박준용 교수

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

High‐dimensional classification has challenges mainly due to the singularity issue of the sample covariance matrix. In this work, we propose a different approach to get a more reliable sample covariance matrix by adjusting the estimated eigenvalues. This procedure also brings us a nonsingular matrix as a by‐product. We improve the optimization procedure to obtain a linear classifier by incorporating the adjusted sample covariance matrix and a shrinkage mean vector into the original optimization