 (1), 133-157.
[17] Yanagihara, H. & Ohmoto, C. (2005).
On distribution of AIC in linear regression models.
J. Statist. Plann. Inference, Vol 133 (2), 417-433.
(This paper was in the top 23 of the most downloaded articles April - June 2005)
DOI:10.1016/j.jspi.2004.03.016
[18] 能本美穂・吉本 敦・蛹エ宏和 (2005).
木材生産を通した炭素の収支分析 — 福岡県八女地域を事例として —.
日本森林学会誌, Vol. 87 (4), 313-322.
DOI:10.4005/jjfs.87.313
[19] Yanagihara, H., Tonda, T. & Matsumoto, C. (2005).
The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance
structures under normal assumption.
J. Multivariate Anal., Vol. 96 (2), 237-264.
Erratum, (2008), Vol. 99 (2), 309-310.
(This paper was in the top 20 of the most downloaded articles October - December 2005)
DOI:10.1016/j.jmva.2004.10.014 (Original) DOI:10.1016/j.jmva.2005.10.008 (Erratum)
[20] Yanagihara, H. & Yuan, K.-H. (2005).
Four improved statistics for contrasting means by correcting skewness and kurtosis.
British J. Math. Statist. Psych., Vol. 58 (2), 209-237.
DOI:10.1348/000711005X64060
[21] Yanagihara, H. & Yuan, K.-H. (2005).
Three approximate solutions to the multivariate Behrens-Fisher problem.
Comm. Statist. Simulation Comput., Vol. 34 (4), 975-988.
DOI:10.1080/03610910500308396
[22] Yanagihara, H. & Ohtaki, M. (2005).
A family of regression models having partially additive and multiplicative covariate structure.
Bull. Inform. Cybernet. “Special Issue in Honor of Professor Takashi Yanagawa Part II”, Vol. 37, 49-64.
DOI:10.5109/12591
[23] 吉本 敦・蛹エ宏和・二宮嘉行 (2005).
多変量線形モデルによる林分成長要因探索のための変数選択.
日本森林学会誌 (特集号: 森林計画最前線 — 緑豊かな森林の育成を目指した最新研究 —), Vol. 87 (6), 504-512.
DOI:10.4005/jjfs.87.504
[24] Fujikoshi, Y., Yanagihara, H., & Wakaki, H. (2005).
Bias corrections of some criteria for selecting multivariate linear models in a general nonnormal case.
Amer. J. Math. Management Sci. “25th Anniversary of the Thomas L. Saaty and Jacob Wolfowitz Prizes:
New Advances and Applications by Prize Winners II”, Vol. 25 (3-4), 221-258.
(This paper was selected as 2006 Jacob Wolfowitz Prize)
DOI:10.1080/01966324.2005.10737651
[25] Yanagihara, H. (2006).
Corrected version of AIC for selecting multivariate normal linear regression models in a general nonnormal case.
J. Multivariate Anal., Vol. 97 (5), 1070-1089.
(This paper was in the top 3 of the most downloaded articles April - June 2006)
DOI:10.1016/j.jmva.2005.06.005
[27] Yanagihara, H., Tonda, T. & Matsumoto, C. (2006).
Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition.
J. Multivariate Anal. “Special issue dedicated to Professor Yasunori Fujikoshi”, Vol. 97 (9), 1965-1975.
(This paper was in the top 15 of the most downloaded articles July - September 2006)
DOI:10.1016/j.jmva.2005.10.009
[26] Yanagihara, H. (2007).
A family of estimators for multivariate kurtosis in a nonnormal linear regression model.
J. Multivariate Anal. , Vol. 98 (1), 1-29.
(This paper was in the top 8 of the most downloaded articles October - December 2006)
DOI:10.1016/j.jmva.2005.05.015
[28] Kamo, K., Kaneko, S., Satoh, K., Yanagihara, H., Mizuno, S. & Sobue, T. (2007).
A mathematical estimation of true cancer incidence using data from population-based cancer registries.
Jpn. J. Clin. Oncol., Vol. 37 (2), 150-155.
DOI:10.1093/jjco/hyl143
[29] Yanagihara, H. (2007).
Conditions for robustness to nonnormality of test statistics in a GMANOVA model.
J. Japan Statist. Soc., Vol. 37 (1), 135-155.
DOI:10.14490/jjss.37.135
[30] Yuan, K.-H., Hayashi, K. & Yanagihara, H. (2007).
A class of population covariance matrices in the bootstrap approach to covariance structure analysis.
Multivariate Behav. Res., Vol. 42 (2), 261-281.
DOI:10.1080/00273170701360662
[31] 山村麻理子・蛹エ宏和 (2007).
「国民生活基礎調査」データにもとづく居宅介護サービス利用に関する多変量プロビット分析.
統計数理, Vol. 55 (1), 125-142.
[32] Kamo, K., Yanagihara, H., Kato, A. & Yoshimoto, A. (2008).
Probability estimation of snow damage on sugi (Cryptomeria japonica) forest stands by logistic regression
model in Toyama prefecture, Japan.
J. Forest Sci., Vol. 24 (3), 137-142.
[33] Yoshimoto, A., Kato, A. & Yanagihara, H. (2008).
Economic analysis of snow damage on sugi (Cryptomeria japonica) forest stands in Japan within the forest
stand optimization framework.
J. Forest Sci., Vol. 24 (3), 143-149.
[34] 佐藤健一・蛹エ宏和・加茂憲一 (2009).
離散分布の経時測定データにおける線形な変化係数の推測について.
応用統計学, Vol. 38 (1), 19-28.
(本論文により2010年度応用統計学会優秀論文章を受章)
DOI:10.5023/jappstat.38.19
[35] 蛹エ宏和・永井 勇・佐藤健一 (2009).
多変量一般化リッジ回帰におけるリッジパラメータ最適化のためのバイアス補正 Cp 規準.
応用統計学, Vol. 38 (3), 151-172.
DOI:10.5023/jappstat.38.151
[36] Yamamura, M., & Yanagihara, H. (2009).
Ordering municipalities by medical cost efficiency under the Japanese national health insurance system
using the stochastic cost frontier model.
Amer. J. Math. Management Sci., Vol. 29 (3-4), 371-392.
DOI:10.1080/01966324.2009.10737764
[37] Yanagihara, H. & Satoh, K. (2010).
An unbiased Cp criterion for multivariate ridge regression.
J. Multivariate Anal., Vol. 101 (5), 1226-1238.
DOI:10.1016/j.jmva.2009.09.017
[38] Srivastava, M. S. & Yanagihara, H. (2010).
Testing the equality of several covariance matrices with fewer observations than the dimension.
J. Multivariate Anal., Vol. 101 (6), 1319-1219.
DOI:10.1016/j.jmva.2009.12.010
[39] Yamamura, M., Yanagihara, H. & Srivastava, M. S. (2010).
Variable selection in multivariate linear regression models with fewer observations than the dimension.
Japanese J. Appl. Statist., Vol. 39 (1), 1-19.
DOI:10.5023/jappstat.39.1
[40] Satoh, K., Yanagihara, H. & Kamo, K. (2010).
A robust estimation method for a growth curve model with balanced design.
J. Stat., Adv. Theory Appl., Vol. 3 (2), 113-124.
[41] 吉本 敦・木島真志・蛹エ宏和 (2010).
隣接空間制約による最適林分団地化パターンの探求.
統計数理, Vol. 58 (1), 113-126.
[42] Yanagihara, H., Himeno, T. & Yuan, K.-H. (2010).
GLS discrepancy based information criteria for selecting covariance structure models.
Behaviormetrika, Vol. 37 (2), 71-86.
DOI:10.2333/bhmk.37.71
[43] 冨田哲治・佐藤健一・蛹エ宏和 (2010).
空間データに対する交互作用モデルを用いた変化係数曲面の推測について.
応用統計学, Vol. 39 (2-3), 59-70.
DOI:10.5023/jappstat.39.59
[44] Satoh, K. & Yanagihara, H. (2010).
Estimation of varying coefficients for a growth curve model.
Amer. J. Math. Management Sci., Vol. 30 (3-4), 243-256.
DOI:10.1080/01966324.2010.10737787
[45] Matsumoto, C., Yanagihara, H. & Wakaki, H. (2011).
Improvement of the quality of the chi-square approximation for the ADF test on a covariance matrix
with a linear structure.
J. Statist. Plann. Inference, Vol. 141 (4), 1535-1542 .
DOI:10.1016/j.jspi.2010.11.012
[46] Yanagihara, H., Kamo, K. & Tonda, T. (2011).
Second-order bias-corrected AIC in multivariate normal linear models under nonnormality.
Canad. J. Statist., Vol. 39 (1), 126-146.
DOI:10.1002/cjs.10090
[47] Yanagihara, H. (2012).
A non-iterative optimization method for smoothness in penalized spline regression.
Stat. Comput., Vol. 22 (2), 527-544.
DOI:10.1007/s11222-011-9245-0
[48] Yanagihara, H. & Fujisawa, H. (2012).
Iterative bias correction of the cross-validation criterion.
Scand. J. Stat., Vol. 39 (1), 116-130.
DOI:10.1111/j.1467-9469.2011.00754.x
[49] Yanagihara, H., Kamo, K., Imori, S. & Satoh, K. (2012).
Bias-corrected AIC for selecting variables in multinomial logistic regression models.
Linear Algebra Appl., Vol. 436 (11), 4329-4341.
DOI:10.1016/j.laa.2012.01.018
[50] Nagai, I., Yanagihara, H. & Satoh, K. (2012).
Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods.
Hiroshima Math. J., Vol. 42 (3), 301-324.
DOI:10.32917/hmj/1355238371
[51] Kamo, K., Yanagihara, H. & Satoh, K. (2013).
Bias-corrected AIC for selecting variables in Poisson regression models.
Comm. Statist. Theory Methods, Vol. 42 (11), 1911-1921.
DOI:10.1080/03610926.2011.600504
[52] Yanagihara, H., Yuan, K.-H., Fujisawa, H. & Hayashi, K. (2013).
A class of model selection criteria based on cross-validation method.
Hiroshima Math. J., Vol. 43 (2), 149-177.
DOI:10.32917/hmj/1372180510
[53] Nagai, I., Fukui, K. & Yanagihara, H. (2013).
Choosing the number of repetitions in the multiple plug-in optimization method for the ridge parameters
in multivariate generalized ridge regression.
Bull. Inform. Cybernet., Vol. 45, 25-35.
DOI:10.5109/1563529
[54] Fujikoshi, Y., Sakurai, T. & Yanagihara, H. (2014).
Consistency of high-dimensional AIC-type and Cp-type criteria in multivariate linear regression.
J. Multivariate Anal., Vol. 123, 184-200.
DOI:10.1016/j.jmva.2013.09.006
[55] Imori, S., Yanagihara, H. & Wakaki, H. (2014).
Simple formula for calculating bias-corrected AIC in generalized linear models.
Scand. J. Stat., Vol. 41 (2), 535-555.
DOI:10.1111/sjos.12049
[56] Hashiyama, Y., Yanagihara, H. & Fujikoshi, Y. (2014).
Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under
model misspecification.
Linear Algebra Appl., Vol. 455, 82-106.
DOI:10.1016/j.laa.2014.04.028
[57] Srivastava, M. S., Yanagihara, H. & Kubokawa, T. (2014).
Tests for covariance matrices in high dimension with less sample size.
J. Multivariate Anal., Vol. 130, 289-309.
DOI:10.1016/j.jmva.2014.06.003
[58] Kamada, A., Yanagihara, H., Wakaki, H. & Fukui, K. (2014).
Selecting a shrinkage parameter in structural equation modeling with a near singular covariance matrix
by the GIC minimization method.
Hiroshima Math. J., Vol. 44 (3), 315-326.
DOI:10.32917/hmj/1419619749
[59] Fukui, K., Yamamura, M. & Yanagihara, H. (2015).
Comparison with RSS-based model selection criteria for selecting growth functions.
FORMATH , Vol. 14, 27-39.
DOI:10.15684/formath.14.004
[60] Yanagihara, H., Wakaki, H. & Fujikoshi, Y. (2015).
A consistency property of the AIC for multivariate linear models when the dimension and the sample
size are large.
Electron. J. Stat., Vol. 9, 869-897.
DOI:10.1214/15-EJS1022
[61] Yuan, K.-H., Tian, Y. & Yanagihara, H. (2015).
Empirical correction to the likelihood ratio statistic for structural equation modeling with many variables.
Psychometrika, Vol. 80 (2), 379-405..
DOI:10.1007/s11336-013-9386-5
[62] Yanagihara, H. (2015).
Conditions for consistency of a log-likelihood-based information criterion in normal multivariate linear
regression models under the violation of normality assumption.
J. Japan Statist. Soc., Vol. 45 (1), 21-56.
DOI:10.14490/jjss.45.21
[63] Yamamura, M., Fukui, K. & Yanagihara, H. (2016).
Illustration of the varying coefficient model for a tree growth analysis from the age and space
perspectives.
FORMATH , Vol. 15, 1-9.
DOI:10.15684/formath.15.001
[64] Yanagihara, H. (2016).
A high-dimensionality-adjusted consistent Cp-type statistic for selecting variables in a normality-assumed
linear regression with multiple responses.
Procedia Comput. Sci., Vol. 96, Knowledge-Based and Intelligent Information & Engineering Systems:
Proceedings of the 20th International Conference KES-2016 (Eds. R. J. Howlett, L. C. Jain, B. Gabrys,
C. Toro & C. P. Lim), 1096-1105.
DOI:10.1016/j.procs.2016.08.151
[65] Yamamura, M., Yanagihara, H., Solvang, H. K., Øien, N., & Haug, T. (2016).
Canonical correlation analysis for geographical and chronological responses.
Procedia Comput. Sci., Vol. 96, Knowledge-Based and Intelligent Information & Engineering Systems:
Proceedings of the 20th International Conference KES-2016 (Eds. R. J. Howlett, L. C. Jain, B. Gabrys,
C. Toro & C. P. Lim), 1096-1105.
DOI:10.1016/j.procs.2016.08.180
[66] Solvang, H. K., Yanagihara, H., Øien, N. & Haug, T. (2017)
Temporal and geographical variation in body condition of common minke whales (Balaenoptera
acutorostrata acutorostrata) in the Northeast Atlantic.
Polar Biol., Vol. 40 (3), 667-683.
DOI:10.1007/s00300-016-1992-0
[67] Yanagihara, H., Oda, R., Hashiyama, Y. & Fujikoshi, Y. (2017).
High-dimensional asymptotic behaviors of differences between the log-determinants of two Wishart
matrices.
J. Multivariate Anal., Vol. 157, 70-86.
DOI:10.1016/j.jmva.2017.03.002
[68] Yanagihara, H., Kamo, K., Imori, S. & Yamamura, M. (2017).
A study on the bias-correction effect of the AIC for selecting variables in normal multivariate linear
regression models under model misspecification.
REVSTAT-Stat. J., Vol. 15 (3), 299-332.
[69] Nagai, I., Takahashi, K. & Yanagihara, H. (2017).
Information criterion-based nonhierarchical clustering.
Int. J. Knowl. Eng. Soft Data Paradig., Vol. 6 (1), 1-43.
DOI:10.1504/IJKESDP.2017.089504
[70] Yanagihara, H. (2018).
Explicit solution to the minimization problem of generalized cross-validation criterion for selecting ridge
parameters in generalized ridge regression.
Hiroshima Math. J., Vol. 48 (2), 203-222.
DOI:10.32917/hmj/1533088835
[71] Oda, R., Yanagihara, H. & Fujikoshi, Y. (2019).
Asymptotic null and non-null distributions of test statistics for redundancy in high-dimensional canonical
correlation analysis.
Random Matrices-Theo., 1950001-1-26.
DOI:10.1142/S2010326319500011
[72] 蛹エ宏和 (2019).
非正規性の下での多変量線形回帰モデルにおけるモデル選択規準の大標本・高次元漸近理論による一致性の評価.
日本統計学会誌, Vol. 49 (1), 133-159 (日本統計学会研究業績賞受賞者特別寄稿論文).
DOI:10.11329/jjssj.49.133
[73] Oda, R., Suzuki,Y., Yanagihara, H. & Fujikoshi, Y. (2020).
A consistent variable selection method in high-dimensional canonical discriminant analysis.
J. Multivariate Anal., Vol. 175, 104561-1-13.
DOI:10.1016/j.jmva.2019.104561
[74] Ohishi, M., Yanagihara, H. & Fujikoshi, Y. (2020).
A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model
selection criterion.
J. Statist. Plann. Inference, Vol. 204, 187-205.
DOI:10.1016/j.jspi.2019.04.010
[75] Oda, R. & Yanagihara, H. (2020).
A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large
number of explanatory variables.
Electron. J. Stat., Vol. 14, 1386-1412.
DOI:10.1214/20-EJS1701
[76] Ohishi, M., Yanagihara, H. & Wakaki, H. (2020).
Optimization of generalized Cp criterion for selecting ridge parameters in generalized ridge regression.
Smart Innov. Syst. Tec. Vol. 193, Intelligent Decision Technologies 2020: Proceedings of the 12th KES International
Conference on Intelligent Decision Technologies (KES-IDT 2020) (eds. I. Czarnowski, R. J. Howlett & L. C. Jain),
267-278.
DOI:10.1007/978-981-15-5925-9_23
[77] Fukui, K., Ohishi, M., Yamamura, M. & Yanagihara, H. (2020).
A fast optimization method for additive model via partial generalized ridge regression.
Smart Innov. Syst. Tec. Vol. 193, Intelligent Decision Technologies 2020: Proceedings of the 12th KES International
Conference on Intelligent Decision Technologies (KES-IDT 2020) (eds. I. Czarnowski, R. J. Howlett & L. C. Jain),
279-290.
DOI:10.1007/978-981-15-5925-9_24
[78] Ohishi, M., Yanagihara, H. & Kawano, S. (2020).
Equivalence between adaptive-Lasso and generalized ridge estimators in linear regression with orthogonal explanatory
variables after optimizing regularization parameters.
Ann. Inst. Statist. Math., Vol. 72 (6), 1501-1516.
DOI:10.1007/s10463-019-00734-2
[79] Oda, R., Yanagihara, H. & Fujikoshi, Y. (2021).
Strong consistency of log-likelihood-based information criterion in high-dimensional canonical correlation analysis.
Sankhya A, Vol. 83 (1), 109-127.
DOI:10.1007/s13171-019-00174-3
[80] Oda, R., Mima,Y., Yanagihara, H. & Fujikoshi, Y. (2021).
A high-dimensional bias-corrected AIC for selecting response variables in multivariate calibration.
Comm. Statist. Theory Methods, Vol. 50 (14), 3453-3476.
DOI:10.1080/03610926.2019.1705978
[81] Oda, R. & Yanagihara, H. (2021).
A consistent likelihood-based variable selection method in normal multivariate linear regression.
Smart Innov. Syst. Tec., Vol. 238, Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 391-401.
DOI:10.1007/978-981-16-2765-1_33
[82] Yanagihara, H. & Oda, R. (2021).
Coordinate descent algorithm for normal-likelihood-based group Lasso in multivariate linear regression.
Smart Innov. Syst. Tec., Vol. 238, Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 429-439.
DOI:10.1007/978-981-16-2765-1_36
[83] Ohishi, M., Okamura, K., Itoh, Y. & Yanagihara, H. (2021).
Optimizations for categorizations of explanatory variables in linear regression via generalized fused Lasso.
Smart Innov. Syst. Tec., Vol. 238, Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 457-467.
DOI:10.1007/978-981-16-2765-1_38
[84] Yamamura, M., Ohishi, M. & Yanagihara, H. (2021).
Spatio-temporal adaptive fused Lasso for proportion data.
Smart Innov. Syst. Tec., Vol. 238, Intelligent Decision Technologies: Proceedings of the 13th KES-IDT 2021 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 479-489.
DOI:10.1007/978-981-16-2765-1_40
[85] Kamo, K. & Yanagihara, H. (2021).
Ridge estimate application to growth function.
FORMATH Vol. 20, 20.002,1-12.
DOI:10.15684/formath.20.002
[86] Ohishi, M., Fukui, K., Okamura, K., Itoh, Y. & Yanagihara, H. (2021).
Coordinate optimization for generalized fused lasso.
Comm. Statist. Theory Methods Vol. 50 (24), 5955-5973.
DOI:10.1080/03610926.2021.1931888
[87] Ohishi, M., Yamamura, M. & Yanagihara, H. (2022).
Coordinate descent algorithm of generalized fused Lasso logistic regression for multivariate trend filtering.
Jpn. J. Stat. Data Sci. Vol. 5 (2), 535-551.
DOI:10.1007/s42081-022-00162-2
[88] Mochizuki, K. & Yanagihara, H. (2022).
Confidence intervals in multiple linear regression conditioned on the selected model via the kick-one-out method.
Int. J. Knowl. Eng. Soft Data Paradig., Vol. 7 (2), 95-114.
DOI:10.1504/IJKESDP.2022.10051915
[89] Yanagihara, H., Nagai, I., Fukui, K. & Hijikawa, Y. (2023).
Modified Cp criterion in widely applicable models.
Smart Innov. Syst. Tec., Vol. 352, Intelligent Decision Technologies: Proceedings of the 15th KES-IDT 2023 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 173-182.
DOI:10.1007/978-981-99-2969-6_15
[90] Ohishi, M., Kirishima, K., Okamura, K., Itoh, Y. & Yanagihara, H. (2023).
Geographically weighted sparse group Lasso: local and global variable selections for GWR.
Smart Innov. Syst. Tec., Vol. 352, Intelligent Decision Technologies: Proceedings of the 15th KES-IDT 2023 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 183-192.
DOI:10.1007/978-981-99-2969-6_16
[91] Horikawa, K., Nagai, I., Monden, R. & Yanagihara, H. (2023).
Estimation algorithms for MLE of three-mode GMANOVA model with Kronecker product covariance matrix.
Smart Innov. Syst. Tec., Vol. 352, Intelligent Decision Technologies: Proceedings of the 15th KES-IDT 2023 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 203-213.
DOI:10.1007/978-981-99-2969-6_18
[92] Monden, R., Nagai, I. & Yanagihara, H. (2023).
Implications of the usage of the three-mode principal component analysis with fixed polynomial-basis.
Smart Innov. Syst. Tec., Vol. 352, Intelligent Decision Technologies: Proceedings of the 15th KES-IDT 2023 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 214-224.
DOI:10.1007/978-981-99-2969-6_19
[93] Yamamura, M., Ohishi, M. & Yanagihara, H. (2023).
Spatio-temporal analysis of rates derived from count data using generalized fused Lasso Poisson model.
Smart Innov. Syst. Tec., Vol. 352, Intelligent Decision Technologies: Proceedings of the 15th KES-IDT 2023 Conference
(eds. I. Czarnowski, R. J. Howlett & L. C. Jain), 225-234.
DOI:10.1007/978-981-99-2969-6_20
[94] Oda, R., Ohishi, M., Suzuki, Y. & Yanagihara, H. (2023).
An ℓ2,0-norm constrained matrix optimization via extended discrete first-order algorithms.
Hiroshima Math. J., Vol. 53, No. 3, 251-267.
DOI:10.32917/h2021058
[95] Yanagihara, H., Nagai, I., Fukui, K. & Hijikawa, Y. (2023).
Ridge parameter optimization using a modified Cp statistic in multivariate generalized ridge regression for the
GMANOVA model.
Procedia Comput. Sci., Vol. 225, 27th International Conference on Knowledge-Based and Intelligent
Information & Engineering Systems (KES-2023), (ed. R. Howlett), 1987-1996.
DOI:10.1016/j.procs.2023.10.189
[96] Yamamura, M., Ohishi, M. & Yanagihara, H. (2023).
Additive Poisson regression via forced categorical covariates and generalized fused Lasso.
Procedia Comput. Sci., Vol. 225, 27th International Conference on Knowledge-Based and Intelligent
Information & Engineering Systems (KES-2023), (ed. R. Howlett), 1987-1996.
DOI:10.1016/j.procs.2023.10.189
[97] Ohishi, M., Yanagihara, H., Wakaki, H. & Ono, M. (2023).
Stable estimation of the slant parameter in skew normal regression via an MM algorithm and ridge shrinkage.
Int. J. Knowl. Eng. Soft Data Paradig. (in press).
DOI:10.1504/IJKESDP.2023.10057725
[98] Yanagihara, H. & Shibayama, S. (2024).
Non-parametric bias-reduction estimation of residual variance in varying coefficient regression model.
Smart Innov. Syst. Tec., Intelligent Decision Technologies: Proceedings of the 16th KES-IDT 2024 Conference
(in press).
[99] Monden, R., Horikawa, K., Nagai, I. & Yanagihara, H. (2024).
Coordinate descent algorithm of the group Lasso for selecting between-individual explanatory variables in the
three-mode GMANOVA model.
Smart Innov. Syst. Tec., Intelligent Decision Technologies: Proceedings of the 16th KES-IDT 2024 Conference
(in press).
[100] Yamamura, M., Ohishi, M. & Yanagihara, H. (2024).
Poisson regression with categorical explanatory variables via Lasso using the median as a baseline.
Smart Innov. Syst. Tec., Intelligent Decision Technologies: Proceedings of the 16th KES-IDT 2024 Conference
(in press).
---著書---
[101] 蛹エ宏和・吉本 敦・能本美穂 (2004).
林分成長分析のための一般化非線形混合効果モデル.
森林資源管理と数理モデル Vol. 3, -FORMATH TSUKUBA 2003-(鹿又秀聡・吉本敦 編), 14-46,
森林計画学会出版局, 東京.
DOI:10.15684/formath.03.002
[102] 蛹エ宏和・吉本 敦 (2005).
単純同齢林における林木成長パターンのクラスタリング.
森林資源管理と数理モデル Vol. 4, -FORMATH NAGOYA 2004-(近藤洋史・吉本敦・松村直人 編), 49-70,
森林計画学会出版局, 東京.
DOI:10.15684/formath.04.003
[103] 吉本 敦・蛹エ宏和・能本美穂 (2005).
最適林分経営モデルによる間伐計画最適化と炭素吸収量.
森林資源管理と数理モデル Vol. 4, -FORMATH NAGOYA 2004-(近藤洋史・吉本敦・松村直人 編), 71-92,
森林計画学会出版局, 東京.
DOI:10.15684/formath.04.004
[104] Yanagihara, H. & Yoshimoto, A. (2005).
Statistical procedure for assessing the amount of carbon sequestrated by sugi (Cryptomeria japonica) plantation.
In Multipurpose Inventory for the Aged Artificial Forest (eds. Y. Nobori, N. Takahashi & A. Yoshimoto), 125-140,
Japan Society of Forest Planning Press, Utsunomiya.
[105] 蛹エ宏和・吉本 敦・二宮嘉行 (2006).
複数の成長パターンを持つスギ単純同齢林における炭素固定量予測.
森林資源管理と数理モデル Vol. 5, -FORMATH KYOTO 2005-(吉本敦・近藤洋史・広嶋卓也 編), 63-83,
森林計画学会出版局, 東京.
DOI:10.15684/formath.05.004
[106] 二宮嘉行・蛹エ宏和・吉本 敦 (2007).
非正則な統計モデルに基づく林分成長分析.
森林資源管理と数理モデル Vol. 6, -FORMATH KYUSHU 2006-(吉本敦・広嶋卓也・近藤洋史 編), 43-56,
森林計画学会出版局, 東京.
DOI:10.15684/formath.06.005
[107] 蛹エ宏和 (2007).
正規多変量線形モデルの変数選択.
統計データ科学事典 (杉山高一・藤越康祝・杉浦成昭・国友直人 編集), 192-193, 朝倉書店, 東京.
[108] 蛹エ宏和 (2007).
非正規性の下での多変量変数選択.
統計データ科学事典 (杉山高一・藤越康祝・杉浦成昭・国友直人 編集), 194-195, 朝倉書店, 東京.
[109] 蛹エ宏和 (2007).
その他の規準量での変数選択.
統計データ科学事典 (杉山高一・藤越康祝・杉浦成昭・国友直人 編集), 196-197, 朝倉書店, 東京.
[110] 蛹エ宏和 (2007).
次元縮小のための変数選択.
統計データ科学事典 (杉山高一・藤越康祝・杉浦成昭・国友直人 編集), 198-199, 朝倉書店, 東京.
[111] Yanagihara, H., Ninomiya, Y. & Yoshimoto, A. (2008).
Analysis of grouped growth patterns in even-aged sugi forest stand within the framework of mixture model.
森林資源管理と数理モデル Vol. 7, -FORMATH KOBE 2007-(eds. N. Sasaki & A. Yoshimoto), 39-60,
森林計画学会出版局, 東京.
DOI:10.15684/formath.07.004
[112] 加茂憲一・蛹エ宏和・嘉戸昭夫・吉本 敦 (2009).
自然災害リスク評価のためのロジスティック回帰モデルと変数選択.
森林資源管理と数理モデル Vol. 8, -FORMATH TOHOKU 2008-(FORMATH 研究会 編), 137-152,
森林計画学会出版局, 東京.
DOI:10.15684/formath.08.009
[113] Yamamura, M., Yanagihara, H. & Srivastava, M. S. (2010).
Variable selection by Cp statistic in multiple responses regression with fewer sample size than the dimension.
Knowledge-Based Intelligent Information and Engineering Systems: Lecture Notes in Computer Science,
14th International Conference, KES 2010, Cardiff, Wales, UK, September 8-10, Proceedings, Part III
(eds. R. Setchi et al.), 7-14, Springer, Heidelberg.
DOI:10.1007/978-3-642-15393-8_2
[114] 藤越康祝・若木宏文・蛹エ宏和 (2011).
確率・統計の数学的基礎. 広島大学出版会, 広島.
[115] 吉本 敦・加茂憲一・蛹エ宏和 (2012).
Rによる環境データの統計分析 —森林科学分野での応用—. 朝倉書店, 東京.
[116] Fukui, K. & Yanagihara, H. (2012).
Selection of high-dimensional multivariate linear regression models by cross-validation.
Proceedings of the 6th International Conference of IMBIC on Mathematical Sciences for Advancement of
Science and Technology MSAST 2012 (eds. A. Adhikari & M. R. Adhikari), 108-117, IMBIC, Kolkata.
[117] Imori, S. & Yanagihara, H. (2012).
General expression of bias of AIC.
Proceedings of the 6th International Conference of IMBIC on Mathematical Sciences for Advancement of
Science and Technology MSAST 2012 (eds. A. Adhikari & M. R. Adhikari), 118-129, IMBIC, Kolkata.
---紀要等---
[118] 高橋 綾・蛹エ宏和・大瀧 慈・務中昌己 (2000).
生活科学としての健康科学, — 戸建て住宅の価格と環境条件に関するノンパラメトリック回帰分析 —.
広島女学院大学生活科学部紀要, Vol. 7, 57-65.
[119] Yanagihara, H. & Yuan, K.-H. (2008).
Edgeworth expansions of functions of the sample covariance matrix with an unknown population.
TR 08-05, Statistical Research Group, Hiroshima University, Hiroshima.
[120] Ninomiya, Y., Yanagihara, H. & Yuan, K.-H. (2008).
Selecting the number of factors in exploratory factor analysis via locally conic parameterization.
Research Memorandum No. 1078, The Institute of Statistical Mathematics, Tokyo.
[121] 蛹エ宏和 (2016).
多変量線形回帰モデルにおける一致性を持つ Cp 型規準が真の変数を選択する確率の収束オーダー.
Statistical Inference on Divergence Measures and Its Related Topics (京都, 2016),
数理解析研究所講究録, Vol. 1999, 72-85.
[122] 小田凌也・蛹エ宏和 (2017).
異なった GMANOVA モデルにおける最小重み付き残差平方和の差の分布.
Bayes Inference and Its Related Topics (京都, 2017),
数理解析研究所講究録, Vol. 2047, 107-123.
[123] 大石峰暉・蛹エ宏和 (2017).
Minimization algorithm of model selection criterion for optimizing tuning parameter in Lasso estimator when
explanatory variables are orthogonal.
Bayes Inference and Its Related Topics (京都, 2017),
数理解析研究所講究録, Vol. 2047, 124-140.
[124] 蛹エ宏和 (2019).
重回帰モデルでの変数選択における一般化 Cp 規準の一致性の評価.
高次元量子雑音の統計モデリング (京都, 2018),
数理解析研究所講究録, Vol. 2133, 56-65.
[125] Yanagihara, H. (2020).
High-dimensionality-adjusted asymptotically loss and mean efficient GCp criterion for normal multivariate linear
regression models.
TR 20-03, Statistical Research Group, Hiroshima University, Hiroshima.
[126] Ohishi, M., Okamura, K., Itoh, Y. & Yanagihara, H. (2021).
Coordinate descent algorithm for generalized group fused Lasso.
TR 21-02, Statistical Research Group, Hiroshima University, Hiroshima.
[127] Oda, R., Yanagihara, H. & Fujikoshi, Y. (2021).
On model selection consistency using a kick-one-out method for selecting response variables in high-dimensional
multivariate linear regression.
TR 21-07, Statistical Research Group, Hiroshima University, Hiroshima.
[128] Yamamura, M., Yanagihara, H., Ohishi, M., Fukui, K., Solvang, H., Øien, N. & Haug, T. (2023).
Estimation of spatial effects by generalized fused Lasso for nonuniformly sampled spatial data: An analysis of the
body condition of common minke whales (Balaenoptera acutorostrata acutorostrata) in the northeast Atlantic.
TR 23-05, Statistical Research Group, Hiroshima University, Hiroshima.
---その他の論文---
[129] 磯山真紀・纐纈憲子・蛹エ宏和.
日本語留学 - その現実と問題点.



所属学会

日本統計学会 : 日本数学会 :

その他の活動

日本統計学会 75周年記念事業 若手委員 (2006年)
日本応用統計学会誌: 応用統計学 編集委員(2006年−2009年)
日本数学会 統計数学分科会 運営委員 (2009年−2012年, 2019年−2022年)
日本統計学会誌 英文誌: Journal of the Japan Statistical Society 編集委員 (2013年−2018年)
統計関連学会連合大会 運営委員 (2013年).
行動計量学会誌 和文誌: 行動計量学 編集委員 (2015年6月−2021年5月).
行動計量学会誌 英文誌: Behaviormetrika 編集委員 (2016年2月−)
Hiroshima Mathematical Journal 編集委員 (2017年3月−)
日本統計関連学会誌 英文誌: Japanese Journal of Statistics and Data Science 編集委員 (2017年9月−2023年3月)
日本統計学会誌 和文誌: 日本統計学会誌 編集委員長 (2021年6月−2023年5月), 編集委員 (2023年6月−2025年5月)


広島大学 数理統計学グループ のホームページ

広島大学 情報科学部 のホームページ

広島大学 大学院先進理工系科学研究科 数学プログラム のホームページ

広島大学 のホームページ



統計数理研究所 のホームページ

University of Notre Dame, Department of Psychology のホームページ

筑波大学 大学院システム情報工学研究群 のホームページ

(株) 東京カンテイ のホームページ

University of Toronto, Department of Statistical Science のホームページ