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We construct an integrated probabilistic model to capture interactions between atoms of a nanocomponent. We then use this model to assess reliabilities of nanocomponents with different structures. Several properties of our proposed model are also described under a sparseness condition. The model is an extension of our previous model based on Markovian random field theory. The proposed integrated model is flexible in that pairwise relationship information among atoms as well as features of individual atoms can be easily incorporated. An important feature that distinguishes the integrated probabilistic model from our previous model is that the integrated approach uses all available sources of information with different weights for different types of interaction. In this paper we consider the nanocomponent at a fixed moment of time, say the present moment, and we assume that the present state of the nanocomponent depends only on the present states of its atoms.

Multivariate dependence of spacings of generalized order statistics is studied. It is shown that spacings of generalized order statistics from DFR (IFR) distributions have the CIS (CDS) property. By restricting the choice of the model parameters and strengthening the assumptions on the underlying distribution, stronger dependence relations are established. For instance, if the model parameters are decreasingly ordered and the underlying distribution has a log-convex decreasing (log-concave) hazard rate, then the spacings satisfy the MTP2 (S- MRR2) property. Some consequences of the results are given. In particular, conditions for non-negativity of the best linear unbiased estimator of the scale parameter in a location-scale family are obtained. By applying a result for dual generalized order statistics, we show that in the particular situation of usual order statistics the assumptions can be weakened.

A more general definition of MTP2 (multivariate total positivity of order 2) probability measure is given, without assuming the existence of a density. Under this definition the class of MTP2 measures is proved to be closed under weak convergence. Characterizations of the MTP2 property are proved under this more general definition. Then a precise definition of conditionally increasing measure is provided, and closure under weak convergence of the class of conditionally increasing measures is proved. As an application we investigate MTP2 properties of stationary distributions of Markov chains, which are of interest in actuarial science.

International audience; The authors study new notions of positive dependence that are associated to multivariate stochastic orders of positive dependence introduced recently by Colangelo et al. [J. Multivar. Anal., to appear]. In particular, they discuss the relationship of these new notions to other existing concepts of positive dependence.

Let $(X_{ij}, \mathbf{z}_i), i = 1, 2, \ldots, k, j = 1, 2, \ldots, n_i$, be independent observations such that $\mathbf{z}_i$ is a fixed $r \times 1$ vector $\lbrack r$ can be 0 (no $\mathbf{z}$'s observed) or $1, 2, \ldots, k - 1\rbrack$, and $X_{ij}$ is distributed according to a one-parameter exponential family which is log concave with natural parameter $\theta_i$. We test the hypothesis that $\theta = \mathbf{Z}\beta$, where $\theta = (\theta_1, \ldots, \theta_k)', \mathbf{Z}$ is the matrix whose $i$th row is $\mathbf{z}'_i$ and $\beta = (\beta_1, \ldots, \beta_r)'$ is a vector of parameters. We focus on $r = 2$ and $\mathbf{z}'_i = (1, z_i), i = 1, 2, \ldots, k, z_i < z_{i + 1}$. The null hypothesis on hand is thus of the form $\theta_i = \beta_1 + \beta_2z_i$. In such a case the model under the null hypothesis becomes logistic regression in the binomial case, Poisson regression in the Poisson case and linear regression in the normal case. We consider mostly the one-sided alternative that the second-order differences of the natural parameters are nonnegative. Such testing problems test goodness of fit vs. alternatives in which the natural parameters behave in a convex way. We find classes of tests that are unbiased and that lie in a complete class. We also note that every admissible test of constant size is unbiased. In some discrete situations we find the minimal complete class of unbiased admissible tests. Generalizations and examples are given.

It is shown that the bivariate density of the absolute normal distribution is totally positive of order 2. Necessary and sufficient conditions are given for the trivariate density of the absolute normal distribution to be totally positive of order 2 in pairs of arguments. These results are then used to show that certain generalized bivariate and trivariate t, X-squared and F random variables are associated.