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Related papers: The central limit theorem under random truncation

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We study behavior of the restricted maximum likelihood (REML) estimator under a misspecified linear mixed model (LMM) that has received much attention in recent gnome-wide association studies. The asymptotic analysis establishes consistency…

Statistics Theory · Mathematics 2014-04-10 Jiming Jiang , Cong Li , Debashis Paul , Can Yang , Hongyu Zhao

We obtain the law of large numbers (LLN) and the central limit theorem (CLT) for weakly dependent non-stationary arrays of random fields with asymptotically unbounded moments. The weak dependence condition for arrays of random fields is…

Statistics Theory · Mathematics 2024-08-15 Yue Pan , Jiazhu Pan

Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing, and is typically only feasible using approximate MCMC sampling. In this article we propose a minimax tilting…

Computation · Statistics 2016-03-15 Z. I. Botev

In this paper, based on the kernel estimator proposed by Ould-Said and Lemdani (Ann. Instit. Statist. Math. 2006), we develop some new generalized M-estimator procedures for single index regression models with left-truncated responses. The…

Statistics Theory · Mathematics 2018-01-22 Kong Lingtao , Zhang Yanli , Dai Hongshuai

The NPMLE of a distribution function from doubly truncated data was introduced in the seminal paper of Efron and Petrosian. The consistency of the Efron-Petrosian estimator depends however on the assumption of independent truncation. In…

Methodology · Statistics 2021-01-15 Carla Moreira , Jacobo de Uña-Álvarez , Roel Braekers

Suppose $B_i:= B(p,r_i)$ are nested balls of radius $r_i$ about a point $p$ in a dynamical system $(T,X,\mu)$. The question of whether $T^i x\in B_i$ infinitely often (i. o.) for $\mu$ a.e.\ $x$ is often called the shrinking target problem.…

Dynamical Systems · Mathematics 2015-06-16 Nicolai Haydn , Matthew Nicol , Sandro Vaienti , Licheng Zhang

The Latent Block Model (LBM) is a model-based method to cluster simultaneously the $d$ columns and $n$ rows of a data matrix. Parameter estimation in LBM is a difficult and multifaceted problem. Although various estimation strategies have…

Statistics Theory · Mathematics 2020-02-26 Vincent Brault , Christine Keribin , Mahendra Mariadassou

The paper aims at reconsidering the famous Le Cam LAN theory. The main features of the approach which make it different from the classical one are as follows: (1) the study is nonasymptotic, that is, the sample size is fixed and does not…

Statistics Theory · Mathematics 2013-03-06 Vladimir Spokoiny

We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit…

Machine Learning · Statistics 2024-03-05 Thomas Cook , Alan Mishler , Aaditya Ramdas

This paper studies an approximation method for the log-likelihood function of a nonlinear diffusion process using the bridge of the diffusion. The main result (Theorem \refthm:approx) shows that this approximation converges uniformly to the…

Statistics Theory · Mathematics 2010-01-11 Aleksandar Mijatović , Paul Schneider

We study the problem of estimating the parameters of a Boolean product distribution in $d$ dimensions, when the samples are truncated by a set $S \subset \{0, 1\}^d$ accessible through a membership oracle. This is the first time that the…

Machine Learning · Computer Science 2026-05-05 Dimitris Fotakis , Alkis Kalavasis , Christos Tzamos

Chen [Ann. Appl. Probab. {\bf 11} (2001), 1242--1262] derived exact convergence rates in a central limit theorem and a local limit theorem for a supercritical branching Wiener process.We extend Chen's results to a branching random walk…

Probability · Mathematics 2015-11-17 Zhiqiang Gao , Quansheng Liu

In this paper, we introduce a fundamental model for independent and identically distributed sequence with model uncertainty on the canonical space $(\mathbb{R}^\mathbb{N},\mathcal{B}(\mathbb{R}^\mathbb{N}))$ via probability kernels. Thanks…

Probability · Mathematics 2023-07-25 Xiaofan Guo , Xinpeng Li

We study the maximum likelihood estimation (MLE) in the multivariate deviated model where the data are generated from the density function $(1-\lambda^{\ast})h_{0}(x)+\lambda^{\ast}f(x|\mu^{\ast}, \Sigma^{\ast})$ in which $h_{0}$ is a known…

Statistics Theory · Mathematics 2023-10-31 Dat Do , Huy Nguyen , Khai Nguyen , Nhat Ho

For the univariate current status and, more generally, the interval censoring model, distribution theory has been developed for the maximum likelihood estimator (MLE) and smoothed maximum likelihood estimator (SMLE) of the unknown…

Statistics Theory · Mathematics 2013-06-18 Piet Groeneboom

We discuss nonparametric estimators of the distribution of the incubation time of a disease. The classical approach in these models is to use parametric families like Weibull, log-normal or gamma in the estimation procedure. We analyze…

Statistics Theory · Mathematics 2023-02-01 Piet Groeneboom

Let $(X_i)_{i \geq 1}$ and $(Y_i)_{i\geq1}$ be two independent sequences of independent identically distributed random variables taking their values in a common finite alphabet and having the same law. Let $LC_n$ be the length of the…

Probability · Mathematics 2023-01-09 Christian Houdré , Ümit Işlak

By using the independence structure of points following a determinantal point process, we study the radii of the spherical ensemble, the truncation of the circular unitary ensemble and the product ensemble with parameter n and k. The…

Probability · Mathematics 2014-11-10 Tiefeng Jiang , Yongcheng Qi

In this paper, we study the asymptotic error distribution for a two-level irregular discretization scheme of the solution to the stochastic differential equations (SDE for short) driven by a continuous semimartingale and obtain a central…

Probability · Mathematics 2025-12-15 Yi Guo , Yuxi Guo , Hanchao Wang

We develop an asymptotic statistical theory for parameter estimation from a class of non-i.i.d. periodic binary event-detection processes subject to nonparalyzable dead time and gating, which we call "dead-time event detection" (DED)…

Signal Processing · Electrical Eng. & Systems 2026-05-25 Frederic J. N. Jorgensen , Steven G. Johnson