Kubokawa,T.,and Srivastava,M.S. (2010), An Empirical Bayes Information Criteria for Selecting Variables in Linear Mixed Models. J. Jour. Japan Statist. Soc: To appear

Srivastava,M.S.(2010), Controlling the Avearage False Dicovery in Large-Scale Mutiple Testing. Journal of astatistical Resarch,vol 44: To appear

Srivastava,M.S. and Kubokawa,T.(2010), Conditional Information Criteria for Selecting Variables in Linear Regression Mixed Model. J. Multivariate Analysis: 101,1970-1980

Srivastava, M.S,and Yanagihara,H,(2010),Testing the equality of several covariance matrices with fewer observations than the dimension. J. Multivariate Analysis, No.101, 1319-1329.

Yamamura,Y., Yanagihara,H. and Srivastava, M.S.(2010). Variable Selection in Multivariate Linear Regression Models with Fewer observations than the dimension. Japanese J. Applied Statistics, vol 39, No.1, 1-19.

Srivastava,M.S, and Dolatabadi,M.(2009). Multiple imputation and other resampling schemes for inputing missing observations. J. Multivariate Analysis, No.100, 1919-1937.

Srivastava,M.S.(2009), A Review of Multivariate Theory For High Dimensional Data with Fewer Observations. Advances in Multivariate Statistical Methods, 9 Editor Ashis SenGupta,25-52.

Srivastava, M.S. (2009), A Test of the Mean Vector with Fewer Observations than the Dimension under non-normality. J. Multivariate Analysis, No.100, 518-532.

Srivastava, M.S. and Kubokawa, T. (2008) Akaike Information Criterion for Selecting Components of the mean Vector in High Dimensional Data with Fewer Observations. J. Japan Statist. Soc, No.2, 259-283.

Srivastava, M.S., von Rosen, T. and von Rosen, D. (2008) Models with a Kronecker Product Covariance Structure: Estimation and Testing. Mathematical Methods of Stiatitics,17,No.4,357-370

Kubokawa, T., and Srivastava,M.S.(2008), Estimation Of The Precision Matrix Of A singular Wishart Distribution And Its Applications In High Dimensional Data  J. Multivariate Analysis,99,1906-1928

Srivastava,M.S.and Du, M. (2008), A Test for the Mean Vector with Fewer Observations than the Dimension. Journal of Multivariate Analysis, 99,386-402

Srivastava,M.S.and Kubokawa,T.(2007). Emperical Bayes Regression Analysis With Many Regressors But Fewer Observations.J. Statist. Plann. Inf. 137, 3778-3792

Srivastava,M.S. and Kubokawa, T. (2007), Comparison of Discrimination Methods for High Dimensional Data, Journal of Japan Sta. Soc. 37, No.1, 123-134

Srivastava,M.S.(2007), Multivariate Theory For Analyzing High Dimensional Data Journal of Japan Sta. Soc. 37, 53-86

Srivastava,M.S.(2006), Minimum Distance Classification Rules For High Dimensional Data: Journal of Multivariate Analysis, 97, 2057-2070

Srivastava,M.S., and Fujikoshi,Y.(2006),  Multivariate Analysis Of Variance With Fewer Observations Than The Dimension: Journal of Multivariate Analysis, 97, 1927-1940

Srivastava,M.S. and Saleh, A.K.M.E. (2005), Estimation of the mean vector of a multivariate normal distribution: Subspace Hypothesis. Jour. Multivariate Analysis, 96,55-72.

Srivastava,M.S. and Kubokawa, (2005), Minimax Multivariate Empirical Bayes Estimators under Multicollinearity. Jour. Multivariate Analy., 93, 394-416.