CiteExport$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt196",{id:"formSmash:upper:j_idt196",widgetVar:"widget_formSmash_upper_j_idt196",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:exportLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt197_j_idt199",{id:"formSmash:upper:j_idt197:j_idt199",widgetVar:"widget_formSmash_upper_j_idt197_j_idt199",target:"formSmash:upper:j_idt197:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

On the variance parameter estimator in general linear modelsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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(English)Manuscript (preprint) (Other academic)
##### Abstract [en]

##### Keywords [en]

General linear models, non-Gaussian error terms, moments of variance parameter estimators, finite sample size bounds, random covariates, unconditional bounds
##### National Category

Mathematics
##### Research subject

Mathematical Statistics
##### Identifiers

URN: urn:nbn:se:su:diva-174299OAI: oai:DiVA.org:su-174299DiVA, id: diva2:1358030
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt517",{id:"formSmash:j_idt517",widgetVar:"widget_formSmash_j_idt517",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt523",{id:"formSmash:j_idt523",widgetVar:"widget_formSmash_j_idt523",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt529",{id:"formSmash:j_idt529",widgetVar:"widget_formSmash_j_idt529",multiple:true}); Available from: 2019-10-06 Created: 2019-10-06 Last updated: 2019-10-13Bibliographically approved
##### In thesis

In the present note we consider general linear models where the covariates may be both random and non-random, and where the only restrictions on the error terms are that they are independent and have finite fourth moments. For this class of models we analyse the variance parameter estimator. In particular we obtain finite sample size bounds for the variance of the variance parameter estimator which are independent of covariate information regardless of whether the covariates are random or not. For the case with random covariates this immediately yields bounds on the unconditional variance of the variance estimator *—* a situation which in general is analytically intractable. The situation with random covariates is illustrated in an example where a certain vector autoregressive model which appears naturally within the area of insurance mathematics is analysed. Further, the obtained bounds are sharp in the sense that both the lower and upper bound will converge to the same asymptotic limit when scaled with the sample size. By using the derived bounds it is simple to show convergence in mean square of the variance parameter estimator for both random and non-random covariates. Moreover, the derivation of the bounds for the above general linear model is based on a lemma which applies in greater generality. This is illustrated by applying the used techniques to a class of mixed effects models.

1. Micro-level claims reserving in non-life insurance$(function(){PrimeFaces.cw("OverlayPanel","overlay1360444",{id:"formSmash:j_idt809:0:j_idt813",widgetVar:"overlay1360444",target:"formSmash:j_idt809:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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