Results 21 to 30 of about 7,690 (301)
Composition Estimation Via Shrinkage
SSRN Electronic Journal, 2022 In this note, we explore a simple approach to composition estimation, using penalized likelihood density estimation on a nominal discrete domain. Practical issues such as smoothing parameter selection and the use of prior information are investigated in simulations, and a theoretical analysis is attempted. The method has been implemented in a pair of R
openaire +2 more sources
A penalised data-driven block shrinkage approach to empirical Bayes wavelet estimation [PDF]
, 2010 In this paper we propose a simple Bayesian block wavelet shrinkage method for estimating an unknown function in the presence of Gaussian noise. A data–driven procedure which can adaptively choose the block size and the shrinkage level at each resolution ...
Wang, Xue, Walker, Stephen G.
core +1 more source
Comparison of Risk Ratios of Shrinkage Estimators in High Dimensions
Mathematics, 2021 In this paper, we analyze the risk ratios of several shrinkage estimators using a balanced loss function. The James–Stein estimator is one of a group of shrinkage estimators that has been proposed in the existing literature.
Abdenour Hamdaoui +3 more
doaj +1 more source
Comparison of Some of Estimation methods of Stress-Strength Model: R = P(Y < X < Z)
مجلة بغداد للعلوم, 2021 In this study, the stress-strength model R = P(Y < X < Z) is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used to estimate the
Sairan Hamza Raheem +2 more
doaj +1 more source
On Improved Loss Estimation for Shrinkage Estimators
Statistical Science, 2012 Let $X$ be a random vector with distribution $P_θ$ where $θ$ is an unknown parameter. When estimating $θ$ by some estimator $φ(X)$ under a loss function $L(θ,φ)$, classical decision theory advocates that such a decision rule should be used if it has suitable properties with respect to the frequentist risk $R(θ,φ)$. However, after having observed $X=x$,
Fourdrinier, Dominique, Wells, Martin T.
openaire +3 more sources
Improving generalized ridge estimator for the gamma regression model. [PDF]
المجلة العراقية للعلوم الاحصائيةIt has been consistently proven that the ridge estimator is an effective shrinking strategy for reducing the effects of multicollinearity. An effective model to use when the response variable is positively skewed is the Gamma Regression Model (GRM ...
AVAN Al-Saffar, Zakaria Y. Algamal
doaj +1 more source
Small Area Shrinkage Estimation
Statistical Science, 2012 The need for small area estimates is increasingly felt in both the public and private sectors in order to formulate their strategic plans. It is now widely recognized that direct small area survey estimates are highly unreliable owing to large standard errors and coefficients of variation.
Datta, G., Ghosh, M.
openaire +4 more sources
Estimating the Variance of an Exponential Distribution in the Presence of Large True Observations
Austrian Journal of Statistics, 2016 The present paper discusses some classes of shrinkage estimators for the variance of the exponential distribution in the presence of large true observations when some a priori or guessed interval containing the variance parameter is available from some ...
Housila P. Singh, Vankim Chander
doaj +1 more source
Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications
Iraqi Journal for Computer Science and Mathematics, 2023 Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent.
Zakariya Algamal +3 more
doaj +1 more source
Bayesian Approaches to Shrinkage and Sparse Estimation [PDF]
Foundations and Trends® in Econometrics, 2021 In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/nonparametric inference is the norm in several fields of applied econometric work. The purpose of this monograph is to introduce the reader
Korompilis, Dimitris, Shimizu, Kenichi
openaire +3 more sources