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The purpose of this note is to point out a problem related to stress-strength models in some published papers. This difficulty has occurred in the asymptotic variance of the maximum likelihood estimator of the stress-strength parameter. The asymptotic variance will be studied in general and then in the mentioned papers. Finally, the correct version of asymptotic variance is computed. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor addressing a typographical error in the formulas for the double Pareto-lognormal distribution and the normal-Laplace distribution. The authors clarify that the formulas themselves are correct, but there was an error in the expression of the Mills ratio in the correction by Amini and Rabbani. The authors provide the correct formulas for both distributions and offer further explanation upon request. The authors are affiliated with Juraj Dobrila University of Pula in Croatia. [Extracted from the article]

In this paper new filters for removing unspecified form of heteroscedasticity are proposed. The filters build on the assumption that the variance of a pre-whitened time series can be viewed as a latent stochastic process by its own. This makes the filters flexible and useful in many situations. A simulation study shows that removing heteroscedasticity before fitting a model leads to efficiency gains and bias reductions when estimating the parameters of ARMA models. A real data study shows that pre-filtering can increase the forecasting precision of quarterly US GDP growth. [ABSTRACT FROM AUTHOR]

Confidence intervals Since the I t i test and the test based on the I K i SB 1 sb statistic are the same, the confidence intervals (CIs) that are obtained by inverting these two tests should be the same. Thus, if I t SB q sb i denotes the 100 I q i percentile of the I t i statistic, then Graph HT ht is the 100 I q i percentile of the I K i SB 1 sb statistic. [Extracted from the article]

Note, for instance, that the probabilities given by Castellares et al. ([1]) in page 4 are the same than those shown in expression (9.115) in the book by Johnson et al. ([3]), with the change of parameters Graph HT ht and Graph HT ht Neyman type A (NTA) distribution is frequently used in Biology, Biodosimetry, Environmental Sciences, Epidemiology, etc. A count random variable I X i is said to follow a stopped-sum Poisson, compound Poisson, multiple Poisson or clustered-Poisson distribution, if it can be represented as Graph HT ht where I N i is a Poisson random variable with parameter Graph HT ht and Graph HT ht are independent, identically distributed random variables that are also independent of I N i . [Extracted from the article]

In this paper, a new criterion is constructed for testing hypothesis about covariance function of Gaussian stationary stochastic process with an unknown mean. This criterion is based on the fact that we can estimate the deviation of covariance function from its estimator with a given accuracy and reliability in Lp metric. [ABSTRACT FROM AUTHOR]

The paper of Erich Lehmann (Lehmann 1953) is renowned for its ground-breaking contribution to rank tests within the area of nonparametric statistics. This work, in all likelihood, is known to every statistician. But, what is likely not known to many are some of the novel concepts and models that this paper succinctly introduced to the area of distribution theory. This, unfortunately, has led to some of these being rediscovered in the literature and then being referred to under different names. The purpose of this note is, therefore, two-fold: first to explain the key models that are contained in the mentioned work of Erich Lehmann, and second to point out how some of the known models discussed in the distribution theory and stochastic modeling literature are indeed present either explicitly or implicitly in the paper of Lehmann. [ABSTRACT FROM AUTHOR]

A bivariate zero-inflated Poisson control chart for rare events in an industrial process was presented in the paper of Fatahi et al. (2012). They used a specific copula to address the dependency between the bivariate counts. The copula that they provided did not satisfy the criteria for a copula when the correlation parameter is negative and they did not calculate the upper control limit for the sum of events correctly. We provide proper copula's and upper control limits and also discuss their choice of parameter estimators. We illustrate our results with simulations. [ABSTRACT FROM AUTHOR]