Results 31 to 40 of about 205 (138)

New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model

مجلة بغداد للعلوم, 2020
This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model.
Mustafa Ismaeel Naif Alheety
doaj  

Null intercept measurement error regression models

Maximum likelihood, pretest/posttest data, random effects, 62F05, 62J05,
Julio Singer, Hereno Bolfarine, Reiko Aoki   +2 more
core   +1 more source

2000 Mathematics Subject Classification: Primary 62C99, sec-ondary 62C10, 62C20, 62J05

, 2016
The paper deals with recovering an unknown vector β ∈ R^p based on the observations Y = Xβ + ∈ξ and Z = X + σζ, where X is an unknown n×p-matrix with n ≥ p, ξ ∈ R^p is a standard white Gaussian noise, ζ is a n × p-matrix with i.i.d. standard Gaussian entries, and ∈, σ ∈ R^+ are known noise levels. It is assumed that X has a large condition number and p
Golubev, Yu., Zimolo, Th.
openaire   +1 more source

Asymptotic efficiency properties of least squares in an ultrastructural model

Measurement errors, direct regression, reverse regression, ultrastructural model, 62J05, 62F12,
Shalabh, A. Srivastava
core   +1 more source

Least squares estimators in measurement error models under the balanced loss function

Balanced loss function, direct and reverse regression, ineasurement errors, ultrastructural model, 62J05,
Shalabh
core   +1 more source

Parametric modelling of growth curve data: An overview

Alc, BIC, covariance structure, longitudinal data, mean structure, repeated measures, RLRT, 62J05, 62F10, 62P10,
Geert Verbeke, Michael Kenward, Dale Zimmerman, Timothy Gregoire, Jeffrey Hart, Philippe Vieu, Vicente Núñez-Antón, Geert Molenberghs, Mohsen Pourahmadi, Dela Zimmerman, Oliver Schabenberger   +10 more
core   +1 more source

A note on the robust interpretation of regression coefficients

Least squares, log normal distribution, parameter of interest, parameter stability, 62J05,
D. Cox, M. Wong
core   +1 more source

On the choice of support of Re-descendingψ-Functions in linear models with asymmetric error distributions

Primary 62F35, secondary 62G05, 62J05, Linear models, asymmetric errors, robust estimation, re-descending influence functions,
M. Hlynka, D. Wiens, J. Sheahan
core   +1 more source

Estimation of regression coefficients subject to exact linear restrictions when some observations are missing and quadratic error balanced loss function is used

Balanced loss function, exact restrictions, missing observations, 62J05,
Shalabh, H. Toutenburg
core   +1 more source

A theorem on the covariance matrix of a generalized least squares estimator under an elliptically symmetric error

Linear regression model, Covariance matrix, Elliptically symmetric distribution, Generalized least squares estimator, Heteroscedastic model, 62J05, 62H12,
Hiroshi Kurata
core   +1 more source