Weighted-average least squares estimation of generalized linear models

Density Weighted Linear Least Squares

for an unknown vector of parameters β0 and an unknown univariate function τ(·). This model is implied by many important limited dependent variable and regression models, as discussed in Ruud (1986) and Stoker (1986). Consistent estimators for β0, up to an unknown scale factor, have been developed by Ruud (1986), Stoker (1986), Powell, Stock, and Stoker (1989), Ichimura (1993), and others. In th...

Frequency weighted generalized total least squares linear prediction for frequency estimation

This paper presents a frequency weighted generalized total least squares linear prediction for estimating closely spaced sinusoids. In this method, the received data is first processed by a pole-zero prefilter and then a generalized total least squares linear prediction is applied to the prefiltered signal. A procedure of optimizing the generalized solution is introduced. By computer simulation...

Penalized least squares versus generalized least squares representations of linear mixed models

The methods in the lme4 package for R for fitting linear mixed models are based on sparse matrix methods, especially the Cholesky decomposition of sparse positive-semidefinite matrices, in a penalized least squares representation of the conditional model for the response given the random effects. The representation is similar to that in Henderson’s mixed-model equations. An alternative represen...

Estimation of Linear Vectorial Semiparametric Models by Least Squares

Abstract. Semiparametric model is a statistical model consisting of both parametric and nonparametric components, which can be looked on as a mixture model. The theoretical properties of this model have been studied extensively, such as large-sample property. However, most researches are based on scalar value, in which the dimension of the observation is one at each moment. In the fields of spa...

Weighted Least Squares Estimation with Missing Data