in this paper, we generalize a theorem of shao [12] by assuming that is a sequence of linear negatively dependent random variables. also, we extend some theorems of chao [6] and thrum [14]. it is shown by an elementary method that for linear negatively dependent identically random variables with finite -th absolute moment the weighted sums converge to zero as where and is an array of real numbe...

Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hash codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in s...

Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate respon...

In prior work [4], Anderson et al. introduced a new problem, the rankability problem, which refers to dataset's inherent ability produce meaningful ranking of its items. Ranking is fundamental data science task with numerous applications that include web search, mining, cybersecurity, machine learning, and statistical lea...

This note is concerned with the asymptotic properties of pairwise likelihood estimation procedures for linear time series models. The latter includes ARMA as well as fractionally integrated ARMA processes, where the fractional integration parameter d < 0.5. In some cases, including AR(1) processes and longmemory processes with d < 0.25, the loss in efficiency in using pairwise likelihood method...