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Related papers: Density estimation for RWRE

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Weakly chaotic maps with unstable fixed points are investigated in the regime where the invariant density is non-normalizable. We propose that the infinite invariant density of these maps can be estimated using as the long time limit of…

Statistical Mechanics · Physics 2013-09-03 Nickolay Korabel , Eli Barkai

We describe a method to computationally estimate the probability density function of a univariate random variable by applying the maximum entropy principle with some local conditions given by Gaussian functions. The estimation errors and…

Statistics Theory · Mathematics 2012-06-21 Mihail-Ioan Pop

We study a symmetric random walk (RW) in one spatial dimension in environment, formed by several zones of finite width, where the probability of transition between two neighboring points and corresponding diffusion coefficient are…

Statistical Mechanics · Physics 2017-04-03 A. V. Nazarenko , V. Blavatska

The problem of nonparametric estimation of the conditional density of a response, given a vector of explanatory variables, is classical and of prominent importance in many prediction problems since the conditional density provides a more…

Methodology · Statistics 2015-04-21 Catia Scricciolo

Statistical inference based on optimal transport offers a different perspective from that of maximum likelihood, and has increasingly gained attention in recent years. In this paper, we study univariate nonparametric shape-constrained…

Statistics Theory · Mathematics 2026-04-13 Takeru Matsuda , Ting-Kam Leonard Wong

The estimation of a log-concave density on $\mathbb{R}$ is a canonical problem in the area of shape-constrained nonparametric inference. We present a Bayesian nonparametric approach to this problem based on an exponentiated Dirichlet…

Statistics Theory · Mathematics 2020-07-14 Ester Mariucci , Kolyan Ray , Botond Szabo

In many statistical and econometric applications, we gather individual samples from various interconnected populations that undeniably exhibit common latent structures. Utilizing a model that incorporates these latent structures for such…

Methodology · Statistics 2023-09-19 Archer Gong Zhang , Jiahua Chen

We consider one infinite path of a Random Walk in Random Environment (RWRE, for short) in an unknown environment. This environment consists of either i.i.d.\ site or bond randomness. At each position the random walker stops and tells us the…

Probability · Mathematics 2021-09-16 Jonas Jalowy , Matthias Löwe

We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density estimator (KDE) with ideas from classical…

Machine Learning · Statistics 2011-09-07 JooSeuk Kim , Clayton D. Scott

For $n$ independent random variables having the same H\"older continuous density, this paper deals with controls of the Wolverton-Wagner's estimator MSE and MISE. Then, for a bandwidth $h_n(\beta)$, estimators of $\beta$ are obtained by a…

Statistics Theory · Mathematics 2020-09-22 Fabienne Comte , Nicolas Marie

We study the nonparametric estimation of the jump density of a renewal reward process from one discretely observed sample path over [0,T]. We consider the regime when the sampling rate goes to 0. The main difficulty is that a renewal reward…

Statistics Theory · Mathematics 2012-07-09 Celine Duval

Non-parametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging…

Methodology · Statistics 2016-10-10 Adelchi Azzalini

We consider random walks on the nonnegative integers in a space-time dependent random environment. We assume that transition probabilities are given by independent $\mathrm{Beta}(\mu,\mu)$ distributed random variables, with a specific…

Probability · Mathematics 2022-11-30 Guillaume Barraquand , Mark Rychnovsky

Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution…

Machine Learning · Statistics 2011-06-24 Makoto Yamada , Taiji Suzuki , Takafumi Kanamori , Hirotaka Hachiya , Masashi Sugiyama

This paper proposes a new method for estimating high-dimensional binary choice models. We consider a semiparametric model that places no distributional assumptions on the error term, allows for heteroskedastic errors, and permits endogenous…

Econometrics · Economics 2025-07-15 Fu Ouyang , Thomas Tao Yang

In this paper, we study a class of non-parametric density estimators under Bayesian settings. The estimators are piecewise constant functions on binary partitions. We analyze the concentration rate of the posterior distribution under a…

Statistics Theory · Mathematics 2015-08-21 Linxi Liu , Wing Hung Wong

We investigate the nonparametric estimation problem of the density $\pi$, representing the stationary distribution of a two-dimensional system $\left(Z_t\right)_{t \in[0, T]}=\left(X_t, \lambda_t\right)_{t \in[0, T]}$. In this system, $X$…

Statistics Theory · Mathematics 2025-10-01 Chiara Amorino , Charlotte Dion-Blanc , Arnaud Gloter , Sarah Lemler

We study the densities of uniform random walks in the plane. A special focus is on the case of short walks with three or four steps and less completely those with five steps. As one of the main results, we obtain a hypergeometric…

Classical Analysis and ODEs · Mathematics 2015-03-19 Jonathan M. Borwein , Armin Straub , James Wan , Wadim Zudilin

We consider the penetration length $l$ of random walkers diffusing in a medium of perfect or imperfect absorbers of number density $\rho$. We solve this problem on a lattice and in the continuum in all dimensions $D$, by means of a…

Statistical Mechanics · Physics 2009-11-11 David S. Dean , Clément Sire , Julien Sopik

The Beta kernel estimator offers a theoretically superior alternative to the Gaussian kernel for unit interval data, eliminating boundary bias without requiring reflection or transformation. However, its adoption remains limited by the lack…

Methodology · Statistics 2026-05-12 Johan Hallberg Szabadváry
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