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Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made their mathematical analysis difficult, recent work has…

Methodology · Statistics 2019-12-10 Tim Coleman , Wei Peng , Lucas Mentch

Causal inference is crucial for understanding the true impact of interventions, policies, or actions, enabling informed decision-making and providing insights into the underlying mechanisms that shape our world. In this paper, we establish…

Methodology · Statistics 2024-03-26 Jingyue Huang , Changbao Wu , Leilei Zeng

Binary classifiers trained on a certain proportion of positive items introduce a bias when applied to data sets with different proportions of positive items. Most solutions for dealing with this issue assume that some information on the…

Machine Learning · Statistics 2021-02-18 Marco J. H. Puts , Piet J. H. Daas

The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs remains limited. In this paper, we propose the FlipFlop…

Computation and Language · Computer Science 2024-02-22 Philippe Laban , Lidiya Murakhovs'ka , Caiming Xiong , Chien-Sheng Wu

In this work, we introduce a modified (rescaled) likelihood for imbalanced logistic regression. This new approach makes easier the use of exponential priors and the computation of lasso regularization path. Precisely, we study a limiting…

Methodology · Statistics 2018-04-19 Vincent Runge

Sample size derivation is a crucial element of the planning phase of any confirmatory trial. A sample size is typically derived based on constraints on the maximal acceptable type I error rate and a minimal desired power. Here, power…

Large language models (LLMs) exhibit probabilistic output characteristics, yet conventional evaluation frameworks rely on deterministic scalar metrics. This study introduces a Bayesian approach for LLM capability assessment that integrates…

Computation and Language · Computer Science 2025-05-01 Xiao Xiao , Yu Su , Sijing Zhang , Zhang Chen , Yadong Chen , Tian Liu

A composite likelihood is a non-genuine likelihood function that allows to make inference on limited aspects of a model, such as marginal or conditional distributions. Composite likelihoods are not proper likelihoods and need therefore…

Methodology · Statistics 2021-04-06 Michele Lambardi di San Miniato , Nicola Sartori

The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We…

Statistics Theory · Mathematics 2017-12-12 Matias D. Cattaneo , Michael Jansson , Whitney K. Newey

The linear regression models are widely used statistical techniques in numerous practical applications. The standard regression model requires several assumptions about the regres- sors and the error term. The regression parameters are…

Methodology · Statistics 2016-10-23 P. Vellaisamy

In modern scientific research, small-scale studies with limited participants are increasingly common. However, interpreting individual outcomes can be challenging, making it standard practice to combine data across studies using random…

Statistics Theory · Mathematics 2025-11-04 Lucas Kania , Larry Wasserman , Sivaraman Balakrishnan

This work presents a theoretical and empirical evaluation of Anderson-Darling test when the sample size is limited. The test can be applied in order to backtest the risk factors dynamics in the context of Counterparty Credit Risk modelling.…

Risk Management · Quantitative Finance 2015-05-19 M. Formenti , L. Spadafora , M. Terraneo , F. Ramponi

This paper provides some useful tests for fitting a parametric single-index regression model when covariates are measured with error and validation data is available. We propose two tests whose consistency rates do not depend on the…

Methodology · Statistics 2016-04-29 Hira L. Koul , Chuanlong Xie , Lixing Zhu

Subsampling methods have been recently proposed to speed up least squares estimation in large scale settings. However, these algorithms are typically not robust to outliers or corruptions in the observed covariates. The concept of influence…

Machine Learning · Statistics 2014-06-20 Brian McWilliams , Gabriel Krummenacher , Mario Lucic , Joachim M. Buhmann

Understanding statistical inference under possibly non-sparse high-dimensional models has gained much interest recently. For a given component of the regression coefficient, we show that the difficulty of the problem depends on the sparsity…

Statistics Theory · Mathematics 2022-08-22 Jelena Bradic , Jianqing Fan , Yinchu Zhu

The application of machine learning models can be significantly impeded by the occurrence of distributional shifts, as the assumption of homogeneity between the population of training and testing samples in machine learning and statistics…

Machine Learning · Statistics 2023-06-06 Wenlu Tang , Zicheng Liu

We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the…

Statistics Theory · Mathematics 2022-06-08 Adam Bloniarz , Hanzhong Liu , Cun-Hui Zhang , Jasjeet Sekhon , Bin Yu

In the present paper, we develop a new goodness-of-fit test for the Birnbaum- Saunders distribution based on the probability plot. We utilize the sample correlation coefficient from the Birnbaum-Saunders probability plot as a measure of…

Applications · Statistics 2023-08-22 Chanseok Park , Min Wang

In this paper new test statistics are introduced and studied for the important problem of testing hypothesis that involves inequality constraint on proportions when the sample comes from independent binomial random variables: Wald type and…

Methodology · Statistics 2014-02-28 Nirian Martín , Raquel Mata , Leando Pardo

We consider the problem of goodness-of-fit testing for a model that has at least one unknown parameter that cannot be eliminated by transformation. Examples of such problems can be as simple as testing whether a sample consists of…

Methodology · Statistics 2021-04-28 Sean van der Merwe