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Cohort studies of the onset of a disease often encounter left-truncation on the event time of interest in addition to right-censoring due to variable enrollment times of study participants. Analysis of such event time data can be biased if…

Methodology · Statistics 2025-04-11 Spencer Matthews , Bin Nan

For a sample of Exponentially distributed durations we aim at point estimation and a confidence interval for its parameter. A duration is only observed if it has ended within a certain time interval, determined by a Uniform distribution.…

Methodology · Statistics 2021-10-19 Rafael Weißbach , Dominik Wied

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

This paper introduces a new version of the smoothly trimmed mean with a more general version of weights, which can be used as an alternative to the classical trimmed mean. We derive its asymptotic variance and to further investigate its…

Statistics Theory · Mathematics 2024-09-10 Elina Kresse , Emils Silins , Janis Valeinis

Statistical analyses of multipopulation studies often use the data to select a particular population as the target of inference. For example, a confidence interval may be constructed for a population only in the event that its sample mean…

Statistics Theory · Mathematics 2025-09-18 Peter Hoff , Surya Tokdar

Prediction intervals are a valuable way of quantifying uncertainty in regression problems. Good prediction intervals should be both correct, containing the actual value between the lower and upper bound at least a target percentage of the…

Machine Learning · Computer Science 2018-07-02 Dongqi Su , Ying Yin Ting , Jason Ansel

Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal inference provides a pivotal and flexible instrument for assessing the uncertainty of…

Machine Learning · Statistics 2025-09-09 Junxi Wu , Dongjian Hu , Yajie Bao , Shu-Tao Xia , Changliang Zou

Composite likelihood inference has gained much popularity thanks to its computational manageability and its theoretical properties. Unfortunately, performing composite likelihood ratio tests is inconvenient because of their awkward…

Computation · Statistics 2014-08-01 Manuela Cattelan , Nicola Sartori

Constructing valid inferential methods for constrained parameters in normal and Poisson distributions represents two fundamental and important problems in applied statistics, for which there is currently no unified framework for statistical…

Methodology · Statistics 2026-04-13 Hezhi Lu , Qijun Wu

A simple yet effective way of modeling survival data with cure fraction is by considering Box-Cox transformation cure model (BCTM) that unifies mixture and promotion time cure models. In this article, we numerically study the statistical…

Methodology · Statistics 2023-10-25 Suvra Pal , Sandip Barui

Progressive censoring scheme has received considerable attention in recent years. In this paper we introduce a new type-II progressive censoring scheme for two samples. It is observed that the proposed censoring scheme is analytically more…

Methodology · Statistics 2016-09-20 Shuvashree Mondal , Debasis Kundu

We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the test distribution)…

Machine Learning · Statistics 2020-02-20 Robin Vogel , Mastane Achab , Stéphan Clémençon , Charles Tillier

We consider the power to reject false values of the parameter in Frequentist methods for the calculation of confidence intervals. We connect the power with the physical significance (reliability) of confidence intervals for a parameter…

High Energy Physics - Experiment · Physics 2010-12-23 C. Giunti , M. Laveder

The pseudo-observations approach has been gaining popularity as a method to estimate covariate effects on censored survival data. It is used regularly to estimate covariate effects on quantities such as survival probabilities, restricted…

Methodology · Statistics 2024-12-06 Yael Travis-Lumer , Micha Mandel , Rebecca A. Betensky

In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep learning methods for analyzing censored time-to-event data, especially when…

Machine Learning · Statistics 2025-03-13 Sehwan Kim , Rui Wang , Wenbin Lu

We introduce a kernel-based goodness-of-fit test for censored data, where observations may be missing in random time intervals: a common occurrence in clinical trials and industrial life-testing. The test statistic is straightforward to…

Methodology · Statistics 2018-10-11 Tamara Fernández , Arthur Gretton

This study aims to predict failure times for some units in some lifetime experiments. In some practical situations, the experimenter may not be able to register the failure times of all units during the experiment. Recently, this situation…

Statistics Theory · Mathematics 2023-04-13 Mahmoud Mansour , Mohamed Aboshady

In survival analysis, the lifetime under study is not always observed. In certain applications, for some individuals, the value of the lifetime is only known to be smaller or larger than some random duration. This framework represent an…

Statistics Theory · Mathematics 2020-02-25 Laurent Bordes , Maria Carmen Pardo , Christian Paroissin , Valentin Patilea

Interval censored data commonly arise in medical studies when the event time of interest is only known to lie within an interval. In the presence of a cure subgroup, conventional mixture cure models typically assume a logistic model for the…

Methodology · Statistics 2026-01-21 Xiaoru Huang , Tonghui Yu , Xiaoyu Liu

Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors. In this paper, we develop an inferential method based on ideas from conformal prediction, which…

Methodology · Statistics 2023-04-25 Emmanuel J. Candès , Lihua Lei , Zhimei Ren