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This paper introduces a high-dimensional binary variate model that accommodates nonstationary covariates and factors, and studies their asymptotic theory. This framework encompasses scenarios where single indices are nonstationary or…

Statistics Theory · Mathematics 2025-05-29 Xinbing Kong , Bin Wu , Wuyi Ye

This paper considers the asymptotic properties of the recursive maximum likelihood estimation in hidden Markov models. The paper is focused on the asymptotic behavior of the log-likelihood function and on the point-convergence and…

Statistics Theory · Mathematics 2009-09-24 Vladislav B. Tadić

We introduce ellipticity criteria for random walks in i.i.d. random environments under which we can extend the ballisticity conditions of Sznitman's and the polynomial effective criteria of Berger, Drewitz and Ramirez originally defined for…

Probability · Mathematics 2014-06-05 David Campos , Alejandro F. Ramirez

Maximum likelihood is the most widely used statistical estimation technique. Recent work by the authors introduced a general methodology for the construction of estimators for functionals in parametric models, and demonstrated improvements…

Methodology · Statistics 2014-09-29 Jiantao Jiao , Kartik Venkat , Yanjun Han , Tsachy Weissman

We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are…

Statistics Theory · Mathematics 2007-08-22 Yun Ju Sung , Charles J. Geyer

This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the dimensions of the moment restrictions and…

Statistics Theory · Mathematics 2015-01-28 Jinyuan Chang , Song Xi Chen , Xiaohong Chen

In many complex statistical models maximum likelihood estimators cannot be calculated. In the paper we solve this problem using Markov chain Monte Carlo approximation of the true likelihood. In the main result we prove asymptotic normality…

Statistics Theory · Mathematics 2018-08-09 Błażej Miasojedow , Wojciech Niemiro , Wojciech Rejchel

We consider random walks in Dirichlet environment (RWDE) on $\Z ^d$, for $ d \geq 3 $, in the sub-ballistic case. We associate to any parameter $ (\alpha_1, ..., \alpha_{2d}) $ of the Dirichlet law a time-change to accelerate the walk. We…

Probability · Mathematics 2012-05-28 Élodie Bouchet

Consider a nearest-neighbor random walk with certain asymptotically zero drift on the positive half line. Let $M$ be the maximum of an excursion starting from $1$ and ending at $0.$ We study the distribution of $M$ and characterize its…

Probability · Mathematics 2020-04-28 Hongyan Sun , Hua-Ming Wang

It is well known that under general regularity conditions the distribution of the maximum likelihood estimator (MLE) is asymptotically normal. Very recently, bounds of the optimal order $O(1/\sqrt n)$ on the closeness of the distribution of…

Statistics Theory · Mathematics 2016-12-15 Iosif Pinelis

We give new criteria for ballistic behavior of random walks in random environment which are perturbations of the simple symmetric random walk on $\mathbb Z^d$ in dimensions $d\ge 4$. Our results extend those of Sznitman [Ann. Probab. 31,…

Probability · Mathematics 2021-03-05 Ryoki Fukushima , Alejandro F. Ramírez

Consider a branching random walk evolving in a macroscopic time-inhomogeneous environment, that scales with the length $n$ of the process under study. We compute the first two terms of the asymptotic of the maximal displacement at time $n$.…

Probability · Mathematics 2018-10-01 Bastien Mallein

Temporal Point Processes (TPP) with partial likelihoods involving a latent structure often entail an intractable marginalization, thus making inference hard. We propose a novel approach to Maximum Likelihood Estimation (MLE) involving…

Machine Learning · Computer Science 2019-12-20 Amrith Setlur , Barnabás Póczós

We prove the local asymptotic mixed normality (LAMN) property for a family of probability measures defined by parametrized diffusion processes with nonsynchronous observations. We assume that observation times of processes are independent…

Statistics Theory · Mathematics 2015-09-21 Teppei Ogihara

The stationary distribution of allele frequencies under a variety of Wright--Fisher $k$-allele models with selection and parent independent mutation is well studied. However, the statistical properties of maximum likelihood estimates of…

Applications · Statistics 2009-10-12 Erkan Ozge Buzbas , Paul Joyce

We establish some new non-asymptotical lower bounds for deviation of regular unbiased estimation of unknown parameter from its true value in different norms, alike the classical Rao-Kramer's inequality. We show that if the new norm is…

Statistics Theory · Mathematics 2014-07-17 E. Ostrovsky , L. Sirota

Cosmological parameter estimation is traditionally performed in the Bayesian context. By adopting an "agnostic" statistical point of view, we show the interest of confronting the Bayesian results to a frequentist approach based on…

Cosmology and Nongalactic Astrophysics · Physics 2016-07-12 S. Henrot-Versillé , O. Perdereau , S. Plaszczynski , B. Rouillé d'Orfeuil , M. Spinelli , M. Tristram

Consider $M_n$ the maximal position at generation $n$ of a supercritical branching random walk. A\"id\'ekon (2013) obtained and described the convergence in law, as time $n$ goes to infinity, of $M_n-m_n$, where $m_n$ is an explicit…

Probability · Mathematics 2026-01-14 Louis Chataignier , Lianghui Luo

In this paper non-asymptotic exact exponential estimates are derived (under minimal conditions) for the tail of deviation of the MLE distribution in the so-called natural terms: natural function, natural distance, metric entropy, Banach…

Probability · Mathematics 2009-03-25 E. Ostrovsky , E. Rogover

We introduce an original way to estimate the memory parameter of the elephant random walk, a fascinating discrete time random walk on integers having a complete memory of its entire history. Our estimator is nothing more than a…

Probability · Mathematics 2021-12-21 Bernard Bercu , Lucile Laulin