This article extends the results of Fang and Zeitouni [Electron. J. Probab. 17 (2012a) 18] on branching random walks (BRWs) with Gaussian increments in time inhomogeneous environments. We treat the case where the variance of the increments changes a finite number of times at different scales in $[0,1]$ under a slight restriction. We find the asymptotics of the maximum up to an $O_{\mathbb{P}}(1)$ error and show how the profile of the variance influences the leading order and the logarithmic correction term. A more general result was independently obtained by Mallein [Electron. J. Probab. 20 (2015b) 40] when the law of the increments is not necessarily Gaussian. However, the proof we present here generalizes the approach of Fang and Zeitouni [Electron. J. Probab. 17 (2012a) 18] instead of using the spinal decomposition of the BRW. As such, the proof is easier to understand and more robust in the presence of an approximate branching structure.

Survival models with frailty are used when additional data are non-available to explain the occurrence time of an event of interest. This non-availability may be considered as a random effect related to unobserved explanatory variables, or that cannot be measured, often attributed to environmental or genetic factors. We propose a survival model with frailty based on the Birnbaum–Saunders distribution. This distribution has been widely applied to lifetime data. The random effect is the frailty, which is assumed to follow the Birnbaum–Saunders distribution and introduced on the baseline hazard rate to control the unobservable heterogeneity of the patients. We use the maximum likelihood method to estimate the model parameters and evaluate its performance under different censoring proportions by a Monte Carlo simulation study. Two types of residuals are considered to assess the adequacy of the proposed model. Examples with uncensored and censored real-world data sets illustrate the potential applications of the proposed model.

In this article, we introduce a procedure for selecting variables in principal components analysis. It is developed to identify a small subset of the original variables that best explain the principal components through nonparametric relationships. There are usually some noisy uninformative variables in a dataset, and some variables that are strongly related to one another because of their general dependence. The procedure is designed to be used following the satisfactory initial principal components analysis with all variables, and its aim is to help to interpret the underlying structures. We analyze the asymptotic behavior of the method and provide some examples.

Statistical and probabilistic reasoning enlightens our judgments about uncertainty and the chance or beliefs on the occurrence of random events in everyday life. Therefore, there are scientists working with Probability and Statistics in various fields of knowledge, what favors the formation of scientific network collaborations of researchers with different backgrounds. Here, we propose to describe the Brazilian PhDs who work with probability and statistics. In particular, we analyze national and states collaboration networks of such researchers by calculating different metrics. We show that there is a greater concentration of nodes in and around the cites which host Probability and Statistics graduate programs. Moreover, the states that host P&S Doctoral programs are the most central. We also observe a disparity in the size of the states networks. The clustering coefficient of the national network suggests that this network and regional differences especially with respect to states from South-east and North is not cohesive and, probably, it is in a maturing stage.

We consider a interacting particle system, the Glauber $+$ Kawasaki model. This model is the result of the combination of a fast stirring, the Kawasaki part, and a spin flip process, the Glauber part. This process has a Reaction–Diffusion equation as hydrodynamic limit, as is proven by De Masi and Presutti (Mathematical Methods for Hydrodynamic Limits (1991) Springer). The ergodicity of these dynamics (one well potential) was proven in Brasseco et al. (Amer. Math. Soc. Transl. Ser. 2 198 (2000) 37–49), for any dimension. In this article, we prove the asymptotic exponentiality for certain exit time from a subset of the basin of attraction of the well.

Fisher information is a very important and fundamental criterion in statistical inference especially in optimal and large sample studies in estimation theory. It also plays a key role in physics, thermodynamic, information theory and other applications. In the literature there have been defined two forms of Fisher information: one for the parameters of a distribution function and one for the density function of a distribution. In this paper, we consider a nonnegative continuous random (lifetime) variable $X$ and define a time-dependent Fisher information for density function of the residual random variable associated to $X$. We also propose a time-dependent version of Fisher information distance (relative Fisher information) between the densities of two nonnegative random variables. Several properties of the proposed measures and their relations to other statistical measures are investigated. To illustrate the results various examples are also provided.

Let $(X_{n})$ be a sequence of random variables, adapted to a filtration $(\mathcal{G}_{n})$, and let $\mu_{n}=(1/n)\sum_{i=1}^{n}\delta_{X_{i}}$ and $a_{n}(\cdot)=P(X_{n+1}\in\cdot|\mathcal{G}_{n})$ be the empirical and the predictive measures. We focus on \begin{equation*}\Vert \mu_{n}-a_{n}\Vert =\mathop{\mathrm{sup}}_{B\in\mathcal{D}}\vert\mu_{n}(B)-a_{n}(B)\vert,\end{equation*} where $\mathcal{D}$ is a class of measurable sets. Conditions for $\Vert \mu_{n}-a_{n}\Vert \rightarrow0$, almost surely or in probability, are given. Also, to determine the rate of convergence, the asymptotic behavior of $r_{n}\Vert \mu_{n}-a_{n}\Vert $ is investigated for suitable constants $r_{n}$. Special attention is paid to $r_{n}=\sqrt{n}$ and $r_{n}=\sqrt{\frac{n}{\log\log n}}$. The sequence $(X_{n})$ is exchangeable or, more generally, conditionally identically distributed.

We construct a wavelet estimator for the derivative of a probability density function in the presence of an additive noise and study its $L_{p}$-consistency property.

In this paper, a dimension reduction method is proposed by using the first derivative of the conditional density function of response given predictors. To estimate the central subspace, we propose a direct methodology by taking expectation of the product of predictor and kernel function about response, which helps to capture the directions in the conditional density function. The consistency and asymptotic normality of the proposed estimation methodology are investigated. Furthermore, we conduct some simulations to evaluate the performance of our proposed method and compare with existing methods, and a real data set is analyzed for illustration.

We suggest a modification of the classical Marshall–Olkin’s bivariate exponential distribution considering a possibility of a singularity contribution along arbitrary line through the origin. It serves as a base of a new weaker version of the bivariate lack of memory property, which might be both “aging” and “non-aging” depending on the additional inclination parameter. The corresponding copula is obtained and we establish its disagreement with Lancaster’s phenomena. Characterizations and properties of the novel bivariate memory-less notion are obtained and its applications are discussed. We characterize associated weak multivariate version. The weak bivariate lack of memory property implies restrictions on the marginal distributions. Starting from pre-specified marginals we propose a procedure to build bivariate distributions possessing a weak bivariate lack of memory property and illustrate it by examples. We complement the methodology with closure properties of the new class. We finish with a discussion and suggest several related problems for future research.