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Deconvolution problem of cumulative distribution function with heteroscedastic errors
We study the nonparametric deconvolution problem of cumulative distribution function when measurement errors are heteroscedastic and have known...
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Bayesian Quantile Estimation in Deconvolution
Estimating quantiles of a population is a fundamental problem of high practical relevance in nonparametric statistics. This chapter addresses the...
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Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach
Interest in studying genomics and transcriptomics at the single-cell level has been increasing. One of the keys to single-cell study is develo**...
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Parametric estimation of hidden Markov models by least squares type estimation and deconvolution
This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For...
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Density deconvolution for generalized skew-symmetric distributions
The density deconvolution problem is considered for random variables assumed to belong to the generalized skew-symmetric (GSS) family of...
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Bivariate Kernel Deconvolution with Panel Data
We consider estimation of the density of a multivariate response, that is not observed directly but only through measurements contaminated by...
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Density Deconvolution in a Non-standard Case of Heteroscedastic Noises
We study the density deconvolution problem with heteroscedastic noises whose densities are known exactly and Fourier-oscillating. Based on available...
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Kernel Circular Deconvolution Density Estimation
We consider the problem of nonparametrically estimating a circular density from data contaminated by angular measurement errors. Specifically, we...
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Data-driven Deconvolution Recursive Kernel Density Estimators Defined by Stochastic Approximation Method
In this paper we show how one can implement in practice the bandwidth selection in deconvolution recursive kernel estimators of a probability density...
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Kernel regression for errors-in-variables problems in the circular domain
We study the problem of estimating a regression function when the predictor and/or the response are circular random variables in the presence of...
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Density Deconvolution with Small Berkson Errors
The present paper studies density deconvolution in the presence of small Berkson errors, in particular, when the variances of the errors tend to zero...
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Parameter estimation for Logistic errors-in-variables regression under case–control studies
The article develops parameter estimation in the Logistic regression when the covariate is observed with measurement error. In Logistic regression...
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Right-censored nonparametric regression with measurement error
This study focuses on estimating a nonparametric regression model with right-censored data when the covariate is subject to measurement error. To...
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A Nonnegative Robust Linear Model for Deconvolution of Proportions
Estimating mixing rates of a sample mixture is a popular problem in biomedical studies. Recently, it is applied to find immune cell infiltration in...
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Uncoupled Isotonic Regression with Discrete Errors
In Rigollet and Weed (2019), an estimator was proposed for the uncoupled isotonic regression problem. It was shown that a so-called minimum...
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Analysis and Modeling of TL Data
In this chapter we provide detailed R codes which show how researchers can analyze and model their experimental TL data. We provide R codes for the...
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On the performance of weighted bootstrapped kernel deconvolution density estimators
We propose a weighted bootstrap approach that can improve on current methods to approximate the finite sample distribution of normalized maximal...
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Bayesian Kantorovich Deconvolution in Finite Mixture Models
This chapter addresses the problem of recovering the mixing distribution in finite kernel mixture models, when the number of components is unknown,...
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Leveraging Data Analytics and a Deep Learning Framework for Advancements in Image Super-Resolution Techniques: From Classic Interpolation to Cutting-Edge Approaches
Image SR is a critical task in the field of computer vision, aiming to enhance the resolution and quality of low-resolution images. This chapter...