Search results for "maximum likelihood"
showing 3 items of 53 documents
2019
The Trier Inventory for Chronic Stress (TICS), consisting of 57 items, is an instrument for measuring chronic stress in nine areas. There is also a short form (SSCS) of the TICS consisting of 12 items. However, this 12-item short form does not include all nine areas of the theoretical model and the long version. Therefore, a short version including all nine scales/areas was investigated. The TICS was taken by a sample of N = 2,473 respondents from the general population, aged 14 to 99, selected by random-route sampling. Confirmatory factor analyses applying robust maximum likelihood estimations (MLM) tested the model fit. The one-factor-model proposed by the original authors was tested, and…
CALIBRATION OF LÉVY PROCESSES USING OPTIMAL CONTROL OF KOLMOGOROV EQUATIONS WITH PERIODIC BOUNDARY CONDITIONS
2018
We present an optimal control approach to the problem of model calibration for L\'evy processes based on a non parametric estimation procedure. The calibration problem is of considerable interest in mathematical finance and beyond. Calibration of L\'evy processes is particularly challenging as the jump distribution is given by an arbitrary L\'evy measure, which form a infinite dimensional space. In this work, we follow an approach which is related to the maximum likelihood theory of sieves. The sampling of the L\'evy process is modelled as independent observations of the stochastic process at some terminal time $T$. We use a generic spline discretization of the L\'evy jump measure and selec…
SNP and SML estimation of univariate and bivariate binary–choice models
2008
We discuss the semi-nonparametric approach of Gallant and Nychka (1987, Econometrica 55: 363–390), the semiparametric maximum likelihood approach of Klein and Spady (1993, Econometrica 61: 387–421), and a set of new Stata commands for semiparametric estimation of three binary-choice models. The first is a univariate model, while the second and the third are bivariate models without and with sample selection, respectively. The proposed estimators are root-n consistent and asymptotically normal for the model parameters of interest under weak assumptions on the distribution of the underlying error terms. Our Monte Carlo simulations suggest that the efficiency losses of the semi-nonparametric a…