Results 31 to 40 of about 124,454 (198)

Comparing Linear Regression to Shrinkage Regression Algorithms (RR, Lasso, El Net) Using PTSD Patients’ Data [PDF]

پژوهش‌های کاربردی روانشناختی, 2020
The purpose of this research was to introduce the alternative model of regression algorithms and having it compared to linear regression. To do this, we need to use modern algorithms such as Ridge, Lasso, and Elastic net regression in which precision is ...
Hojjatollah Farahani
doaj   +1 more source

Preprocessing of Heteroscedastic Medical Images

IEEE Access, 2018
Tissue intensity distributions in medical images can have varying degrees of statistical dispersion, which is referred to as heteroscedasticity. This can influence image contrast and gradients, but can also negatively affect the performance of general ...
Philip Joris, Wim Develter, Wim Van De Voorde, Paul Suetens, Frederik Maes, Dirk Vandermeulen, Peter Claes   +6 more
doaj   +1 more source

Bayesian estimation for the random moderation model: effect size, coverage, power of test, and type І error

Frontiers in Psychology, 2023
The random moderation model (RMM) was developed based on a two-level regression model to cope with heteroscedasticity in moderation analysis, and normal-distributed-based maximum likelihood (NML) estimation was developed to estimate the RMM.
Dan Wei, Dan Wei, Peida Zhan
doaj   +1 more source

Accurate Standard Errors in Multilevel Modeling with Heteroscedasticity: A Computationally More Efficient Jackknife Technique

Psych, 2023
In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated
Steffen Zitzmann, Sebastian Weirich, Martin Hecht   +2 more
doaj   +1 more source

MENGATASI MASALAH HETEROSKEDASTISITAS DENGAN MENGASUMSIKAN VARIANS VARIABEL GANGGUANNYA PROPORSIONAL DENGAN X_i^2 DAN [E(Y_i)]^2

E-Jurnal Matematika, 2014
The volatilities of time series data often experience heteroscedastic problems. Heteroscedasticity is a nuisance variable in the regression equation having a variance that is not constant.
MADE ADI GUNAWAN, LUH PUTU IDA HARINI, MADE ASIH   +2 more
doaj   +1 more source

Corona, crisis and conditional heteroscedasticity [PDF]

Applied Economics Letters, 2020
In this paper, we illustrate the macroeconomic risk associated with the early stage of the corona-virus outbreak.
Kiss, Tamás, Österholm, Pär
openaire   +2 more sources

Hybridization of long short-term memory neural network in fractional time series modeling of inflation

Frontiers in Big Data
Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates.
Erman Arif, Elin Herlinawati, Dodi Devianto, Mutia Yollanda, Dony Permana   +4 more
doaj   +1 more source

MENGATASI HETEROSKEDASTISITAS PADA REGRESI DENGAN MENGGUNAKAN WEIGHTED LEAST SQUARE

E-Jurnal Matematika, 2015
In the regression analysis we need a method to estimate parameters to fulfill the BLUE characteristic. There are assumptions that must be fulfilled homoscedasticity one of which is a condition in which the assumption of error variance is constant (same),
PUTU AYU MAZIYYA, I KOMANG GDE SUKARSA, NI MADE ASIH   +2 more
doaj   +1 more source

Statistical Assessment of Stress Redistribution in Loaded Polycrystals

Image Analysis and Stereology, 2022
This work deals with the analysis of stress redistribution in a polycrystal due to external loading, anisotropy of elastic properties, and microstructure characteristics. A statistical method that enables assessing relationships between stress fields and
Iva Karafiátová, Zbyněk Pawlas, Luděk Heller   +2 more
doaj   +1 more source

The relationship between ARIMA-GARCH and unobserved component models with GARCH disturbances [PDF]

, 2007
The objective of this paper is to analyze the consequences of fitting ARIMA-GARCH models to series generated by conditionally heteroscedastic unobserved component models.
Espasa, Antoni, Pellegrini, Santiago, Ruiz Ortega, Esther   +2 more
core   +6 more sources