The authors develop score tests of goodness-of-fit for discrete generalized linear models against zero-inflation. The binomial and Poisson models are treated as examples and in the latter case, the proposed test reduces to that of Broek (1995). Some simulation results and an illustrative example are presented. RÉSUMÉ Les auteurs développent des procédures scores permettant de tester l'adéquation de modèles linéaires généralisés discrets lorsque la valeur zéro est en surnombre dans l'échantillon. Les modèles binomial et de Poisson font l'objet d'une attention particulière et, dans ce dernier cas, le test obtenu se ramèneà celui de Broek (1995). Des simulations et un exemple sontégalement présentés.
ABSTRACT. In this study, an inexact inventory nonlinear programming (IINP) model is proposed for supporting electric power system planning under multiple unit prices and uncertain demands. The proposed IINP can deal with uncertainties presented as intervals and address nonlinearities in the objective function. It can also help to solve material supply problem with diverse unit prices as well as what, where, when and how much material should be purchased under uncertainty. Then, the IINP is applied to a case study of energy resources supply planning for an electric power system. Results obtained are useful for supporting (a) determination of reasonable energy resources supply scheme with global solutions, and elimination of step by step comparisons among many purchase schemes, (b) adjustment or justification purchase batches of energy resources supply and facility expansion for power-conversion technologies under different demand levels, and (c) integration of policies regarding energy resources supply, economy objective and environmental protection for in-depth analysis of tradeoff between purchase batch and unit price as well as system cost and risk.
Estimating the medical costs from disease diagnosis to a terminal event is of immense interest to researchers. However, most of existing literature on such research focused on the estimation of cumulative mean function (CMF) for history process. In this paper, the combined scheme of both inverse probability of censoring weighting (IPCW) technique and longitudinal quantile regression model is used to develop a novel procedure to the estimation of cumulative quantile function (CQF) based on history process with time-dependent covariates and right censored time-to-event variable. The consistency of proposed estimator is derived. The extensive simulation study is conducted to investigate the performance of the estimator given in this paper. A medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) is analyzed to illustrate the application of developed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.