[PDF] Top 20 Semi and non semi parametric models for reliability analysis

... The estimated regression coefficients together with GCV statistics is computed from equ. (7) and equ. (8). The standard error for the LASSO estimates can be obtained by using the approximately given in the equ. (7). The ... See full document

... quantitative analysis of each working subsystem of the plant ...Further reliability & availability analysis can beneficial for the industry in terms of higher productivity & lower maintenance ... See full document

... two models are determined using a radio-fingerprints database and kernel-based learning ...a non- parametric regression model, while the second one is a semi-parametric regression model ... See full document

... survival analysis that consider univari- ate prognostic indices derived from the semi-parametric Cox proportional hazards ...of non-proportionality (monotone, converging or diverging hazard ... See full document

... a non-parametric method. Charnes et al. (1978) developed a non-parametric technique which is known as data envelopment analysis ...using parametric truncated ... See full document

... sion models have been considered by Prentice et al [11] and Andersen and Gill [12] in their seminal studies in this ...novel semi-para- metric proportional hazards model and Lin [14] pre- sented general ... See full document

... ROC analysis is to determine the optimal cut-off values using equal or unequal weight for FPF and FNF ...of parametric, semi-parametric non-parametric have been proposed for ... See full document

... survival analysis, there are two types of model, parametric and ...For parametric models the survival data is described by a known non negative ...Survival models that take into ... See full document

... such models for applications to risk-management and portfolio ...Other models such as Multivariate GARCH by Engle and Kroner (1995) and Dynamic Conditional Correlation (DCC) of Engle (2002) have also been ... See full document

... the semi-parametric models for the diseased and non-diseased populations by using the normal approximation, bootstrap and empirical likelihood ... See full document

... the analysis of failure tim e d a ta [30] assume th a t each individual experiences at most one failure and th a t the failure times of different individuals are indepen­ ...naive analysis assumes th a t ... See full document

... adopt non-parametric reward model, are inefficient, due to the large number of ...a parametric reward model with arm features, contextual ban- dit algorithms are more efficient, but they can also ... See full document

... the parametric model improves model fit or not and also selection of ...the parametric cases, the best fitting model can be selected by employing a commonly used selection crite- ria, Akaike’s Information ... See full document

... the analysis of population dynamics, most research still relies on meth- ods such as empirical calculations and linear models to do so which are open to issues such as susceptibility to noise and assuming a ... See full document

... identified models have received a growing attention in recent years in the econometric literature, due to their broad applications in empirical ...a parametric model, by specifying an ad-hoc ... See full document

... The problem of finding a representation of multivariate random variables which maintains its essential distributional structure using a set of lower dimensional random variables has been of interest to researchers in ... See full document

... Probabilistic models have been used for semi-supervised learning, by fitting data samples with certain assumption about the marginal distribution (Ghazvininejad et ... See full document

... the semi-parametric additive hazard model of Lin and Ying (1994), where all covariate effects are assumed to be time-independent, was developed by Li et ...the non-parametric additive hazard ... See full document

... the models differs considerably with respect to the measure used for evaluating their perfor- ...well-performing models in only 44% of the evaluation tasks, and applying just this model means it would ... See full document

... Longitudinal studies commonly assume that the data-collection schedule is independent of a sub- ject’s outcomes and measured or unmeasured characteristics. However, this independence as- sumption may be violated if ... See full document