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Big data presents potential but unresolved value as a source for analysis and inference. However,selection bias, present in many of these datasets, needs to be accounted for so that appropriate inferences can be made on the target…

Methodology · Statistics 2025-01-09 Lyndon Ang , Robert Clark , Bronwyn Loong , Anders Holmberg

Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in…

Methodology · Statistics 2017-02-28 Shonosuke Sugasawa , Tatsuya Kubokawa

Variable selection in sparse regression models is an important task as applications ranging from biomedical research to econometrics have shown. Especially for higher dimensional regression problems, for which the link function between…

Machine Learning · Statistics 2019-12-10 Burim Ramosaj , Markus Pauly

This paper proposes a new model-based approach to small area estimation of general finite-population parameters based on grouped data or frequency data, which is often available from sample surveys. Grouped data contains information on…

Methodology · Statistics 2019-09-20 Yuki Kawakubo , Genya Kobayashi

The incorporation of uncertainties to calculations of signal significance in planned experiments is an actual task. Several approaches to this problem are discussed. We present a procedure for taking into account the systematic uncertainty…

High Energy Physics - Phenomenology · Physics 2009-11-07 S. I. Bityukov

The population recovery problem is a basic problem in noisy unsupervised learning that has attracted significant research attention in recent years [WY12,DRWY12, MS13, BIMP13, LZ15,DST16]. A number of different variants of this problem have…

Data Structures and Algorithms · Computer Science 2017-03-07 Anindya De , Ryan O'Donnell , Rocco Servedio

We consider the problem of counting the population size in the population model. In this model, we are given a distributed system of $n$ identical agents which interact in pairs with the goal to solve a common task. In each time step, the…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-05-29 Petra Berenbrink , Dominik Kaaser , Tomasz Radzik

Standard neutral population genetics theory with a strictly fixed population size has important limitations. An alternative model that allows independently fluctuating population sizes and reproduces the standard neutral evolution is…

Populations and Evolution · Quantitative Biology 2017-03-08 Thiparat Chotibut , David R. Nelson

While non-invasive sampling is more and more commonly used in capture-recapture (CR) experiments, it carries a higher risk of misidentifications than direct observations. As a consequence, one must screen the data to retain only the…

Quantitative Methods · Quantitative Biology 2023-04-04 Rémi Fraysse , Rémi Choquet , Carlo Costantini , Roger Pradel

In this paper, we develop an exact method for the determination of the minimum sample size for estimating the proportion of a finite population with prescribed margin of error and confidence level. By characterizing the behavior of the…

Statistics Theory · Mathematics 2007-08-03 Xinjia Chen

Detection of defective members of large populations has been widely studied in the statistics community under the name "group testing", a problem which dates back to World War II when it was suggested for syphilis screening. There the main…

Information Theory · Computer Science 2009-09-28 Mahdi Cheraghchi , Ali Hormati , Amin Karbasi , Martin Vetterli

A prevailing challenge in the biomedical and social sciences is to estimate a population mean from a sample obtained with unknown selection probabilities. Using a well-known ratio estimator, Aronow and Lee (2013) proposed a method for…

Statistics Theory · Mathematics 2017-06-26 Luke W. Miratrix , Stefan Wager , Jose R. Zubizarreta

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Covid-19 infections, people who use drugs, sex workers, conflict…

Methodology · Statistics 2022-10-17 Jinghao Sun , Luk Van Baelen , Els Plettinckx , Forrest W. Crawford

Estimation of the number of species or unobserved classes from a random sample of the underlying population is a ubiquitous problem in statistics. In classical settings, the size of the sample is usually small. New technologies such as…

Methodology · Statistics 2019-03-06 Timothy Daley , Andrew D Smith

We consider inference from non-random samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized…

Methodology · Statistics 2021-04-13 Yutao Liu , Andrew Gelman , Qixuan Chen

The Tweedie Compound Poisson-Gamma model is routinely used for modeling non-negative continuous data with a discrete probability mass at zero. Mixed models with random effects account for the covariance structure related to the grouping…

Machine Learning · Statistics 2019-02-05 Yaodong Yang , Rui Luo , Yuanyuan Liu

Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting…

A new approach to estimate population size based on a stratified link-tracing sampling design is presented. The method extends on the Frank and Snijders (1994) approach by allowing for heterogeneity in the initial sample selection…

Methodology · Statistics 2017-09-25 Kyle Vincent

Count data are common in medical research. When these data have more zeros than expected by the most used count distributions, it is common to employ a zero-inflated regression model. However, the interpretability of these models is much…

Methodology · Statistics 2025-09-30 Gustavo H. A. Pereira , Jeremias Leão , Manoel Santos-Neto , Jianwen Cai

We reconsider a nonparametric density model based on Gaussian processes. By augmenting the model with latent P\'olya--Gamma random variables and a latent marked Poisson process we obtain a new likelihood which is conjugate to the model's…

Machine Learning · Statistics 2018-05-30 Christian Donner , Manfred Opper