Empirical likelihood-based tests for stochastic ordering
Hammou El Barmi,Ian W. McKeague +1 more
Reads0
Chats0
TLDR
In this paper, an empirical likelihood approach to test for the presence of stochastic ordering among univariate distributions based on independent random samples from each distribution is proposed. But the approach is used to compare the lengths of rule of Roman Emperors over various historical periods, including the decline and fall of the empire.Abstract:
This paper develops an empirical likelihood approach to testing for the presence of stochastic ordering among univariate distributions based on independent random samples from each distribution. The proposed test statistic is formed by integrating a localized empirical likelihood statistic with respect to the empirical distribution of the pooled sample. The asymptotic null distribution of this test statistic is found to have a simple distribution-free representation in terms of standard Brownian bridge processes. The approach is used to compare the lengths of rule of Roman Emperors over various historical periods, including the “decline and fall” phase of the empire. In a simulation study, the power of the proposed test is found to improve substantially upon that of a competing test due to El Barmi and Mukerjee.read more
Citations
More filters
Journal ArticleDOI
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Proceedings ArticleDOI
Deep Dominance - How to Properly Compare Deep Neural Models
TL;DR: The criteria for a high quality comparison method between DNNs is defined, and it is shown that the proposed test meets all criteria while previously proposed methods fail to do so.
Book
Statistical Significance Testing for Natural Language Processing
TL;DR: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms and has become rare to see an NLP paper, in the last decade.
Journal ArticleDOI
Combining isotonic regression and em algorithm to predict genetic risk under monotonicity constraint.
TL;DR: In this paper, a method that combines Expectation-Maximization and isotonic regression to estimate the cumulative risk across the entire support was developed, which satisfies self-consistent estimating equations, and has high power in detecting differences between the cumulative risks of different populations.
Journal ArticleDOI
Efficiency of Exponentiality Tests Based on a Special Property of Exponential Distribution
TL;DR: In this article, the goodness-of-fit tests for exponentiality based on a particular property of exponential law are constructed and conditions of local optimality of new statistics in Bahadur sense are discussed.
References
More filters
Book
Convergence of Probability Measures
TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
Journal ArticleDOI
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Journal ArticleDOI
Empirical likelihood ratio confidence intervals for a single functional
TL;DR: In this article, the empirical distribution function based on a sample is used to define a likelihood ratio function for distributions, which can be used to construct confidence intervals for the sample mean, for a class of M-estimates that includes quantiles, and for differentiable statistical functionals.
Book
Order restricted statistical inference
TL;DR: In this paper, a set of multinomial parameters are derived about distributions subject to shape restrictions, and a conditional expectation given a sigma-lattice is given in a more general setting.
Journal ArticleDOI
Empirical Likelihood Ratio Confidence Regions
TL;DR: In this article, an empirical likelihood ratio function is defined and used to obtain confidence regions for vector valued statistical functionals, and an effective method is presented for computing empirical profile likelihoods for the mean of a vector random variable.
Related Papers (5)
Inferences Under a Stochastic Ordering Constraint: The k-Sample Case
Hammou El Barmi,Hari Mukerjee +1 more