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Dispersion is a fundamental concept in statistics, yet standard approaches - especially via stochastic orders - face limitations in the discrete setting. In particular, the classical dispersive order, well-established for continuous…

Methodology · Statistics 2025-11-11 Andreas Eberl , Bernhard Klar , Alfonso Suárez-Llorens

A new method based on the rejection sampling for finding statistical tests is proposed. This method is conceptually intuitive, easy to implement, and applicable for arbitrary dimension. To illustrate its potential applicability, three…

Methodology · Statistics 2026-03-11 Markku Kuismin

We present a sorting algorithm for the case of recurrent random comparison errors. The algorithm essentially achieves simultaneously good properties of previous algorithms for sorting $n$ distinct elements in this model. In particular, it…

Data Structures and Algorithms · Computer Science 2017-09-22 Barbara Geissmann , Stefano Leucci , Chih-Hung Liu , Paolo Penna

Let $X_1, \ldots, X_n$ be independent non-negative random variables with cumulative distribution functions $F_1,F_2,\ldots,F_n$, each satisfying certain (rather mild) conditions. We show that the median of $k$-th smallest order statistic of…

Probability · Mathematics 2019-01-23 Alexander E. Litvak , Konstantin Tikhomirov

When a planner must decide whether it has enough evidence to make a decision based on probability, it faces the sample size problem. Current planners using probabilities need not deal with this problem because they do not generate their…

Artificial Intelligence · Computer Science 2013-03-26 Nathaniel G. Martin , James F. Allen

``Behind every limit theorem, there is an inequality'' said Kolmogorov. We say ``for every inequality, there is an approximate inequality under approximate regularity conditions.'' Suppose $X, X'$ are independent and identically distributed…

Statistics Theory · Mathematics 2026-04-17 Manit Paul , Arun Kumar Kuchibhotla

There is a growing body of work on sorting and selection in models other than the unit-cost comparison model. This work is the first treatment of a natural stochastic variant of the problem where the cost of comparing two elements is a…

Data Structures and Algorithms · Computer Science 2007-10-02 Stanislav Angelov , Keshav Kunal , Andrew McGregor

If the prior probability distributions of all possible hypothetical true means and all possible observed means of a continuous variable are conditional on the universal set of all numbers (i.e., before the nature of a study is known and a…

Methodology · Statistics 2025-06-05 Huw Llewelyn

By using the matrix formulation of the two-step approach to distributions of patterns in random sequences, recurrence and explicit formulas for the generating functions of successions in random permutations of arbitrary multisets are…

Combinatorics · Mathematics 2024-05-06 Yong Kong

We propose and analyze a specific asymptotic stochastic order for random processes based on the measure of departure discussed in the literature. As applications, we stochastically compare mixtures of order statistics and record values…

Probability · Mathematics 2021-03-04 Sugata Ghosh , Asok K. Nanda

Data based judgments go into artificial intelligence applications but they undergo paradoxical reversal when seemingly unnecessary additional data is provided. Examples of this are Simpson's reversal and the disjunction effect where the…

Artificial Intelligence · Computer Science 2017-09-14 Subhash Kak

Ranking distributions according to a stochastic order has wide applications in diverse areas. Although stochastic dominance has received much attention, convex order, particularly in general dimensions, has yet to be investigated from a…

Methodology · Statistics 2025-01-15 Jakwang Kim , Young-Heon Kim , Yuanlong Ruan , Andrew Warren

Estimating the unknown number of classes in a population has numerous important applications. In a Poisson mixture model, the problem is reduced to estimating the odds that a class is undetected in a sample. The discontinuity of the odds…

Statistics Theory · Mathematics 2007-08-22 Chang Xuan Mao , Bruce G. Lindsay

In this paper we consider the distribution of the zeros of a real random Bargmann-Fock function of one or more variables. For these random functions we prove estimates for two types of families of events, both of which are large deviations…

Complex Variables · Mathematics 2008-07-04 Scott Zrebiec

Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous work has developed methods for assessing low-order predictive…

Machine Learning · Statistics 2022-03-01 Ian Osband , Zheng Wen , Seyed Mohammad Asghari , Vikranth Dwaracherla , Xiuyuan Lu , Benjamin Van Roy

Hypothesis testing in high dimensional data is a notoriously difficult problem without direct access to competing models' likelihood functions. This paper argues that statistical divergences can be used to quantify the difference between…

Data Analysis, Statistics and Probability · Physics 2024-08-02 Jeremy J. H. Wilkinson , Christopher G. Lester

In this paper, we investigate two variants of the secretary problem. In these variants, we are presented with a sequence of numbers $X_i$ that come from distributions $\mathcal{D}_i$, and that arrive in either random or adversarial order.…

Data Structures and Algorithms · Computer Science 2022-08-22 Pranav Nuti , Jan Vondrák

Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order…

Artificial Intelligence · Computer Science 2026-04-01 Florian Andreas Marwitz , Tanya Braun , Ralf Möller

In this paper, we have discussed the stochastic comparison of the smallest and largest ordered statistic from independent heterogeneous Weibull-G random variables and Gompertz Makeham random variables. We compare systems arising from taking…

Statistics Theory · Mathematics 2020-03-02 Madhurima Datta , Nitin Gupta

Several Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncertain domain models. Even though the correct working of these…

Artificial Intelligence · Computer Science 2013-02-28 Marek J. Druzdzel
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