H. Yanagihara

发表

Iterative Bias Correction of the Cross‐Validation Criterion

H. Fujisawa, H. Yanagihara, 2012 .

Three Approximate Solutions to the Multivariate Behrens–Fisher Problem

Ke-Hai Yuan, Hirokazu Yanagihara, K. Yuan, 2005 .

Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables

K. Yuan, H. Yanagihara, Yubin Tian, 2013, Psychometrika.

Conditions for Robustness to Nonnormality on Test Statistics in a GMANOVA Model

H. Yanagihara, 2007 .

Bias-Corrected AIC for Selecting Variables in Poisson Regression Models

K. Satoh, H. Yanagihara, K. Kamo, 2013 .

Variable Selection in Multivariate Linear Regression Models with Fewer Observations than the Dimension

M. Srivastava, H. Yanagihara, Mariko Yamamura, 2010 .

Statistical Inference on a Varying Coefficient Surface Using Interaction Model for Spatial Data

Hirokazu Yanagihara, Kenichi Satoh, Tetsuji Tonda, 2010 .

Temporal and geographical variation in body condition of common minke whales (Balaenoptera acutorostrata acutorostrata) in the Northeast Atlantic

Hirokazu Yanagihara, Nils Øien, Tore Haug, 2017, Polar Biology.

Illustration of the Varying Coefficient Model for a Tree Growth Analysis from the Age and Space Perspectives

Hirokazu Yanagihara, Mariko Yamamura, H. Yanagihara, 2016 .

A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion

Yasunori Fujikoshi, Hirokazu Yanagihara, Mineaki Ohishi, 2019 .

Strong Consistency of Log-Likelihood-Based Information Criterion in High-Dimensional Canonical Correlation Analysis

Y. Fujikoshi, H. Yanagihara, Ryoya Oda, 2019, Sankhya A.

A consistency property of the AIC for multivariate linear models when the dimension and the sample size are large

Y. Fujikoshi, H. Wakaki, H. Yanagihara, 2015 .

Analysis of Grouped Growth Patterns in Even-Aged Sugi Forest Stand within the Framework of Mixture Model

Y. Ninomiya, A. Yoshimoto, H. Yanagihara, 2008 .

Conditions for Consistency of a Log-Likelihood-Based Information Criterion in Normal Multivariate Linear Regression Models under the Violation of the Normality Assumption

H. Yanagihara, 2015 .

Economic Analysis of Snow Damage on Sugi (Cryptomeria japonica) Forest Stands in Japan Within the Forest Stand Optimization Framework

A. Yoshimoto, H. Yanagihara, A. Kato, 2008 .

High-dimensionality-adjusted Asymptotically Loss and Mean Efficient GCp Criterion for Normal Multivariate Linear Regression Models

H. Yanagihara, 2020 .

Explicit solution to the minimization problem of generalized cross-validation criterion for selecting ridge parameters in generalized ridge regression

H. Yanagihara, 2018, Hiroshima Mathematical Journal.

Asymptotic non-null distributions of test statistics for redundancy in high-dimensional canonical correlation analysis

Y. Fujikoshi, H. Yanagihara, Ryoya Oda, 2017 .

High-Dimensional Asymptotic Behaviors of Di fferences between the Log-Determinants of Two Wishart Matrices

Y. Fujikoshi, H. Yanagihara, Yusuke Hashiyama, 2015 .

Asymptotic expansions of the null distributions of test statistics for multivariate linear hypothesis under nonnormality

Y. Fujikoshi, H. Wakaki, H. Yanagihara, 2002 .

Four improved statistics for contrasting means by correcting skewness and kurtosis.

K. Yuan, H. Yanagihara, 2005, The British journal of mathematical and statistical psychology.

Bridging the Gap Between B-Spline and Polynomial Regression Model

K. Satoh, M. Ohtaki, H. Yanagihara, 2003 .

High-Dimensional AIC and Consistency Properties of Several Criteria in Multivariate Linear Regression

Y. Fujikoshi, H. Yanagihara, Tetsuro Sakurai, 2012 .

Asymptotic null and non-null distributions of test statistics for redundancy in high-dimensional canonical correlation analysis

Y. Fujikoshi, H. Yanagihara, Ryoya Oda, 2018, Random Matrices: Theory and Applications.

A FAMILY OF REGRESSION MODELS HAVING PARTIALLY ADDITIVE AND MULTIPLICATIVE COVARIATE STRUCTURE

M. Ohtaki, H. Yanagihara, 2005 .

Coordinate descent algorithm of generalized fused Lasso logistic regression for multivariate trend filtering

H. Yanagihara, Mariko Yamamura, M. Ohishi, 2022, Japanese Journal of Statistics and Data Science.

Equivalence between adaptive Lasso and generalized ridge estimators in linear regression with orthogonal explanatory variables after optimizing regularization parameters

Shuichi Kawano, H. Yanagihara, M. Ohishi, 2019, Annals of the Institute of Statistical Mathematics.

A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables

H. Yanagihara, Ryoya Oda, 2020, Electronic Journal of Statistics.

Ridge Estimate Application to Growth Function

H. Yanagihara, K. Kamo, 2021, FORMATH.

Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables

K. Yuan, H. Yanagihara, Yubin Tian, 2013, Psychometrika.

A non-iterative optimization method for smoothness in penalized spline regression

H. Yanagihara, 2011, Statistics and Computing.

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

Kentaro Hayashi, Ke-Hai Yuan, Hirokazu Yanagihara, 2007, Multivariate behavioral research.

Knot-Placement to Avoid over Fitting in B-Spline Scedastic Smoothing

M. Ohtaki, H. Yanagihara, 2003 .

A Fast Optimization Method for Additive Model via Partial Generalized Ridge Regression

Keisuke Fukui, Mariko Yamamura, Mineaki Ohishi, 2020, KES-IDT.

Optimization of Generalized Cp Criterion for Selecting Ridge Parameters in Generalized Ridge Regression

Hirofumi Wakaki, Mineaki Ohishi, Hirokazu Yanagihara, 2020, KES-IDT.

A High-dimensionality-adjusted Consistent Cp-type Statistic for Selecting Variables in a Normality-assumed Linear Regression with Multiple Responses

Hirokazu Yanagihara, H. Yanagihara, 2016, KES.

Comparison with Residual-Sum-of-Squares-Based Model Selection Criteria for Selecting Growth Functions

H. Yanagihara, Mariko Yamamura, K. Fukui, 2015 .

Comparison with RSS-Based Model Selection Criteria for Selecting Growth Functions

H. Yanagihara, Mariko Yamamura, K. Fukui, 2014 .

Consistency of high-dimensional AIC-type and Cp-type criteria in multivariate linear regression

Yasunori Fujikoshi, Tetsuro Sakurai, Hirokazu Yanagihara, 2014, J. Multivar. Anal..

A non-iterative optimization method for smoothness in penalized spline regression

Hirokazu Yanagihara, H. Yanagihara, 2012, Stat. Comput..

Second‐order bias‐corrected AIC in multivariate normal linear models under non‐normality

T. Tonda, H. Yanagihara, K. Kamo, 2011 .

Variable Selection by Cp Statistic in Multiple Responses Regression with Fewer Sample Size Than the Dimension

Hirokazu Yanagihara, Muni S. Srivastava, Mariko Yamamura, 2010, KES.

GLS Discrepancy Based Information Criteria for Selecting Covariance Structure Models

K. Yuan, H. Yanagihara, Tetsuto Himeno, 2010 .

An unbiased Cp criterion for multivariate ridge regression

Hirokazu Yanagihara, Kenichi Satoh, K. Satoh, 2010, J. Multivar. Anal..

Bias Corrections of some Criteria for Selecting Multivariate Linear Models in a General Nonnormal Case

Y. Fujikoshi, H. Wakaki, H. Yanagihara, 2005 .

Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models

Y. Fujikoshi, M. Ohtaki, H. Yanagihara, 2003 .

A family of estimators for multivariate kurtosis in a nonnormal linear regression model

H. Yanagihara, 2007 .

Testing the equality of several covariance matrices with fewer observations than the dimension

Hirokazu Yanagihara, Muni S. Srivastava, M. Srivastava, 2010, J. Multivar. Anal..

Tests for covariance matrices in high dimension with less sample size

Tatsuya Kubokawa, Hirokazu Yanagihara, Muni S. Srivastava, 2014, J. Multivar. Anal..

Corrected version of AIC for selecting multivariate normal linear regression models in a general nonnormal case

H. Yanagihara, 2006 .

The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance structures under normal assumption

Hirokazu Yanagihara, Chieko Matsumoto, Tetsuji Tonda, 2005 .

Selecting a shrinkage parameter in structural equation modeling with a near singular covariance matrix by the GIC minimization method

H. Wakaki, H. Yanagihara, K. Fukui, 2014 .

Asymptotic expansion of the null distribution of the modified normal likelihood ratio criterion for testing {$\Sigma=\Sigma\sb 0$} under nonnormality

T. Tonda, H. Yanagihara, C. Matsumoto, 2004 .

Variable Selection

Cees G. M. Snoek, Hirokazu Yanagihara, Muni S. Srivastava, 2019, Model-Based Clustering and Classification for Data Science.

Choosing the Number of Repetitions in the Multiple Plug-in Optimization Method for the Ridge Parameters in Multivariate Generalized Ridge Regression ( Last Modified : October 1 , 2012 )

H. Yanagihara, K. Fukui, Isamu Nagai, 2012 .

A mathematical estimation of true cancer incidence using data from population-based cancer registries.

Hirokazu Yanagihara, Tomotaka Sobue, Kenichi Satoh, 2007, Japanese journal of clinical oncology.

Bias correction of AIC in logistic regression models

Yasunori Fujikoshi, Hirokazu Yanagihara, Risa Sekiguchi, 2003 .

Coordinate optimization for generalized fused Lasso

M. Ohishi, K. Fukui, K. Okamura, 2021, Communications in Statistics - Theory and Methods.

Spatio-Temporal Adaptive Fused Lasso for Proportion Data

Hirokazu Yanagihara, Mariko Yamamura, Mineaki Ohishi, 2021, KES-IDT.

Optimizations for Categorizations of Explanatory Variables in Linear Regression via Generalized Fused Lasso

Hirokazu Yanagihara, Mineaki Ohishi, Kensuke Okamura, 2021, KES-IDT.

Ordering Municipalities by Medical Cost Efficiency Under the Japanese National Health Insurance System Using the Stochastic Cost Frontier Model

H. Yanagihara, Mariko Yamamura, 2009 .

Estimation of Varying Coefficients for a Growth Curve Model

K. Satoh, H. Yanagihara, 2010 .

Statistical Inference on a Linear Varying Coefficient on Longitudinal Data of Discrete Distribution

K. Satoh, H. Yanagihara, K. Kamo, 2009 .

Information criterion-based non-hierarchical clustering

Hirokazu Yanagihara, Isamu Nagai, Katsuyuki Takahashi, 2017, Int. J. Knowl. Eng. Soft Data Paradigms.

Bias-corrected AIC for selecting variables in multinomial logistic regression models☆

K. Satoh, H. Yanagihara, K. Kamo, 2012 .

High-dimensional asymptotic behavior of the difference between the log-determinants of two Wishart matrices

Yasunori Fujikoshi, Hirokazu Yanagihara, Ryoya Oda, 2017, J. Multivar. Anal..

Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification

Yasunori Fujikoshi, Hirokazu Yanagihara, Yusuke Hashiyama, 2014 .

A consistent variable selection method in high-dimensional canonical discriminant analysis

Yuya Suzuki, Yasunori Fujikoshi, Hirokazu Yanagihara, 2020, J. Multivar. Anal..

Simple Formula for Calculating Bias‐corrected AIC in Generalized Linear Models

H. Wakaki, H. Yanagihara, S. Imori, 2014 .

On distribution of AIC in linear regression models

H. Yanagihara, Chihiro Ohmoto, 2005 .

Asymptotic expansions of the null distributions of three test statistics in a nonnormal GMANOVA model

H. Yanagihara, 2001 .

A high-dimensional bias-corrected AIC for selecting response variables in multivariate calibration

Y. Fujikoshi, H. Yanagihara, Ryoya Oda, 2020 .

CHOOSING THE NUMBER OF REPETITIONS IN THE MULTIPLE PLUG-IN OPTIMIZATION METHOD FOR THE RIDGE PARAMETERS IN MULTIVARIATE GENERALIZED RIDGE REGRESSION

H. Yanagihara, Isamu Nagai, Keisuke Fukui, 2013 .

Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods

K. Satoh, H. Yanagihara, Isamu Nagai, 2012 .

A Study on the Bias-Correction Effect of the AIC for Selecting Variables in Normal Multivariate Linear Regression Models under Model Misspecification

H. Yanagihara, K. Kamo, Mariko Yamamura, 2013 .

ASYMPTOTIC APPROXIMATIONS OF THE NULL DISTRIBUTION OF THE ONE-WAY ANOVA TEST STATISTIC UNDER NONNORMALITY

Y. Fujikoshi, H. Yanagihara, M. Ohmae, 1999 .