Predicting LGD distributions with mixed continuous and discrete ordinal outcomesConditional distributionCumulative probability modelLogistic regressionLoss given defaultSemiparametric transformationUnconditional
We propose using a fully data-driven cross-validation approach to choose the bandwidths, and further derive the asymptotic optimality theory. In addition, we also establish the asymptotic distribution and uniform consistency (with convergence rates) for the local linear conditional quantile estimators ...
Beta distributionMixture modelsKernel smoothingWhen considering a mixed probability measure on [0, 1] with atoms in zero and one, the frontier points have to be treated (and "weighted") differently and separately with respect to the interior points. In order to avoid the troublesome consequences ...
In the field of machine tools the combined use of the Finite Element Met-hod and powerful optimization algorithms allows to design machines which are di-stinguished by an optimal stress distribution on fillets, maximum stiffness at the point of processing, or minimum weight of machine components ...
mixingdensity.Themixingdistributioncanbecontinuous,discreteoradistributionwithpositive 36D.KARLIS&E.XEKALAKI probabilityatafinitenumberofpoints,i.e.afinitestepdistribution.Inthesequel,amixturewitha finitestepmixingdistributionistermedak-finitestepmixtureofF,wherekisthenumberof ...
(2015), and a general continuous distribution of capacity is considered by Long et al. (2022). In these three studies, the capacity is assumed to follow either a discrete distribution or continuous distribution. We argue that adverse conditions/factors that cause road capacity degradation do not...
sampleswith shape(n, d), wherenis the number of samples anddis the number of elements per sample. First, specify which of the elements are continuous. If, for instance, the distribution has three elements and the first and last elements are continuous whereas the second element is discrete:...
with normal, gamma, Poisson and binomial margins and builds the vine tree from Gaussian, Student, Clayton and rotated Clayton copula families. It calculates and plots multivariate marginal probability densities, samples from the distribution, estimates the model from the samples and calculates entropy....
Distribution function Survivor function Hazard function Predict marginally with respect to random effects Pearson, deviance, and Anscombe residuals Other postestimation analysis Estimate variance components Intraclass correlation coefficients (ICCs) after logistic , probit , and random-effects models ...
The mixed normal distribution is (1.1)H(x)=(1−ϵ)Φ(x)+ϵΦ(x/K), which has mean 0 and variance 1−ϵ+ϵK2. (Stigler, 1973, finds that the use of the mixed normal dates back at least to Newcomb, 1882, p. 382.) In other words, the mixed normal arises by sampling...