|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 13 van 22 gevonden artikelen
| Titel: |
Fitting linear regression models to censored data by least squares and maximum likelihood methods |
| Auteur: |
Chatterjee, S. McLeish, D.L. |
| Verschenen in: |
Communications in statistics |
| Paginering: | Jaargang 15 (1986) nr. 11 pagina's 3227-3243 |
| Jaar: |
1986 |
| Inhoud: |
Several approaches have been suggested for fitting linear regression models to censored data. These include Cox's proportional hazard models based on quasi-likelihoods. Methods of fitting based on least squares and maximum likelihoods have also been proposed. The methods proposed so far all require special purpose optimization routines. We describe an approach here which requires only a modified standard least squares routine. We present methods for fitting a linear regression model to censored data by least squares and method of maximum likelihood. In the least squares method, the censored values are replaced by their expectations, and the residual sum of squares is minimized. Several variants are suggested in the ways in which the expectation is calculated. A parametric (assuming a normal error model) and two non-parametric approaches are described. We also present a method for solving the maximum likelihood equations in the estimation of the regression parameters in the censored regression situation. It is shown that the solutions can be obtained by a recursive algorithm which needs only a least squares routine for optimization. The suggested procesures gain considerably in computational officiency. The Stanford Heart Transplant data is used to illustrate the various methods. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 13 van 22 gevonden artikelen
| << vorige | volgende >> |