Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares o...

The purpose of this paper is to introduce the concept of pairwise F-closedness in bitopological spaces. This space contains both of pairwise strongcompactness and pairwise S-closedness and contained in pairwise quasi H-closedness. The characteristics and relationships concerning this new class of spaces with other corresponding types are established. Moreover, several of its basic and important...

OBJECTIVES Unemployment and temporary employment are known to impact psychological health. However, the extent to which the effect is altered by migration-related and sociodemographic determinants is less clear. The purpose of this study was to investigate whether the association between employment status and psychological distress differs between immigrants and Swedish-born and to what extent,...

Parameter estimation for nonignorable nonresponse data is a challenging issue as the missing mechanism is unverified in practice and the parameters of response probabilities need to be estimated. This article aims at applying the empirical likelihood to construct the confidence intervals for the parameters of interest in linear regression models with nonignorable missing response data and the n...

In financial time series analysis, symmetric and asymmetric GARCH models have become essential for measuring the characteristics of economic volatility. this article, we propose consistency asymptotic normality properties self-weighted quasi-maximum likelihood estimation without assuming existence second moment moving average model with a class error. Numerical simulation shows that parameter p...

In general, the similarity measure is indispensable for most traditional spectral clustering algorithms since these algorithms typically begin with the pairwise similarity matrix of a given dataset. However, a general type of input for most clustering applications is the pairwise distance matrix. In this paper, we propose a distance-based spectral clustering method which makes no assumption on ...