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Related papers: Analytic Bias Reduction for $k$-Sample Functionals

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Importance sampling is a popular technique in Bayesian inference: by reweighting samples drawn from a proposal distribution we are able to obtain samples and moment estimates from a Bayesian posterior over latent variables. Recent work,…

Computation · Statistics 2024-06-19 Sam Bowyer , Thomas Heap , Laurence Aitchison

Ordinary Differential Equations are widespread tools to model chemical, physical, biological process but they usually rely on parameters which are of critical importance in terms of dynamic and need to be estimated directly from the data.…

Methodology · Statistics 2014-10-29 Nicolas Brunel , Quentin Clairon

Recent algebraic parametric estimation techniques led to point-wise derivative estimates by using only the iterated integral of a noisy observation signal. In this paper, we extend such differentiation methods by providing a larger choice…

Numerical Analysis · Mathematics 2011-03-04 Da-Yan Liu , Olivier Gibaru , Wilfrid Perruquetti

We propose the use of a simple intuitive principle for measuring algorithmic classification bias: the significance of the differences in a classifier's error rates across the various demographics is inversely commensurate with the sample…

Methodology · Statistics 2026-01-08 Ioannis Ivrissimtzis , Shauna Concannon , Matthew Houliston , Graham Roberts

In time series analysis, statistics based on collections of estimators computed from sub-samples play a crucial role in an increasing variety of important applications. Proving results about the joint asymptotic distribution of such…

Statistics Theory · Mathematics 2013-05-27 Stanislav Volgushev , Xiaofeng Shao

The raking-ratio method is a statistical and computational method which adjusts the empirical measure to match the true probability of sets of a finite partition. We study the asymptotic behavior of the raking-ratio empirical process…

Statistics Theory · Mathematics 2019-05-07 Mickael Albertus

Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well-suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing…

Computation · Statistics 2010-07-28 Michael Blum

Meta-analyses require an effect-size estimate and its corresponding sampling variance from primary studies. In some cases, estimators for the sampling variance of a given effect size statistic may not exist, necessitating the derivation of…

We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, econometrics, and machine learning. First, we obtain a…

Statistics Theory · Mathematics 2020-07-20 Matias D. Cattaneo , Max H. Farrell , Yingjie Feng

Motivated by recent work involving the analysis of leveraging spatial correlations in sparsified mean estimation, we present a novel procedure for constructing covariance estimator. The proposed Random-knots (Random-knots-Spatial) and…

Methodology · Statistics 2025-11-25 Sijie Zheng , Fandong Meng , Jie Zhou

This paper is concerned with Bayesian inference when the likelihood is analytically intractable but can be unbiasedly estimated. We propose an annealed importance sampling procedure for estimating expectations with respect to the posterior.…

Methodology · Statistics 2014-02-26 M. -N. Tran , C. Strickland , M. K. Pitt , R. Kohn

Bayesian inference provides a framework to combine various model components with shared parameters, allowing joint uncertainty estimation and the use of all available data sources. Unfortunately, misspecification of any part of the model…

Methodology · Statistics 2026-03-13 Emilia Pompe , Mikołaj J. Kasprzak , Pierre E. Jacob

A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning…

Machine Learning · Statistics 2014-01-14 Mahdi Pakdaman Naeini , Gregory F. Cooper , Milos Hauskrecht

We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator. Two types of oracle inequalities are derived for the NBR's elastic-net estimates by using the…

Machine Learning · Statistics 2022-01-11 Huiming Zhang , Jinzhu Jia

Considering the increasing size of available data, the need for statistical methods that control the finite sample bias is growing. This is mainly due to the frequent settings where the number of variables is large and allowed to increase…

Statistics Theory · Mathematics 2018-10-12 Stéphane Guerrier , Mucyo Karemera , Samuel Orso , Maria-Pia Victoria-Feser

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the…

Computation · Statistics 2010-05-04 M. G. B. Blum , O. Francois

Sub-sampling is a common and often effective method to deal with the computational challenges of large datasets. However, for most statistical models, there is no well-motivated approach for drawing a non-uniform subsample. We show that the…

Machine Learning · Statistics 2017-09-07 Daniel Ting , Eric Brochu

This paper is concerned with finite sample approximations to the supremum of a non-degenerate $U$-process of a general order indexed by a function class. We are primarily interested in situations where the function class as well as the…

Statistics Theory · Mathematics 2019-02-15 Xiaohui Chen , Kengo Kato

Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biases via a weighting mechanism, these procedures estimate an…

Methodology · Statistics 2021-07-06 Ashkan Ertefaie , Nima S. Hejazi , Mark J. van der Laan

A Bayesian approach to the classification problem is proposed in which random partitions play a central role. It is argued that the partitioning approach has the capacity to take advantage of a variety of large-scale spatial structures, if…

Statistics Theory · Mathematics 2007-06-13 Marc A. Coram