Results 1 to 10 of about 79 (76)

Recovering Jackknife Ridge Regression Estimates from OLS Results [PDF]

مجلة جامعة الانبار للعلوم الصرفة, 2014
The aim of this paper is addressing or recalculate the estimation methods in multiple linear regression model when there is a problem of Multicollinearity in this model like the ridge regression for Hoerl and Kannard, Baldwin estimator (HKB) and ...
Feras Sh. Mahmood
doaj   +2 more sources

On shrinkage estimators improving the positive part of James-Stein estimator

Demonstratio Mathematica, 2021
In this work, we study the estimation of the multivariate normal mean by different classes of shrinkage estimators. The risk associated with the quadratic loss function is used to compare two estimators. We start by considering a class of estimators that
Hamdaoui Abdenour
doaj   +1 more source

Influence diagnostics for the Poisson regression model using two-parameter estimator

Alexandria Engineering Journal, 2021
The identification of influential observations is an essential element in regression analysis as they posed a threat to the model building process.
Aamna Khan, Muhammad Amanullah, Hassan M. Aljohani, Sh.A.M. Mubarak   +3 more
doaj   +1 more source

A study of minimax shrinkage estimators dominating the James-Stein estimator under the balanced loss function

Open Mathematics, 2022
One of the most common challenges in multivariate statistical analysis is estimating the mean parameters. A well-known approach of estimating the mean parameters is the maximum likelihood estimator (MLE).
Benkhaled Abdelkader, Hamdaoui Abdenour, Almutiry Waleed, Alshahrani Mohammed, Terbeche Mekki   +4 more
doaj   +1 more source

Density derivative estimation for stationary and strongly mixing data

Alexandria Engineering Journal, 2020
Estimation of density derivatives has found multiple uses in statistical data analysis. An inefficient two-step method to obtain it is estimating the density and then computing the derivatives.
Marziyeh Mahmoudi, Ahmad Nezakati, Mohammad Arashi, Mohammad Reza Mahmoudi   +3 more
doaj   +1 more source

Theoretical Modeling by Addressing Nonresponse Complications to Improve the Population Mean Under Stratified Random Sampling: Application and Analysis

Journal of Mathematics, Volume 2026, Issue 1, 2026.
An important part of survey sampling is additional information, which allows for more precise estimates of population parameters such as population distribution function, mean, variance, and median. The best outcomes can be assured in this manner. Researchers using survey sampling face the risk of missing important details while attempting to compile ...
Abdullah Mohammed Alomair, Sohaib Ahmad, Nian-Sheng Tang   +2 more
wiley   +1 more source

Valid causal inference with unobserved confounding in high-dimensional settings

Journal of Causal Inference
Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data-generating processes when high-dimensional nuisance models are estimated by post-model-selection or machine ...
Moosavi Niloofar, Gorbach Tetiana, de Luna Xavier   +2 more
doaj   +1 more source

Improved Efficiency in Generalized Poisson Hurdle Model Estimation Using Restricted and Shrinkage Methods

Journal of Mathematics, Volume 2025, Issue 1, 2025.
This paper investigates the use of shrinkage estimators in the generalized Poisson hurdle (GPH) model for count data analysis. The GPH model effectively handles data with both excess zeros and over‐ or underdispersion. We propose shrinkage estimators to improve parameter estimation in this model and analyze their asymptotic properties, including biases
Hayder Hasan Rahmah Al-Gharrawi, Hossein Bevrani, Basim Shlaibah Msallam, Antonio Di Crescenzo   +3 more
wiley   +1 more source

Regularisasi model pembelajaran mesin dengan regresi terpenalti pada data yang mengandung multikolinearitas (Studi kasus prediksi Indeks Pembangunan Manusia di 34 provinsi di Indonesia)

Majalah Ilmiah Matematika dan Statistika
This research intends to model high-dimensional data that contains multicollinearity in four machine-learning algorithms: Random Forest, K-Nearest Neighbor, XGBoost, and Regression Tree.
Nur Khamidah, Kusman Sadik, Agus M Soleh, Gerry Alfa Dito   +3 more
doaj   +1 more source

On the performance of the new minimax shrinkage estimators for a normal mean vector

Demonstratio Mathematica
This paper explores new classes of estimators for a multivariate normal mean (MNM) with an unknown variance and evaluating their performance based on the risk relative to the balanced loss function (BLF).
Benkhaled Abdelkader, Alahmadi Amani, Hamdaoui Abdenour, Terbeche Mekki   +3 more
doaj   +1 more source