ǂA ǂpermutation approach to goodness-of-fit testing inregression models

Jakob Peterlin (Avtor), Janez Stare (Avtor), Rok Blagus (Avtor)

Povzetek

Model checking plays an important role in parametric regression asmodel misspecification seriously affects the validity and efficiency ofregression analysis. Model checks can be performed by constructingan empirical process from the model’s fitted values and residuals.Due to a complex covariance function of the process obtaining theexact distribution of the test statistic is, however, intractable. Sev-eral solutions to overcome this have been proposed. It was shownthat the simulation and bootstrap-based approaches are asymptoti-cally valid, however, we show by using simulations that the rate ofconvergence can be slow. We, therefore, propose to estimate thenull distribution by using a novel permutation-based procedure. Weprove, under some mild assumptions, that this yields consistent testsunder the null and some alternative hypotheses. Small sample prop-erties of the proposed approach are studied in an extensive MonteCarlo simulation study and real data illustration is also provided.

Ključne besede

asimptotična konvergenca;naključne permutacije;stohastični procesi;asymptotic convergence;random permutations;stochastic processes;

Podatki

Jezik:	Angleški jezik
Leto izida:	2023
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL MF - Medicinska fakulteta
UDK:	61
COBISS:	140453123
ISSN:	0233-1888
Št. ogledov:	50
Št. prenosov:	8
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	asimptotična konvergenca;naključne permutacije;stohastični procesi;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 123-149
Letnik:	ǂVol. ǂ57
Zvezek:	ǂiss. ǂ1
Čas izdaje:	2023
DOI:	10.1080/02331888.2023.2172173
ID:	22400387