... total **least** **squares** **estimator**, deﬁned via a constrained optimization problem, is a generalization of the total **least** **squares** **estimator** when the data matrix and the applied ...

44

... the **least** **squares** **estimator** in the nonlinear regression model are established, which extend the corresponding ones for independent errors and some dependent ...

11

... ordinary **least** **squares** **estimator** of the long-memory parameter from a fractionally integrated process that is an alternative to the Geweke and Porter-Hudak (1983) ...ordinary **least** ...

17

... The aim of this work is to review the paper by Hellerstein & Imbens (1982) focusing on the use of auxiliary data and a formal derivation of the asymptotic properties of the underlying Weighted **Least** ...

14

... We simulated N = 1000 independent replications of length n = 500 and n = 2000 of INARCH(q) models, and compared the finite-sample performance of the following estimators: the PQMLE (1.7), the NBQMLE (1.8) with r=1, the ...

44

... The simulation results indicate that the estimator is, 1 easy-tocompute and fast, 2 insensitive to initial estimates, 3 appears to be \/-consistent and asymptotically normal, and, 4 bett[r] ...

32

... We consider numerical methods for the solution of the optimization problem (5). One approach is to use standard algorithms for local optimization. The choice of the optimization method is inspired by the need to use as ...

18

... likelihood **estimator**, the **least** **squares** **estimator** and the weighted **least** **squares** **estimator**, which do not utilize the conditional moment ...

13

... adjusted **least** **squares** **estimator** is derived that yields a consistent estimate of the parameters of an implicit quadratic measurement error ...consistent **estimator** for the measurement error ...

25

... the **least** **squares** **estimator**, they proposed a class of weighted jackknife variance estimators for the **least** **squares** **estimator** by deleting any fixed number of observations at a ...

11

... The paper establishes the conditions under which the generalised **least** **squares** estima- tor of the regression parameters is equivalent to the weighted **least** **squares** **estimator**. The ...

23

... In practical regression analysis, researchers often encounter the problem of multicollinearity. In case of multicollinearity we know that when the correlation matrix has one or more small eigenvalues, the estimates of ...

10

... kernel **estimator** under substantially more restrictive conditions on the data generating process, Robinson (1987) estimated the residual variances of unknown function of the explanatory variables by nearest ...

7

... Carter and Yang (1986) derived the asymptotic distribution of the estimated generalized least squares estimator as either n, the number of experimental units, tends to infinity and/or as[r] ...

27

... For exhibiting dependence among the observations within the same subject, the paper considers the estimation problems of partially linear models for longitudinal data with the -mixing and r -mixing error structures, ...

18

... the **least** **squares** **estimator**, they proposed a class of weighted jackknife variance estimators for the **least** **squares** **estimator** by deleting any fixed number of observations at a ...

8

... In this paper, we study the Maximum Likelihood Estimation (MLE) and Ordinary **Least** **Squares** **Estimator** (OLSE) methods for estimation of the unknown parameters of the modified Weibull distribution. A ...

7

... the **least** **squares** method is used to obtain the explicit formula of the **estimator** and the estimation error is given as ...the **least** **squares** **estimator** is proved by applying the ...

6

... pretest **estimator** that has inferior properties, and its use can be harmful (see Danilov and Magnus, ...ordinary **least**-**squares** (OLS) **estimator** for linear regression models with homoscedastic ...

12

... the **least** **squares** **estimator** of b and the nonparametric **estimator** of g(t) based on NA samples, Hu [12] obtained the consistency and complete consistency for these esti- mations based on the ...

13