A note on reduction of the number of parameters in linear statistical models

Rok, strany: 2012, 143 - 155

In certain settings the mean response is modeled by a linear model using a large number of parameters. Sometimes it is desirable to reduce the number of parameters prior to conducting the experiment and prior to the actual statistical analysis. Essentially, it means to formulate a simpler approximate model to the original "ideal" one. The goal is to find conditions (on the model matrix and covariance matrix) under which the reduction does not influence essentially the data fit. Here we try to develop such conditions in regular linear model without and with linear restraints. We emphasize that these conditions are independent of observed data.

ISO 690:

Kubáček, L. 2012. A note on reduction of the number of parameters in linear statistical models. In Mathematica Slovaca, vol. 62, no.1, pp. 143-155. 0139-9918. DOI: https://doi.org/10.2478/s12175-011-0079-1

APA:

Kubáček, L. (2012). A note on reduction of the number of parameters in linear statistical models. Mathematica Slovaca, 62(1), 143-155. 0139-9918. DOI: https://doi.org/10.2478/s12175-011-0079-1