A Note on the Regression of Order Statistics

This paper examines the relationships between the mean residual life functions of parallel and k-out-of-n systems with the regression of order statistics. Using these relationships, the results and properties about the mean residual life function of those systems can be used for the regression of order statistics and vice versa. Finally, the paper proposes a definition for the mean residual life function of a k-out-of-n system when the number of failed components of the system is known.     

Posterior Regret Γ-Minimax Estimation and Prediction with Applications on k-Records Data Under Entropy Loss Function

Robust Bayesian analysis is connected with the effect of changing a prior within a class Γ instead of being specified exactly. The multiplicity of prior leads to a collection or a range of Bayes actions. It is interesting not only to investigate the range of estimators but also to recommend the optimal procedures. In this article, we deal with posterior regret Γ-minimax (PRGM) estimation and prediction of an unknown parameter θ and a value of a random variable Y under entropy loss function. Applications for k-records such as estimation and prediction problems are discussed.     

A Bayesian Inference and Stochastic Dynamic Programming Approach to Determine the Best Binomial Distribution

In this research, we employ Bayesian inference and stochastic dynamic programming approaches to select the binomial population with the largest probability of success from n independent Bernoulli populations based upon the sample information. To do this, we first define a probability measure called belief for the event of selecting the best population. Second, we explain the way to model the selection problem using Bayesian inference. Third, we clarify the model by which we improve the beliefs and prove that it converges to select the best population. In this iterative approach, we update the beliefs by taking new observations on the populations under study. This is performed using Bayesian rule and prior beliefs. Fourth, we model the problem of making the decision in a predetermined number of decision stages using the stochastic dynamic programming approach. Finally, in order to understand and to evaluate the proposed methodology, we provide two numerical examples and a comparison study by simulation. The results of the comparison study show that the proposed method performs better than that of Levin and Robbins (1981 Levin , B. , Robbins , H. ( 1981 ). Selecting the highest probability in Binomial or multinomial trials . Proc. Nat. Acad. Sci. USA 78 : 4663 – 4666 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) for some values of estimated probability of making a correct selection.     

Comparison among non parametric prediction intervals of order statistics

Abstract

In this article, we are interested in conducting a comparison study between different non parametric prediction intervals of order statistics from a future sample based on an observed order statistics. Typically, coverage probabilities of well-known non parametric prediction intervals may not reach the preassigned probability levels. Moreover, prediction intervals for predicting future order statistics are no longer available in some cases. For this, we propose different methods involving random indices and fractional order statistics. In each case, we find the optimal prediction intervals. Numerical computations are presented to assess the performances of the so-obtained intervals. Finally, a real-life data set is presented and analyzed for illustrative purposes.     

Lindley first-order autoregressive model with applications

ABSTRACT

A new stationary first-order autoregressive process with Lindley marginal distribution, denoted as LAR(1) is introduced. We derive the probability function for the innovation process. We consider many properties of this process, involving spectral density, some multi-step ahead conditional measures, run probabilities, stationary solution, uniqueness and ergodicity. We estimate the unknown parameters of the process using three methods of estimation and investigate properties of the estimators with some numerical results to illustrate them. Some applications of the process are discussed to two real data sets and it is shown that the LAR(1) model fits better than other known non Gaussian AR(1) models.     

On Properties of Percentile Bootstrap Confidence Intervals for Period Using Periodogram

Periodic functions have many applications in astronomy. They can be used to model the magnitude of light intensity of the period variable stars that their brightness vary with time. Because the data related to the astronomical applications are commonly observed at the time points that are not regularly spaced, the use of the periodogram as a good tool for estimating period is highlighted. Our bootstrap inference about period is based on maximizing the periodogram and consists of percentile two-sided bootstrap confidence intervals construction for the true period. We also obtain their coverage levels theoretically, and discuss the benefit of double-bootstrap confidence intervals for the parameter by which the coverage levels are substantially improved. Precisely, we show that the coverage error of single-bootstrap confidence intervals is of order n ^?1, decreasing to order n ^?2 when applying double-bootstrap methods. The simulation study given here is a numerical assessment of the theoretical work.     

Exploring the co-movements between COVID-19 pandemic and international air traffic: A global perspective based on wavelet analysis

The travel and tourism industry was one of the fastest-growing industries before the onset of the COVID-19 pandemic. However, to avoid COVID-19 spread, the government authorities imposed strict lockdown and international border restrictions except for some emergency international flights that badly hit the travel and tourism industry. The study explores the nexus between international air departures and the COVID-19 pandemic in this strain. We use a novel wavelet coherence approach to dissect the lead and lag relationships between international flight departures and COVID-19 deaths from January 2020 to September 2020 (COVID-19 first wave period). The results reveal that international flights cause the spread of COVID-19 spread during May 2020 to June 2020 worldwide. The overall findings suggest asymmetries between daily international flight departures and COVID-19 deaths globally at different time-frequency periods due to uncertainty surrounding the COVID-19 pandemic. The study will be conducive for the policymakers to control the upsurge of COVID-19 spread worldwide.     

Public Policy Diffusion: A Scoping Review

Public Organization Review - The purpose of this scoping review is to describe studies focusing on policy diffusion to elicit an overall picture of the research in this field. Although various...     

Investment Areas to Enhance Public Employee Resilience during the Coronavirus Disease 2019 (COVID-19): Evidence from Iran

Public Organization Review - This study aims to investigate the areas of investment that the human resources management of the Iranian public sector has invested in for improving employees'...