Results 51 to 60 of about 2,019,213 (203)
Bayesian D‐optimal designs for error‐in‐variables models [PDF]
Applied Stochastic Models in Business and Industry, 2017 Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D‐optimality for nonlinear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied, and explicit characterisations of the Bayesian D ...
Konstantinou, Maria, Dette, Holger
openaire +5 more sources
Discrete Dynamics in Nature and Society, 2022 This article deals with the semiparametric errors-in-variables (EV) model yi=ξiβ+gti+ϵi, xi=ξi+μi, where yi are the random missing response variables, ξi,ti are the design points, ξi are the potential variables observed with measurement errors μi, and ...
Jing-jing Zhang, Cheng-Liang Liu
doaj +1 more source
Quantile regression in high-dimension with breaking [PDF]
, 2013 The paper considers a linear regression model in high-dimension for which the predictive variables can change the influence on the response variable at unknown times (called change-points).
Ciuperca, Gabriela
core +4 more sources
Goodness-of-Fit Test in a Structural Errors-in-Variables Model Based on a Score Function
Austrian Journal of Statistics, 2016 A polynomial structural measurement error model is considered. A goodness-of-fit test is constructed based on the quasi-likelihood estimator, which is asymptotically optimal in a large class of estimators. The power of the test is discussed. The test for
Alexander Kukush, Andrii Malenko
doaj +1 more source
On errors-in-variables estimation with unknown noise variance ratio [PDF]
, 2006 We propose an estimation method for an errors-in-variables model with unknown input and output noise variances. The main assumption that allows identifiability of the model is clustering of the data into two clusters that are distinct in a certain ...
Kukush, A. +2 more
core +1 more source
Likelihood Inference in the Errors-in-Variables Model
Journal of Multivariate Analysis, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Murphy, S.A., Van Der Vaart, A.W.
openaire +1 more source
Inference on semiparametric models with discrete regressors [PDF]
, 1993 We study statistical properties of coefficient estimates of the partially linear regression model when some or all regressors, in the unknown part of the model, are discrete. The method does not require smoothing in the discrete variables.
Delgado, Miguel A., Mora, Juan
core +5 more sources
, 2015 The Cramer-Rao lower bound for the estimation of the affine transformation parameters in a multivariate heteroscedastic errors-in-variables model is derived.
Cohen, E. A. K., Kim, D., Ober, R. J.
core +1 more source
Multicollinearity and Model Misspecification
Sociological Science, 2016 Multicollinearity in linear regression is typically thought of as a problem of large standard errors due to near-linear dependencies among independent variables. This problem can be solved by more informative data, possibly in the form of a larger sample.
Christopher Winship, Bruce Western
doaj +1 more source
Empirical Strategies to Eliminate Life-Cycle Bias in the Intergenerational Elasticity of Earnings Literature [PDF]
I argue that the empirical strategies for estimation of the intergenerational elasticity of lifetime earnings that are currently employed in the literature might not eliminate bias arising from life-cycle effects.
Jan Leonard Stuhler
core +3 more sources