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In this paper, we consider Bayesian point estimation and predictive density estimation in the binomial case. After presenting preliminary results on these problems, we compare the risk functions of the Bayes estimators based on the…

Statistics Theory · Mathematics 2021-09-13 Yasuyuki Hamura

The distributed Hill estimator is a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. In applications, estimates based on the distributed Hill estimator can be sensitive to the…

Methodology · Statistics 2021-12-21 Liujun Chen , Deyuan Li , Chen Zhou

Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large.…

Methodology · Statistics 2019-03-15 Weiwei Fan , L. Jeff Hong , Xiaowei Zhang

We consider a decoder with an erasure option and a variable size list decoder for channels with non-casual side information at the transmitter. First, universally achievable error exponents are offered for decoding with an erasure option…

Information Theory · Computer Science 2009-03-13 Erez Sabbag , Neri Merhav

While multiple testing procedures have been the focus of much statistical research, an important facet of the problem is how to deal with possible confounding. Procedures have been developed by authors in genetics and statistics. In this…

Methodology · Statistics 2008-12-18 Debashis Ghosh

We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on data that is observed sequentially by sensors in a distributed network. In particular, we assume the data to be drawn from a Gaussian…

Signal Processing · Electrical Eng. & Systems 2020-03-04 Dominik Reinhard , Michael Fauß , Abdelhak M. Zoubir

We consider a broadcast channel with a degraded message set, in which a single transmitter sends a common message to two receivers and a private message to one of the receivers only. The main goal of this work is to find new lower bounds to…

Information Theory · Computer Science 2009-06-09 Yonatan Kaspi , Neri Merhav

The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures…

Methodology · Statistics 2023-03-10 Francesco Denti , Stefano Peluso , Michele Guindani , Antonietta Mira

This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution…

Information Theory · Computer Science 2014-02-19 Adrià Tauste Campo , Gonzalo Vazquez-Vilar , Albert Guillén i Fàbregas , Tobias Koch , Alfonso Martinez

We introduce an expurgation method for source coding with side information that enables direct dual-domain derivations of expurgated error exponents. Dual-domain methods yield optimization problems over few parameters, with any sub-optimal…

Information Theory · Computer Science 2026-02-25 Mehdi Dabirnia , Hamdi Joudeh , Albert Guillén i Fàbregas

In this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizing prospect theory, a behavioral decision-maker is modeled to…

Information Theory · Computer Science 2018-12-05 Sinan Gezici , Pramod K. Varshney

We study a variant of quantum hypothesis testing wherein an additional 'inconclusive' measurement outcome is added, allowing one to abstain from attempting to discriminate the hypotheses. The error probabilities are then conditioned on a…

Quantum Physics · Physics 2024-07-09 Bartosz Regula , Ludovico Lami , Mark M. Wilde

In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens-Angrist instrumental variable model and the monotone…

Econometrics · Economics 2025-07-02 Zeyang Yu

The traditional binary classification framework constructs classifiers which may have good accuracy, but whose false positive and false negative error rates are not under users' control. In many cases, one of the errors is more severe and…

Machine Learning · Statistics 2020-10-22 Miloš Simić

We consider a two-component mixture model with one known component. We develop methods for estimating the mixing proportion and the unknown distribution nonparametrically, given i.i.d.~data from the mixture model, using ideas from shape…

Methodology · Statistics 2015-11-10 Rohit Kumar Patra , Bodhisattva Sen

We introduce a sieve for counting twin primes up to a given range. Our method depends on a parameter ${\lambda}_x$ and the estimation of the number of twin primes obtained as a result, is called a fundamental structure of the distribution…

General Mathematics · Mathematics 2021-11-09 Madieyna Diouf

In this paper, we propose a general method for testing composite hypotheses. Our idea is to use confidence limits to define stopping and decision rules. The requirements of operating characteristic function can be satisfied by adjusting the…

Statistics Theory · Mathematics 2012-02-10 Xinjia Chen

In a two-way relay network, two terminals exchange information over a shared wireless half-duplex channel with the help of a relay. Due to its fundamental and practical importance, there has been an increasing interest in this channel.…

Information Theory · Computer Science 2009-02-24 Hien Quoc Ngo , Tony Q. S. Quek , Hyundong Shin

We consider the problem of hypotheses testing with the basic simple hypothesis: observed sequence of points corresponds to stationary Poisson process with known intensity. The alternatives are stationary self-exciting point processes. We…

Statistics Theory · Mathematics 2009-03-27 Serguei Dachian , Yury A. Kutoyants

Error bounds based on worst likely assignments use permutation tests to validate classifiers. Worst likely assignments can produce effective bounds even for data sets with 100 or fewer training examples. This paper introduces a statistic…

Machine Learning · Statistics 2015-04-02 Eric Bax