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In this paper, we propose to consider the dependence structure of the trade/no trade categorical sequence of individual illiquid stocks returns. The framework considered here is wide as constant and time-varying zero returns probability are allowed. The ability of our approach in highlighting illiquid stock's features is underlined for a variety of situations. More specifically, we show that long-run effects for the trade/no trade categorical sequence may be spuriously detected in presence of a non-constant zero returns probability. Monte Carlo experiments, and the analysis of stocks taken from the Chilean financial market, illustrate the usefulness of the tools developed in the paper. [ABSTRACT FROM AUTHOR]

The local limit theorem (LLT) is one of the well-known limit theorems which can be used to estimate the probability at a particular point of a random variable. In this paper, we generalize weighted sums which are introduced by Giuliano and Weber and give the LLT with explicit error bounds. [ABSTRACT FROM AUTHOR]

In this paper, we consider a risk model with heavy-tailed claims and Brownian perturbation. Assuming that the distribution function of claim-size is subexponential, and the arrival process of claims is a non stationary process satisfying the principle of large deviation, the asymptotic formula for the ruin probability of this risk model at random time is obtained. [ABSTRACT FROM AUTHOR]

For a model incorporating the effect of a common environment on several components of a system, a multivariate Lomax distribution (MLD) is generally considered by mixing exponential variables. Objective Bayesian has very good frequentist properties and provides a moderate solution for the prior elicitation which is one of important and difficult issues on Bayesian analysis. In this paper, we develop noninformative priors, such as the probability matching priors and reference priors, for the parameters of the MLD. We proved that a reference prior for the shape parameter is a first-order probability matching prior, but the reference priors for the scale parameters do not satisfy the first-order matching criterion. In addition, a second-order probability matching prior does not exist for all parameters. We also presented the conditions that make the posterior distributions for the general prior, including the probability matching prior and reference priors, to be proper. In particular, Jeffreys' prior and probability matching priors for all parameters give proper posteriors, whereas reference priors for scale parameters give improper posteriors. [ABSTRACT FROM AUTHOR]

In this paper, we propose approximations for the misclassification probabilities in linear discriminant analysis when the group means have a bilinear regression structure. First, we give a unified location and scale mixture expression of the standard normal distribution for the linear discriminant function. Then, the estimated approximations of misclassification are obtained for the three cases: unweighted case, weighted known covariance matrix Σ , and weighted unknown Σ. It has to be pointed out that larger p is better for classification when Σ is known, also in unweighted case. In the case Σ is unknown, we gain more information if fewer repeated measurements are used compared to when many repeated measurements closer to the number of included sample size are used. Furthermore, the accuracies of the proposed approximations are checked numerically by conducting a Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

In many statistical analysis, a finite population may contain a large proportion of zero-and-one values that make the population distribution severely skew. Confidence intervals based on a normal approximation (NA) for such data may have low coverage probabilities. In this paper, we apply the methods of jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL) to discuss the confidence intervals for the mean of zero-and-one inflated population. Asymptotic distributions of the likelihood-type statistics are studied. Simulations are conducted to compare coverage probabilities with other methods under different distributions. Real data is given to illustrate the procedure of proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate k-out-of-n system with n independent parallel subsystems comprising dependent components drawn from a heterogeneous population consisting of m different subpopulations. The components within each subpopulation are assumed to be dependent, while the subpopulations are independent of each other. We also assume that the subpopulations have different Archimedean copulas for their dependence. Under this setup, we discuss the k-out-of-n systems reliability by comparing two selection probabilities or two allocation policies in the sense of majorization order. In addition, we present some numerical examples to illustrate all the results established here. Finally, some concluding remarks are made. [ABSTRACT FROM AUTHOR]

In mathematical terms, Graph HT ht (1) Song and Wang ([2]) provided two simple proofs of formula (1), and as they pointed out in their paper (immediately after Proposition 1), they have apparently not seen this formula in the literature before proving themselves. Although, in the continuous case, it seems that the use of Graph HT ht instead of Graph HT ht is not known in the common literature. [Extracted from the article]