Some classical idea of ombinus or global testing methods have been extended to high dimension dramatically for the last two decades. One typical problem is the extension of Hotelling’s T test for testing equality of mean vector to high dimensional case. Our research in testing in high dimension includes hihg dimensional Behrens-Fisher problem and simultaneous test of mean vectors and covariance matrces.
In meta analysis, testing the homogeneity of effect sizes or distributions from many different studies is important before we decide to combine all studies or consider random effect model to explain the variations among different studies. Our research includes a variety of hypothesis testing problems such as high dimensional testing or some classifical testing problems in meta analysis.
Cao, M-X, Sun, P. and Park, J. (2021) Simultaneous test of mean vector and covariance matrix in high dimensional settings, Journal of Statistical Planning and Inference, 212, 141-152. Article Link
Cao, M-X., Park, J. and He, D. (2019) A test for the k sample Behrens-Fisher problem in high dimensional data, Journal of Statistical Planning and Inference, 201, 86-102. Article Link
Park, J. (2019) Testing homogeneity of proportions from sparse binomial data with a large number of groups, Annals of the Institute of Statistical Mathematics, 71, 505-535. Article Link
Park, J. and *Gauran, I.I. (2019), Testing the homogeneity of risk differences with sparse count data, Statistics:A Journal of Theoretical and Applied Statistics. Article Link
*Ayyala, D.N., Park, J. and Roy, A. (2017) Mean vector testing for high-dimensional dependent observations, Journal of Multivariate Analysis, 153, 136-155. Article Link
*Choi, S. and Park, J. (2014). Plug-in tests for non-equivalence of means of independent normal populations, Biometrical Journal, 56, 1016-1034.
Park, J., Sinha, B.K. and Shah, A. (2013). Testing Interval Hypotheses for Scale Parameters in Gamma Distributions, Statistics and Probability Letters, 83, 2172-2178.
Park, J. and *Ayyala, D.N. (2013). A test for the mean vector in large dimension and small sample, Journal of Statistical Planning and Inference, 143, 929-943.
Park, J. and Park, D. (2012). Testing the equality of a large number of normal population means, Computational Statistics and Data Analysis, 46, 1131-1149.
Greenshtein, E. and Park, J. (2012). Robust test for detecting a signal in a high dimensional sparse normal vector, Journal of Statistical Planning and Inference, 142, 1445-1456.
Park, J. (2010). The Generalized P-value in one-sided testing in two sample multivariate normal populations, Journal of Statistical Planning and Inference, 140, 1044-1055.