Junyong Park

In this paper we discuss the well known multivariate Behrens-Fisher problem which deals with testing the equality of two normal mean vectors under heteroscedasticity of dispersion matrices. Some existing tests are reviewed and a new test based on Roy's union-intersection principle coupled with the generalized P-value is proposed. The tests are compared with respect to size and power based on simulation, and applied to a few useful data sets. AMS (2000) Subject Classification : 62H10, 62H15.

In this article, we study the methods for two-sample hypothesis testing of high-dimensional data coming from a multivariate binary distribution. We test the random projection method and apply an Edgeworth expansion for improvement. Additionally, we propose new statistics which are especially useful for sparse data. We compare the performance of these tests in various scenarios through simulations run in a parallel computing environment. Additionally, we apply these tests to the 20 Newsgroup data

The Behrens–Fisher problem occurs when testing the equality of means of two normal distributions without the assumption that the two variances are equal. This paper presents approaches based on the exact and near-exact distributions for the test statistic of the Behrens–Fisher problem, depending on different combinations of even or odd sample sizes. We present the exact distribution when both sample sizes are odd and the near-exact distribution when one or both sample sizes are even. The near-ex

As mobile & edge devices are getting powerful, on-device deep learning is becoming a reality. However, there are still many challenges for deep learning edge inferences, such as limited resources such as computing power, memory space, and energy. To address these challenges, model compression such as channel pruning, low rank representation, network quantization, and early exiting has been introduce to reduce the computational load of neural networks at a whole. In this paper, we propose an impr

Abstract We propose a new method in high‐dimensional classification based on estimation of high‐dimensional mean vector under unknown and unequal variances. Our proposed method is based on a semi‐parametric model that combines nonparametric and parametric models for mean and variance, respectively. Our proposed method is designed to be robust to the structure of the mean vector, while most existing methods are developed for some specific cases such as either sparse or non‐sparse case of the mean