Prediction intervals for time series models with trend via sieve bootstrap

This paper discusses method for constructing the prediction intervals for time series model with trend using the sieve bootstrap procedure. Gasser–Müller type of kernel estimator is used for trend estimation and prediction. The boundary modification of the kernel is applied to control the edge effect and to construct the predictor of a trend.     

Electronic Health Record Breaches as Social Indicators

Social Indicators Research - This study presents compelling social indicators of such magnitude that they cannot be ignored. The statistical evidence shows that data breaches of electronic health...     

On exact strong laws of large numbers for ratios of random variables and their applications

Abstract

We study the almost sure convergence of weighted sums of ratios of independent random variables satisfying some general, mild conditions. The obtained results are applied to exact laws for order statistics. An exact law for independent random variables which are nonidentically distributed is also proved and applied to ratios of adjacent order statistics for a sample of uniformly distributed random variables.     

Social Enterprises’ Hybridity in the Concept of Institutional Logics: Evidence from Polish NGOs

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Social enterprises have gained wide recognition as a tool for solving social and environmental problems. They generate new...     

Upper and lower bounds on the variances of linear combinations of the kth records

We describe a method of determining upper bounds on the variances of linear combinations of the kth records values from i.i.d. sequences, expressed in terms of variances of parent distributions. We also present conditions for which the bounds are sharp, and those for which the respective lower ones are equal to zero. A special attention is paid to the case of the kth record spacings, i.e. the differences of consecutive kth record values.     

The development of gaze following in monolingual and bilingual infants: A multi-laboratory study

Determining the meanings of words requires language learners to attend to what other people say. However, it behooves a young language learner to simultaneously encode relevant non-verbal cues, for example, by following the direction of their eye gaze. Sensitivity to cues such as eye gaze might be particularly important for bilingual infants, as they encounter less consistency between words and objects than monolingual infants, and do not always have access to the same word-learning heuristics (e.g., mutual exclusivity). In a preregistered study, we tested the hypothesis that bilingual experience would lead to a more pronounced ability to follow another's gaze. We used a gaze-following paradigm developed by Senju and Csibra (Current Biology, 18, 2008, 668) to test a total of 93 6- to 9-month-old and 229 12- to 15-month-old monolingual and bilingual infants, in 11 laboratories located in 8 countries. Monolingual and bilingual infants showed similar gaze-following abilities, and both groups showed age-related improvements in speed, accuracy, frequency, and duration of fixations to congruent objects. Unexpectedly, bilinguals tended to make more frequent fixations to on-screen objects, whether or not they were cued by the actor. These results suggest that gaze sensitivity is a fundamental aspect of development that is robust to variation in language exposure.     

On robust Bayesian estimation under some asymmetric and bounded loss function

The problem of Bayesian and robust Bayesian estimation with some bounded and asymmetric loss function ABL is considered for various models. The prior distribution is not exactly specified and covers the conjugate family of prior distributions. The posterior regret, most robust and conditional Γ-minimax estimators are constructed and a preliminary comparison with square-error loss and LINEX loss is presented.     

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

The spectral measure plays a key role in the statistical modeling of multivariate extremes. Estimation of the spectral measure is a complex issue, given the need to obey a certain moment condition. We propose a Euclidean likelihood-based estimator for the spectral measure which is simple and explicitly defined, with its expression being free of Lagrange multipliers. Our estimator is shown to have the same limit distribution as the maximum empirical likelihood estimator of Einmahl and Segers (2009 Einmahl , J. H. J. , Segers , J. ( 2009 ). Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution . Ann. Statist. 37 ( 5B ): 2953 – 2989 .[Crossref], [Web of Science ®] , [Google Scholar]). Numerical experiments suggest an overall good performance and identical behavior to the maximum empirical likelihood estimator. We illustrate the method in an extreme temperature data analysis.