Results 251 to 260 of about 77,552 (290)

Regression Diagnostic under Model Misspecification

Journal of Applied Statistics, 2007
We propose two novel diagnostic measures for the detection of influential observations for regression parameters in linear regression. Traditional diagnostic statistics focus on the effect of deletion of data points either on parameter estimates, or on predicted values.
Li-Chu, Chien, Tsung-Shan, Tsou
openaire   +2 more sources

Model Misspecification and Multipoint Linkage Analysis

Human Heredity, 1992
Pairwise linkage analysis is robust to genetic model misspecification provided dominance is correctly specified, the primary effect being inflation of the recombination fraction. By contrast, we show that multipoint analysis under misspecified models is not robust when a putative disease locus is placed between close flanking markers, with potentially ...
N, Risch, L, Giuffra
openaire   +2 more sources

The Misspecification of Arma Models

Statistica Neerlandica, 1989
The object of this paper is to assess the effects of fitting a model of the wrong order to a time series which is generated by an autoregressive moving–average process. The method is to examine the spectral density functions which are indicated by the probability limits of the least–squares estimators of the misspecified models.
openaire   +2 more sources

Higher‐order asymptotics under model misspecification

Canadian Journal of Statistics, 1996
AbstractMost of the higher‐order asymptotic results in statistical inference available in the literature assume model correctness. The aim of this paper is to develop higher‐order results under model misspecification. The density functions to O(n−3/2) of the robust score test statistic and the robust Wald test statistic are derived under the null ...
Viraswami, K., Reid, N.
openaire   +2 more sources

Model misspecification in proportional hazards regression

Biometrika, 1995
Summary: The proportional hazards model is frequently used to evaluate the effect of treatment on failure time events in randomised clinical trials. Concomitant variables are usually available and may be considered for use in the primary analyses under the assumption that incorporating them may reduce bias or improve efficiency.
Anderson, Garnet L., Fleming, Thomas R.
openaire   +2 more sources

Diagnostics of model misspecification

1996
In section 1.2, we denoted the true regression relationship between Y and x by µ(x), and we noted that µ(x) is generally unknown, although in practice it is approximated by a parametric function f (x, θ). Because we use this f to make some statistical inferences, however, we must be able to determine whether or not our choice of f is accurate.
Sylvie Huet, Annie Bouvier, Marie-Anne Gruet, Emmanuel Jolivet   +3 more
openaire   +1 more source

Model misspecification in parametric dual modeling

Journal of Statistical Computation and Simulation, 2000
In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances.
Timothy J. Robinson, Jeffery B. Birch
openaire   +1 more source

Model misspecification in Data Envelopment Analysis

Annals of Operations Research, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Airline overbooking models with misspecification

Journal of Air Transport Management, 2011
Abstract This paper looks at static overbooking models. In theory, a random show demand follows a binomial distribution with each reservation showing up independently and with the same probability. However, in practice, some overbooking models assume that the show demand is the product of the overbooking level and the random show-up rate.
Kannapha Amaruchkul, Patipan Sae-Lim
openaire   +1 more source

KPSS test and model misspecifications

Applied Economics Letters, 2009
The KPSS test is very popular and used extensively by practitioners. The test considers two models under the null: stationarity around a deterministic level or around a deterministic trend. There is no study, as far as we know, on the statistical properties of the test when the wrong model is used. This article endeavour to fill this gap.
Kaddour Hadri, Yao Rao
openaire   +1 more source