Results 31 to 40 of about 22,159 (266)

Parametric and nonparametric bootstrap methods for general MANOVA

Journal of Multivariate Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Frank Konietschke, Arne C. Bathke, Solomon W. Harrar, Markus Pauly   +3 more
openaire   +2 more sources

On the Inconsistency of Nonparametric Bootstraps for the Subvector Anderson-Rubin Test [PDF]

SSRN Electronic Journal, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

MPBoot: fast phylogenetic maximum parsimony tree inference and bootstrap approximation

BMC Evolutionary Biology, 2018
Background The nonparametric bootstrap is widely used to measure the branch support of phylogenetic trees. However, bootstrapping is computationally expensive and remains a bottleneck in phylogenetic analyses.
Diep Thi Hoang, Le Sy Vinh, Tomáš Flouri, Alexandros Stamatakis, Arndt von Haeseler, Bui Quang Minh   +5 more
doaj   +1 more source

Nonparametric bootstrap test for autoregressive additive models

Statistics in Transition new series, 2009
Additive autoregressive models are commonly used to describe and simplify the behaviour of a nonlinear time series. When the additive structure is chosen, and the model estimated, it is important to evaluate if it is really suitable to describe the observed data since additivity represents a strong assumption.
Bagnato L, PUNZO, ANTONIO
openaire   +3 more sources

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Bootstrap Initialization of MLE for Infinite Mixture Distributions with Applications in Insurance Data

Risks
Maximum likelihood estimation (MLE) in infinite mixture distributions often lacks closed-form solutions, requiring numerical methods such as the Newton–Raphson algorithm.
Aceng Komarudin Mutaqin
doaj   +1 more source

Bandwidth Selection for Prediction in Regression

Proceedings, 2019
There exist many different methods to choose the bandwidth in kernel regression. If, however, the target is regression based prediction for samples or populations with potentially different distributions, then the existing methods can easily be ...
Inés Barbeito, Ricardo Cao, Stefan Sperlich   +2 more
doaj   +1 more source

APPLICATION OF THE BOOTSTRAP METHOD ON A LARGE INPUT DATA SET - CASE STUDY ON THE WESTERN PART OF THE SAVA DEPRESSION

Rudarsko-geološko-naftni Zbornik, 2021
The bootstrap method is a nonparametric statistical method that through the resampling of an input data set provides the ability to obtain a new data set that is normally distributed.
Josip Ivšinović, Nikola Litvić
doaj   +1 more source

On Bootstrap Confidence Intervals in Nonparametric Regression

The Annals of Statistics, 1992
A pivotal approach to nonparametric regression is considered. Some of the theoretical advantages of this approach are pointed out, among them a reduction in the error of the bootstrap distribution function estimate and a reduction in coverage error of confidence interval.
openaire   +3 more sources