Titlebook: Complex Data Modeling and Computationally Intensive Statistical Methods; Pietro Mantovan,Piercesare Secchi Book 2010 Springer-Verlag Milan

jungle

Springer Handbook of Petroleum TechnologyAcute Myocardial Infarction”. The main goal of the Programme is the construction and statistical analysis of data coming from the integration of complex clinical and administrative databases concerning patients with Acute Coronary Syndromes treated in the Lombardia region. Clinical data sets arise f

fluffy

Mohamed Rashwan,José Antonio Jardinitimator, three π.-bootstrap algorithms are introduced with the purpose of both simplifying available procedures and of improving efficiency. Results from a simulation study using both natural and artificial data are presented in order to empirically investigate the properties of the provided bootstr

GUEER

Springer Handbook of Power Systemsures. For example, in reproductive studies that involve basal body temperature, a low, high point or sudden changes on the trajectory have important clinical significance in determining the day of ovulation or in causing dysfunctional cycles. It is common to have patient databases with a huge quanti

Abduct

Jean-Pierre Merlet,Clément Gosselin Profto the literature, it results that statisticians and engineers have almost always modeled wear processes by . increments models, which imply that future wear is assumed to depend, at most, on the system’s age. In many cases itseems to be more realistic and appropriate to adopts to chastic models whi

monogamy

Joachim Hertzberg Prof,Raja Chatila Profmodel, for instance, time and space dependence or the smooth effect of covariates. Many well-known statistical models, such as smoothing-spline models, space time models, semiparametric regression, spatial and spatio-temporal models, log-Gaussian Cox models, and geostatistical models are latent Gaus

斗志

清真寺

Henrik I. Christensen,Gregory D. Hagervation and the model parameter. Since, according to the likelihood principle, all the evidence is contained in the joint likelihood function, a predictive likelihood for the future observation is obtained by eliminating the nuisance quantity, namely the unknown model parameter. This paper focuses on

有帮助

Abominate

Debate

Contributions to Statisticshttp://image.papertrans.cn/c/image/231416.jpg