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Quantification, i.e., the task of training predictors of the class prevalence values in sets of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing…

Machine Learning · Computer Science 2023-10-16 Mirko Bunse , Alejandro Moreo , Fabrizio Sebastiani , Martin Senz

Comparison of two univariate distributions based on independent samples from them is a fundamental problem in statistics, with applications in a wide variety of scientific disciplines. In many situations, we might hypothesize that the two…

Methodology · Statistics 2021-07-08 Ted Westling , Kevin J. Downes , Dylan S. Small

A class of probability distributions is characterized via equalities in law between two order statistics shifted by independent exponential variables. An explicit formula for the quintile function of the identified family of distributions…

Probability · Mathematics 2011-07-26 M. Ahsanullah , V. B. Nevzorov , George P. Yanev

In some applications, an experimental unit is composed of two distinct but related subunits. The response from such a unit is $(X_{1}, X_{2})$ but we observe only $Y_1 = \min\{X_{1},X_{2}\}$ and $Y_2 = \max\{X_{1},X_{2}\}$, i.e., the…

Statistics Theory · Mathematics 2019-05-07 Jiahua Chen , Pengfei Li , Jing Qin , Tao Yu

The usual stochastic order and the likelihood ratio order between probability distributions on the real line are reviewed in full generality. In addition, for the distribution of a random pair $(X,Y)$, it is shown that the conditional…

Statistics Theory · Mathematics 2023-03-08 Lutz Duembgen , Alexandre Moesching

Consider a pair of cumulative distribution functions $F$ and $G$, where $F$ is unknown and $G$ is a known reference distribution. Given a sample from $F$, we propose tests to detect the convexity or the concavity of $G^{-1}\circ F$ versus…

Statistics Theory · Mathematics 2025-06-25 Tommaso Lando , Mohammed Es-Salih Benjrada

This manuscript investigates the stochastic comparisons of the second-order statistics from dependent and heterogeneous general semi-parametric family of distributions observations. Some sufficient conditions on the usual stochastic order…

Statistics Theory · Mathematics 2024-07-29 Guoqiang Lv

Organisms and algorithms learn probability distributions from previous observations, either over evolutionary time or on the fly. In the absence of regularities, estimating the underlying distribution from data would require observing each…

Statistical Mechanics · Physics 2024-12-10 William Bialek , Stephanie E. Palmer , David J. Schwab

We consider the problem of computing the joint distribution of order statistics of stochastically independent random variables in one- and two-group models. While recursive formulas for evaluating the joint cumulative distribution function…

Computation · Statistics 2018-12-24 Jonathan von Schroeder , Thorsten Dickhaus

Often it is assumed that a quantum state or a phase-space distribution must be normalizable. Here it is shown that even if it is not normalizable, one may be able to extract normalized observational probabilities from it.

High Energy Physics - Theory · Physics 2012-03-09 Don N. Page

We consider component-wise equivariant estimation of order restricted location/scale parameters of a general bivariate distribution under quite general conditions on underlying distributions and the loss function. This paper unifies various…

Statistics Theory · Mathematics 2022-07-05 Naresh Garg , Neeraj Misra

Researchers are often interested in drawing inferences regarding the order between two experimental groups on the basis of multivariate response data. Since standard multivariate methods are designed for two-sided alternatives, they may not…

Statistics Theory · Mathematics 2013-03-11 Ori Davidov , Shyamal Peddada

We prove uniform estimates for the expected value of averages of order statistics of bivariate functions in terms of their largest values by a direct analysis. As an application, uniform estimates for the expected value of averages of order…

Probability · Mathematics 2018-10-03 Richard Lechner , Markus Passenbrunner , Joscha Prochno

When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the…

Machine Learning · Statistics 2022-08-09 Conrad D. Hougen , Lance M. Kaplan , Federico Cerutti , Alfred O. Hero

In this paper the elicitation of probabilities from human experts is considered as a measurement process, which may be disturbed by random 'measurement noise'. Using Bayesian concepts a second order probability distribution is derived…

Artificial Intelligence · Computer Science 2013-04-05 Gerhard Paaß

In the past couple of years, various approaches to representing and quantifying different types of predictive uncertainty in machine learning, notably in the setting of classification, have been proposed on the basis of second-order…

Machine Learning · Computer Science 2023-12-05 Yusuf Sale , Viktor Bengs , Michele Caprio , Eyke Hüllermeier

Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action…

General Economics · Economics 2022-05-03 Stefan Rass , Sandra König , Stefan Schauer

We investigate stochastic comparisons between exponential family distributions and their mixtures with respect to the usual stochastic order, the hazard rate order, the reversed hazard rate order, and the likelihood ratio order. A general…

Statistics Theory · Mathematics 2009-10-05 Yaming Yu

In this article, we discuss a bivariate distribution whose conditionals are univariate binomial distributions and the marginals are not binomial that exhibits negative correlation. Some useful structural properties of this distribution…

Methodology · Statistics 2023-01-10 Indranil Ghosh , Filipe Marques , Subrata Chakraborty

Let $X_1, X_2,\ldots, X_n$ (resp. $Y_1, Y_2,\ldots, Y_n$) be independent random variables such that $X_i$ (resp. $Y_i$) follows generalized exponential distribution with shape parameter $\theta_i$ and scale parameter $\lambda_i$ (resp.…

Applications · Statistics 2016-01-18 Amarjit Kundu , Shovan Chowdhury , Asok K. Nanda , Nil Kamal Hazra
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