Recursive residuals in generalised linear models

Invariance Principles for Recursive Residuals

Application of Recursive Least Squares to Efficient Blunder Detection in Linear Models

In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is th...

The distribution of Pearson residuals in generalized linear models

In general, the distribution of residuals cannot be obtained explicitly. We give an asymptotic formula for the density of Pearson residuals in continuous generalized linear models corrected to order n−1, where n is the sample size. We define corrected Pearson residuals for these models that, to this order of approximation, have exactly the same distribution of the true Pearson residuals. Applic...

Parsimonious classification via generalised linear mixed models

We devise a classification algorithm based on generalised linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the result...

Recursive Models of Dynamic Linear Economies Recursive Models of Dynamic Linear Economies