Haruhiko Ogasawara
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Professor Emeritus
Otaru University of Commerce
3-5-21, Midori, Otaru 047-8501 Japan
E-mail: emt-hogasa{ at mark without braces }emt.otaru-uc.ac.jp

B.Ed., The University of Tokyo, 1974
Ph.D., Tokyo Institute of Technology, 1992
Ph.D. in Educational Informatics, Tohoku University, 2014

Expertise

The area of interest has been in the latent-variable models and associated methods in multivariate analysis:

• Factor analysis and principal component analysis: Statistical analysis of rotated results
• Asymptotic expansions of the estimators in multivariate analysis
  
• Item response theory
  
• Probability inequalities
  

Professional Services

• Member of Editorial Board (1994- ), Behaviormetrika
• Associate Editor (2007-2018), Psychometrika
• Associate Editor (2019- ), Journal of Multivariate Analysis

ROSEF 2.0 (April, 2003): A subroutine library for the ROtated solutions with their asymptotic Standard Errors in Factor analysis

EL 1.0 (May, 2003): A set of subroutines for the methods of item response theory (IRT) Equating/Linking with their asymptotic standard errors

Recent Publications

• Ogasawara, H. (1989). Covariance structure analysis of continuously changing populations. Behaviormetrika, No.25, 15-33.


• Ogasawara, H. (1990). Covariance structure model when the means and the covariances are functions of the third variable. Japanese Psychological Research, 32 (1), 19-25.


• Ogasawara, H. (1992). Models of the number of errors using structured parameters in a generalized Poisson distributions and the Polya-Eggenberger distribution. Japanese Journal of Behaviormetrics, 19 (2), 1-13. (In Japanese with English abstract)


• Ogasawara, H. (1995). Structural model of ability distribution in the item response theory. Behaviormetrika, 22 (1), 37-48.


• Ogasawara, H. (1995). The gamma-gamma regression for the distribution model of event times. Japanese Psychological Research, 37 (2), 70-79.


• Ogasawara, H. (1996). Rasch's multiplicative Poisson model with covariates. Psychometrika, 61 (1), 73-92.


• Ogasawara, H. (1996). Standard errors for rotated factor loadings by normalized orthomax method. Japanese Journal of Behaviormetrics, 23 (2), 122-129. (In Japanese with English abstract)


• Ogasawara, H. (1998). A factor analysis model for a mixture of various types of variables. Behaviormetrika, 25 (1), 1-12.


• Ogasawara, H. (1998). Standard errors for rotation matrices with an application to the promax solution. British Journal of Mathematical and Statistical Psychology, 51, (1), 163-178.


• Ogasawara, H. (1998). Standard errors of several indices for unrotated and rotated factors. Economic Review, Otaru University of Commerce, 47 (1), 21-69.


• Ogasawara, H. (1998). A log-bilinear model with latent variables. Behaviormetrika, 25 (2), 95-110.


• Ogasawara, H. (1999). Standard errors for procrustes solutions. Japanese Psychological Research, 41 (2), 121-130.


• Ogasawara, H. (1999). Standard errors for matrix correlations. Multivariate Behavioral Research, 34 (1),103-122.


• Ogasawara, H. (1999). Standard errors for the direct oblimin solution with Kaiser's normalization. Japanese Journal of Psychology, 70 (4),333-338. (In Japanese with English abstract)


• Ogasawara, H. (1999). Negative binomial factor analysis. Behaviormetrika, 26 (2), 235-250.


• Ogasawara, H. (2000). On the standard errors of rotated factor loadings with weights for observed variables. Behaviormetrika, 27 (1), 1-14.


• Ogasawara, H. (2000) Some relationships between factors and components. Psychometrika, 65 (2), 167-185 (errata, 65 (4), 551).


• Ogasawara, H. (2000). Asymptotic standard errors of IRT equating coefficients using moments. Economic Review, Otaru University of Commerce, 51 (1), 1-23.


• Ogasawara, H. (2000). Standard errors for the Harris-Kaiser Case II orthoblique solution. Behaviormetrika, 27 (2), 89-103.


• Ogasawara, H. (2000). Asymptotic correlations between rotated solutions in factor analysis. Behaviormetrika, 27 (2), 105-123.


• Ogasawara, H. (2000). Standard errors of the principal component loadings for unstandardized and standardized variables. British Journal of Mathematical and Statistical Psychology, 53 (2), 155-174.


• Ogasawara, H. (2001). Standard errors of item response theory equating/linking by response function methods. Applied Psychological Measurement, 25 (1), 53-67.


• Ogasawara, H. (2001). Marginal maximum likelihood estimation of item response theory (IRT) equating coefficients for the common-examinee design. Japanese Psychological Research, 43 (2), 72-82. (See also Ogasawara, H. (2001). A supplementary note on the joint maximum likelihood estimation of the IRT equating coefficients and abilities for the common-examinee design. Economic Review, Otaru University of Commerce, 52 (1), 239-241.)


• Ogasawara, H. (2001). Approximations to the distributions of fit indexes for misspecified structural equation models. Structural Equation Modeling, 8 (4), 556-574.


• Ogasawara, H. (2001). Item response theory true score equatings and their standard errors. Journal of Educational and Behavioral Statistics, 26 (1), 31-50.


• Ogasawara, H. (2001). Least squares estimation of item response theory linking coefficients. Applied Psychological Measurement, 25 (4), 373-383.


• Ogasawara, H. (2001). Standard errors of fit indices using residuals in structural equation modeling. Psychometrika, 66 (3), 421-436.


• Ogasawara, H. (2002). Exploratory second-order analyses for components and factors. Japanese Psychological Research, 44 (1), 9-19. (See also Ogasawara, H. (2002). A supplementary note on the paper by Ogasawara, H. "Exploratory second-order analyses for components and factors". Economic Review, Otaru University of Commerce, 53 (1), 117-128.)


• Ogasawara, H. (2002). Concise formulas for the standard errors of component loading estimates. Psychometrika, 67 (2), 289-297.


• Ogasawara, H. (2002). Stable response functions with unstable item parameter estimates. Applied Psychological Measurement, 26 (3), 239-254.


• Ogasawara, H. (2002). Asymptotic standard errors of estimated standard errors in structural equation modeling. British Journal of Mathematical and Statistical Psychology, 55 (2), 213-229.


• Ogasawara, H. (2003). A note on the approximate distributions of fit indexes for misspecified structural equation models. In H. Yanai, A. Okada, K. Shigemasu, Y. Kano & J. J. Meulman (Eds.), New developments in psychometrics: Proceedings of the International Meeting of the Psychometric Society IMPS2001, Osaka, Japan, July 15-19, 2001 (pp. 103-108). Tokyo: Springer.


• Ogasawara, H. (2003). Correlations among maximum likelihood and weighted/unweighted least squares estimators in factor analysis. Behaviormetrika, 30 (1), 63-86.


• Ogasawara, H. (2003). Asymptotic standard errors of IRT observed-score equating methods. Psychometrika, 68 (2), 193-211.


• Ogasawara, H. (2003). Oblique factors and components with independent clusters. Psychometrika, 68 (2), 299-321. (See also Ogasawara, H. (2003). A supplementary note on the paper "Oblique factors and components with independent clusters". Economic Review, Otaru University of Commerce, 53 (4), 55-66.)


• Ogasawara, H. (2004). Asymptotic biases of the unrotated/rotated solutions in principal component analysis. British Journal of Mathematical and Statistical Psychology, 57 (2), 353-376.


• Ogasawara, H. (2004). Asymptotic biases of least squares estimators in stuctural equation modeling. In S. P. Shohov (Ed.), Advances in psychology research: Vol. 27. (pp. 65-94). New York: Nova.


• Ogasawara, H. (2004). Asymptotic biases in exploratory factor analysis and stuctural equation modeling. Psychometrika, 69 (2), 235-256.


• Ogasawara, H. (2005). Bias reduction of estimated standard errors in factor analysis. Behaviormetrika, 32 (1), 9-28.


• Ogasawara, H. (2005). Asymptotic robustness of the asymptotic biases in stuctural equation modeling. Computational Statistics and Data Analysis, 49 (3), 771-783.


• Ogasawara, H. (2006). Asymptotic expansion of the sample correlation coefficient under nonnormality. Computational Statistics and Data Analysis, 50 (4), 891-910.


• Ogasawara, H. (2006). Asymptotic expansion and conditional robustness for the sample multiple correlation coefficient under nonnormality. Communications in Statistics - Simulation and Computation, 35 (1), 177-199.


• Ogasawara, H. (2006). Higher-order asymptotic standard error and asymptotic expansion in principal component analysis. Communications in Statistics - Simulation and Computation, 35 (1), 201-223. (See also Ogasawara, H. (2006). A supplementary note on the paper "Higher-order asymptotic standard error and asymptotic expansion in principal component analysis". Economic Review, Otaru University of Commerce, 56 (4), 235-243.)


• Ogasawara, H. (2006). Approximations to the distribution of the sample coefficient alpha under nonnormality. Behaviormetrika, 33 (1), 3-26.


• Ogasawara, H. (2006). Two-term Edgeworth expansion of the distributions of the maximum likelihood estimators in factor analysis under nonnormality. In A. Rizzi & M. Vichi (Eds.), Proceedings in Computational Statistics on CD, 17th symposium held in Rome, Italy, 2006 (pp.1681-1688). Heidelberg: Physica-Verlag.


• Ogasawara, H. (2007). Higher-order estimation error in structural equation modeling. Economic Review, Otaru University of Commerce, 57 (4), 131-160.


• Ogasawara, H. (2007). Higher-order approximations to the distributions of fit indexes under fixed alternatives in structural equation modeling. Psychometrika, 72 (2), 227-243.


• Ogasawara, H. (2007). Asymptotic expansion and asymptotic robustness of the normal-theory estimators in the random regression model. Journal of Statistical Computation and Simulation, 77 (10), 821-838. (See also Ogasawara, H. (2008). A supplementary note on the paper "Asymptotic expansion and asymptotic robustness of the normal-theory estimators in the random regression model". Economic Review, Otaru University of Commerce, 58 (4), 235-242.)


• Ogasawara, H. (2007). Asymptotic expansions of the distributions of estimators in canonical correlation analysis under nonnormality. Journal of Multivariate Analysis, 98 (9), 1726-1750.


• Ogasawara, H. (2007). Asymptotic expansion of the estimators in factor analysis under nonnormality. British Journal of Mathematical and Statistical Psychology, 60 (2), 395-420.


• Ogasawara, H. (2008). Asymptotic expansion in reduced rank regression under normality and nonnormality. Communications in Statistics - Theory and Methods, 37 (7), 1051-1070.


• Ogasawara, H. (2008). Higher-order asymptotic cumulants of Studentized estimators in covariance structures. Communications in Statistics - Simulation and Computation, 37 (5), 945-961. (See also Ogasawara, H. (2008). Errata and supplement to the paper "Higher-order asymptotic cumulants of Studentized estimators in covariance structures". Economic Review, Otaru University of Commerce, 59 (2 & 3), 95-107. Ogasawara, H. (2010). Corrections of the numerical values in the paper "Higher-order asymptotic cumulants of Studentized estimators in covariance structures". Unpublished document.)


• Ogasawara, H. (2008). Asymptotic expansions of the distribution of the estimator for the generalized partial correlation under nonnormality. Behaviormetrika, 35 (1), 15-33.


• Ogasawara, H. (2008). Some properties of the pivotal statistic based on the asymptotically distribution-free theory in structural equation modeling.Communications in Statistics - Simulation and Computation, 37 (10), 1931-1947. (See also Ogasawara, H. (2009). Supplement to Ogasawara's papers on "the ADF pivotal statistic", "mean and covariance structure analysis", and "maximal reliability". Economic Review, Otaru University of Commerce, 60 (1), 21-44.)


• Ogasawara, H. (2008). Inverse transformation of Hall's method back to Edgeworth type expansions: An expository note. In K. Shigemasu, A. Okada, T. Imaizumi, & T. Hoshino (Eds.), New trends in psychometrics (pp.365-400). Tokyo: Universal Academy Press.


• Ogasawara, H.(2009). Asymptotic expansions in mean and covariance structure analysis. Journal of Multivariate Analysis, 100 (5), 902-912. (See also Ogasawara, H. (2009). Supplement to Ogasawara's papers on "the ADF pivotal statistic", "mean and covariance structure analysis", and "maximal reliability". Economic Review, Otaru University of Commerce, 60 (1), 21-44.)


• Ogasawara, H. (2009). On the estimators of model-based and maximal reliability. Journal of Multivariate Analysis, 100 (6), 1232-1244. (See also Ogasawara, H. (2009). Supplement to Ogasawara's papers on "the ADF pivotal statistic", "mean and covariance structure analysis", and "maximal reliability". Economic Review, Otaru University of Commerce, 60 (1), 21-44.)


• Ogasawara, H. (2009). Two-term Edgeworth expansions of the distributions of fit indexes under fixed alternatives in covariance structure models. Economic Review, Otaru University of Commerce, 59 (4), 41-48.


• Ogasawara, H. (2009). Stratified coefficients of reliability and their sampling behavior under nonnormality. Behaviormetrika, 36 (1), 49-73.


• Ogasawara, H. (2009). Asymptotic cumulants of the parameter estimators in item response theory. Computational Statistics, 24 (2), 313-331. (For errata, see here.)


• Ogasawara, H. (2009). Asymptotic expansions of the distributions of the chi-square statistic based on the asymptotically distribution-free theory in covariance structures. Journal of Statistical Planning and Inference, 139 (9), 3246-3261. (See also Ogasawara, H. (2010). Supplement to the paper "Asymptotic expansions of the distributions of the chi-square statistic based on the asymptotically distribution-free theory in covariance structures". Economic Review, Otaru University of Commerce, 60 (4), 187-200.)


• Ogasawara, H. (2009). Asymptotic expansions in the singular value decomposition for cross covariance and correlation under nonnormality. Annals of the Institute of Statistical Mathematics, 61 (4), 995-1017.


• Ogasawara, H. (2010). Accurate distribution and its asymptotic expansion for the tetrachoric correlation coefficient. Journal of Multivariate Analysis, 101 (4), 936-948.


• Ogasawara, H. (2010). Asymptotic expansions for the pivots using log-likelihood derivatives with an application in item response theory. Journal of Multivariate Analysis, 101 (9), 2149-2167.


• Ogasawara, H. (2010). Asymptotic expansions of the null distributions of discrepancy functions for general covariance structures under nonnormality. American Journal of Mathematical and Management Sciences, 30 (3 & 4), 385-422. (See also Ogasawara, H. (2012). Supplement to the papers on "the polyserial correlation coefficients" and "discrepancy functions for general covariance structures". Economic Review, Otaru University of Commerce, 62 (4), 115-164.)


• Ogasawara, H. (2011). Applications of asymptotic expansion in item response theory linking. In A. A. von Davier (Ed.), Statistical models of test equating, scaling, and linking (pp.261-280). New York: Springer.


• Ogasawara, H. (2011). Asymptotic expansions in multi-group analysis of moment structures with an application to linearized estimators. Communications in Statistics - Theory and Methods, 40 (9), 1701-1716. (See also Ogasawara, H. (2011). Supplement to the paper "Asymptotic expansions in multi-group analysis of moment structures with an application to linearlized estimators" and errata for the paper on the ADF chi-square statistic. Economic Review, Otaru University of Commerce, 61 (4), 135-140.)


• Ogasawara, H. (2011). Asymptotic expansions of the distributions of the polyserial correlation coefficients. Behaviormetrika, 38 (2), 153-168. (See also Ogasawara, H. (2012). Supplement to the papers on "the polyserial correlation coefficients" and "discrepancy functions for general covariance structures". Economic Review, Otaru University of Commerce, 62 (4), 115-164.)


• Ogasawara, H. (2012). Cornish-Fisher expansions using sample cumulants and monotonic transformations. Journal of Multivariate Analysis, 103 (1), 1-18.


• Ogasawara, H. (2012). Asymptotic expansions of the distributions of the least squares estimators in factor analysis and structural equation modeling. In R. Chakraborty, C. R. Rao, & P. K. Sen (Eds.), Handbook of statistics: Vol.28. Bioinformatics in human health and heredity (pp. 163-200). New York: Elsevier.


• Ogasawara, H. (2012). Asymptotic cumulants of functions of multinomial sample proportions with adjustment for empty cells. Behaviormetrika, 39 (2), 211-241.


• Ogasawara, H. (2012). Asymptotic expansions for the ability estimator in item response theory. Computational Statistics, 27 (4), 661-683. (See also Ogasawara, H. (2012). Supplement to the paper "Asymptotic expansions for the ability estimator in item response theory". Economic Review, Otaru University of Commerce, 63 (2 & 3), 329-336.)


• Ogasawara, H. (2013). Asymptotic properties of the Bayes and pseudo Bayes estimators of ability in item response theory. Journal of Multivariate Analysis, 114, 359-377. (See also Ogasawara, H. (2013). Supplement to the paper "Asymptotic properties of the Bayes and pseudo Bayes estimators of ability in item response theory". Economic Review, Otaru University of Commerce, 63 (4), 83-89.)


• Ogasawara, H. (2013). Asymptotic cumulants of ability estimators using fallible item parameters. Journal of Multivariate Analysis, 119, 144-162. (See also Ogasawara, H. (2013). Supplement I to the paper "Asymptotic cumulants of ability estimators using fallible item parameters." - Proofs, partial derivatives and tables. Economic Review, Otaru University of Commerce, 64 (2 & 3), 97-147. (Corrected version, March 2014); Ogasawara, H. (2014). Supplement II to the paper "Asymptotic cumulants of ability estimators using fallible item parameters." - Expectations. Economic Review, Otaru University of Commerce, 64 (4), 109-179. (Corrected version, April 2014) )


• Ogasawara, H. (2013). Asymptotic cumulants of the estimator of the canonical parameter in the exponential family. Journal of Statistical Planning and Inference, 143 (12), 2142-2150. (See also Ogasawara, H. (2014). Supplement to the paper "Asymptotic cumulants of the estimator of the canonical parameter in the exponential family". Economic Review, Otaru University of Commerce, 65 (2 & 3), 3-16.)


• Ogasawara, H. (2013). Asymptotic properties of the Bayes modal estimators of item parameters in item response theory. Computational Statistics, 28 (6), 2559-2583. (See also Ogasawara, H. (2014). Supplement to the paper "Asymptotic properties of the Bayes modal estimators of item parameters in item response theory". Economic Review, Otaru University of Commerce, 65 (1), 1-10.)


• Ogasawara, H. (2013). Estimation of ability with reduced asymptotic mean square error in item response theory. Journal of The Japan Statistical Society, 43 (2), 187-202.


• Ogasawara, H. (2014). Estimation of ability using pseudocounts in item response theory. Behaviormetrika, 41 (1), 131-146.


• Ogasawara, H. (2014). Optimization of the Gaussian and Jeffreys power priors with emphasis on the canonical parameters in the exponential family. Behaviormetrika, 41 (2), 195-223. (See also Ogasawara, H. (2015). An expository supplement to the paper "Optimization of the Gaussian and Jeffreys power priors with emphasis on the canonical parameters in the exponential family". Economic Review, Otaru University of Commerce, 66 (1), 1-10; Ogasawara, H. (2015). Errata for the paper "Optimization of the Gaussian and Jeffreys power priors with emphasis on the canonical parameters in the exponential family".)


• Ogasawara, H. (2015). Bias adjustment minimizing the asymptotic mean square error. Communications in Statistics - Theory and Methods, 44 (16), 3503-3522. (See also Ogasawara, H. (2015). An expository supplement to the paper "Bias adjustment minimizing the asymptotic mean square error" with errata. Economic Review, Otaru University of Commerce, 65 (4), 121-130.)


• Ogasawara, H. (2016). Asymptotic expansions for the estimators of Lagrange multipliers and associated parameters by the maximum likelihood and weighted score methods. Journal of Multivariate Analysis, 147, 20-37. (See also Ogasawara, H. (2015). Expository supplment I to the paper "Asymptotic expansions for the estimators of Lagrange multipliers and associated parameters by the maximum likelihood and weighted score methods". Economic Review, Otaru University of Commerce, 66 (2 & 3), 9-58; Ogasawara, H. (2016). Expository supplment II to the paper "Asymptotic expansions for the estimators of Lagrange multipliers and associated parameters by the maximum likelihood and weighted score methods". Economic Review, Otaru University of Commerce, 66 (4), 1-46.)


• Ogasawara, H. (2016). Bias correction of the Akaike information criterion in factor analysis. Journal of Multivariate Analysis, 149, 144-159.


• Ogasawara, H. (2016). Optimal information criteria minimizing their asymptotic mean square errors. Sankhya, B, 78 (1), 152-182.


• Ogasawara, H. (2016). Matching pseudocounts for interval estimation of binomial and Poisson parameters. Communications in Statistics - Theory and Methods, 45 (17), 5166-5178.


• Ogasawara, H. (2016). Asymptotic cumulants of some information criteria. Journal of the Japanese Society of Computational Statistics, 29, 1-25. (See also Ogasawara, H. (2016). Supplement I to the paper "Asymptotic cumulants of some information criteria" - Proofs and technical results. Economic Review, Otaru University of Commerce, 67 (2 & 3), 9-44; Ogasawara, H. (2017). Supplement II to the paper "Asymptotic cumulants of some information criteria" - Asymptotic cumulants of the studentized information criteria and Example 1. Economic Review, Otaru University of Commerce, 67 (4), 5-67; Ogasawara, H. (2017). Supplement III to the paper "Asymptotic cumulants of some information criteria" - Examples 2 and 3. Economic Review, Otaru University of Commerce, 68 (1), 1-65.)


• Ogasawara, H. (2017). Distribution-free properties of some asymptotic cumulants for the Mallows Cp and its modifications in usual and ridge regression. Behaviormetrika, 44, 25-56.


• Ogasawara, H. (2017). A family of the adjusted estimators maximizing the asymptotic predictive expected log-likelihood. Behaviormetrika, 44, 57-95.


• Ogasawara, H. (2017). Expected predictive least squares for model selection in covariance structures. Journal of Multivariate Analysis, 155, 151-164. (See also Ogasawara, H. (2017). Supplement to the paper "Expected predictive least squares for model selection in covariance structures" - Higher-order bias corrections and correlation structures. Economic Review, Otaru University of Commerce, 68 (2 & 3), 7-58.)


• Ogasawara, H. (2017). Accurate distributions of Mallows' Cp and its unbiased modifications with applications to shrinkage estimation. Journal of Statistical Planning and Inference, 184, 105-116. (See also Ogasawara, H. (2018). Supplement to the paper "Accurate distributions of Mallows' Cp and its unbiased modifications with applications to shrinkage estimation". Economic Review, Otaru University of Commerce, 68 (4), 1-7.)


• Ogasawara, H. (2017). Extensions of Pearson's inequality between skewness and kurtosis to multivariate cases. Statistics and Probability Letters, 130, 12-16.


• Ogasawara, H. (2017). Identified and unidentified cases of the three- and four-parameter models in item response theory. Behaviormetrika, 44, 405-423.


• Ogasawara, H. (2017). Predictive estimation of a covariance matrix and its structural parameters. Journal of the Japanese Society of Computational Statistics, 30, 45-63. (See also Ogasawara, H. (2018). Supplement to the paper "Predictive estimation of a covariance matrix and its structural parameters". Economic Review, Otaru University of Commerce, 69 (1), 1-17.)


• Ogasawara, H. (2018). A family of the information criteria using the phi-divergence for categorical data. Computational Statistics and Data Analysis, 124, 87-103. (See also Ogasawara, H. (2018). An expository supplement to the paper "A family of the information criteria using the phi-divergence for categorical data". Economic Review, Otaru University of Commerce, 69 (2 & 3), 11-29; Ogasawara, H. (2019). Errata for the paper "A family of the information criteria using the phi-divergence for categorical data" and its supplement". Unpublished document available at http://www.res.otaru-uc.ac.jp/~emt-hogasa/, http://hdl.handle.net/10252/00005922)


• Ogasawara, H. (2018). The inverse survival function for multivariate distributions and its application to the product moment. Statistics and Probability Letters, 142, 71-76.


• Ogasawara, H. (2019). Asymptotic biases of information and cross-validation criteria under canonical parametrization. Communications in Statistics - Theory and Methods, 48, 964-985.


• Ogasawara, H.(2019). The multiple Cantelli inequalities. Statistical Methods and Applications (Journal of the Italian Statistical Society), 28, 495-506. (See also Ogasawara, H. (2020a). An expository supplement to the paper "The multiple Cantelli inequalities": Higher-order moments for Mardia's bivariate Pareto distribution. Economic Review, Otaru University of Commerce, 70 (4), 1-22.; Ogasawara, H. (2020b). Errata of the paper "The multiple Cantelli inequalities". Unpublished document.)


• Ogasawara, H. (2019). The multivariate Markov and multiple Chebyshev inequalities. Communications in Statistics - Theory and Methods (online published).


• Ogasawara, H. (2020). Some improvements on Markov's theorem with extensions. The American Statistician, 74, 218-225.


• Ogasawara, H. (2020). On an unidentified fixed-effects three-parameter logistic model. Japanese Psychological Research, 62, 196-205.


• Ogasawara, H. (2020). Alternative expectation formulas for real-valued random vectors. Communications in Statistics - Theory and Methods, 49, 454-470.


• Ogasawara, H. (2020). An asymptotic equivalence of the cross-data and predictive estimators. Communications in Statistics - Theory and Methods, 49, 755-768. (See also Ogasawara, H. (2019). A supplement to the paper "An asymptotic equivalence of the cross-data and predictive estimators". Economic Review, Otaru University of Commerce, 70 (1), 1-8.)


• Ogasawara, H. (2020). The echelon Markov and Chebyshev inequalities. Communications in Statistics - Theory and Methods, 49, 1578-1591.


• Ogasawara, H. (2020). Asymptotic cumulants of the minimum phi-divergence estimator for categorical data under possible model misspecification. Communications in Statistics - Theory and Methods, 49, 2448-2465. (See also Ogasawara, H. (2019a). Supplement to the paper "Asymptotic cumulants of the minimum phi-divergence estimator for categorical data under possible model misspecification". Economic Review, Otaru University of Commerce, 70 (2 & 3), 65-86; Ogasawara, H. (2019b). Supplemental tables and R codes for the paper "Asymptotic cumulants of the minimum phi-divergence estimator for categorical data under possible model misspecification".)


• Ogasawara, H. (2021). A unified treatment of agreement coefficients and their asymptotic results: The formula of the weighted mean of weighted ratios. Journal of Classification, 38, 390-422. https://doi.org/10.1007/s00357-020-09366-1. (See also Ogasawara, H. (2020). Supplement to the paper "A unified treatment of agreement coefficients and their asymptotic results: The formula of the weighted mean of weighted ratios". Economic Review (Otaru University of Commerce), 71 (2 & 3), 35-50.)


• Ogasawara, H. (2021). Improvements of the Markov and Chebyshev inequalities using the partial expectation. Communications in Statistics - Theory and Methods, 51, 116-131. [Erratum as of January 6, 2021; The result of the lower partial moments in lines 1 to 3 on page 130 (page 15, online-published version, December 20, 2019) does not hold and should be deleted.]


• Ogasawara, H. (2021). A non-recursive formula for various moments of the multivariate normal distribution with sectional truncation. Journal of Multivariate Analysis (online published). https://doi.org/10.1016/j.jmva.2021.104729. (See also Ogasawara, H. (2021). Revised supplement to the paper "A non-recursive formula for various moments of the multivariate normal distribution with sectional truncation": monsec version 3.0. Unpublished document available at http://www.res.otaru-uc.ac.jp/~emt-hogasa/, http://hdl.handle.net/10252/00006018.)


• Ogasawara, H. (2021). Maximization of some types of information for unidentified item response model with guessing parameters. Psychometrika, 86, 544-563. https://doi.org/10.1007/s11336-021-09763-4. (See also Ogasawara, H. (2021). Supplement to the paper "Maximization of some types of information for unidentified item response model with guessing parameters").


• Ogasawara, H. (2022). Unified and non-recursive formulas for moments of the normal distribution with stripe truncation. Communications in Statistics - Theory and Methods, 51, 6834-6862. https://doi.org/10.1080/03610926.2020.1867742.


• Ogasawara, H. (2024). The multivariate t-distribution with multiple degrees of freedom. Communications in Statistics - Theory and Methods, 53 (1), 144-169. (online published, 31 May 2022). https://doi.org/10.1080/03610926.2022.2076122. Preprint available at https://doi.org/10.13140/RG.2.2.17979.90400.


• Ogasawara, H. (2022). The density of the sample correlations under elliptical symmetry with or without the truncated variance ratio. Journal of Multivariate Analysis (online published). https://doi.org/10.1016/j.jmva.2022.105152. (See also Ogasawara, H. (2023). Supplement to the paper "The density of the sample correlations under elliptical symmetry with or without the truncated variance ratio". Economic Review (Otaru University of Commerce), 74 (1), 1-15.) Preprint available at https://doi.org/10.13140/RG.2.2.35609.08807.


• Ogasawara, H. (2022). Expository moments for pseudo distributions. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3525-1.
  
  About this book  
  
  This book provides expository derivations for moments of a family of pseudo distributions, which is an extended family of distributions including the pseudo normal (PN) distributions recently proposed by the author. The PN includes the skew normal (SN) derived by A. Azzalini and the closed skew normal (CSN) obtained by A. Domínguez-Molina, G. González-Farías and A. K. Gupta as special cases. It is known that the CSN includes the SN and other various distributions as special cases, which shows that the PN has a wider variety of distributions. The SN and CSN have symmetric and skewed asymmetric distributions. However, symmetric distributions are restricted to normal ones. On the other hand, symmetric distributions in the PN can be non-normal as well as normal. In this book, for the non-normal symmetric distributions, the term "kurtic normal (KN)" is used, where the coined word "kurtic" indicates "mesokurtic, leptokurtic or platykurtic" used in statistics. The variety of the PN was made possible using stripe (tigerish) and sectional truncation in univariate and multivariate distributions, respectively. The proofs of the moments and associated results are not omitted and are often given in more than one method with their didactic explanations.


• Ogasawara, H. (2023). The multivariate power-gamma distribution using factor analysis models. In A. Okada, K. Shigemasu, R. Yoshino & S. Yokoyama (Eds.), Facets of behaviormerics: The 50th anniversary of the Behaviormetric Society (pp. 265-285). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-2240-6_13. Preprint at https://doi.org/10.13140/RG.2.2.11878.50240.


• Ogasawara, H. (2023). The Wishart distribution with two different degrees of freedom. Statistics and Probabiity Letters, 200, 109866 (online published). Preprint at https://doi.org/10.13140/RG.2.2.12315.34089.


• Ogasawara, H. (2023). On some known derivations and new ones for the Wishart distribution: A didactic. Journal of Behavioral Data Science, 3 (1), 34-58. https://doi.org/10.35566/jbds/v3n1/ogasawara. (See also Ogasawara, H. (2023). Supplement to the paper "On some known derivations and new ones for the Wishart distribution: A didactic". Economic Review (Otaru University of Commerce), 74 (2 & 3), 1-7.)


• Ogasawara, H. (2023). The distribution of the sample correlation coefficient under variance-truncated normality. Metrika (online publshed). (See also Ogasawara, H. (2023). Supplemnt to the paper "The distribution of the sample correlation coefficient under variance-truncated normality". Document available at the journal homepage of Metrika. https://doi.org/10.1007/s00184-023-00918-0.) Preprint at https://doi.org/10.13140/RG.2.2.19080.90883.


• Ogasawara, H. (2024). A didactic historical review of the distributions using the Bessel function: Some extensions with unification. To appear in Behaviormetrika.


• Ogasawara, H. (2024). Didactic proofs for the surface area of a sphere and the chi-square density. Economic Review (Otaru University of Commerce), 74 (4), 1-6.

Preprints

• Ogasawara, H. (2022). The multivariate power-gamma distribution. Preprint available at https://doi.org/10.13140/RG.2.2.11878.50240.


• Ogasawara, H. (2022). The distribution of the sample correlation coefficient under variance-truncated normality. Preprint available at https://doi.org/10.13140/RG.2.2.19080.90883.


• Ogasawara, H. (2022). The Wishart distribution with multiple degrees of freedom. Preprint available at https://doi.org/10.13140/RG.2.2.12315.34089.


• Ogasawara, H. (2022). A stochastic derivation of the surface area of the (n-1)-sphere. Preprint available at https://doi.org/10.13140/RG.2.2.28827.95528.


• Ogasawara, H. (2022). A simple geometric derivation of the chi-square density. Preprint available at https://doi.org/10.13140/RG.2.2.11211.87843.


• Ogasawara, H. (2023). On some known derivations and new ones for the Wishart distribution: A didactic (2nd version). Preprint available at https://doi.org/10.13140/RG.2.2.18053.42724.


• Ogasawara, H. (2023). The product distribution of more-than-two correlated normal variables: A didactic review with some new findings (2nd version). Preprint available at https://dx.doi.org/10.13140/RG.2.2.10995.71202.


• Ogasawara, H. (2023). A didactic historical review of the distributions using the Bessel function: Some extensions with unification. Preprint available at https://dx.doi.org/10.13140/RG.2.2.29305.65123.


• Ogasawara, H. (2024). Rephrasing the lengthy and involved proof of Kristof's theorem: A tutorial with some new findings. Preprint available at https://dx.doi.org/10.13140/RG.2.2.20026.98243.


• Ogasawara, H. (2024). Anti-ridge regression for communality estimation in factor analysis. Preprint available at https://dx.doi.org/10.13140/RG.2.2.22659.92966.