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Register a new userQuantitative asymptotics of graphical projection pursuit
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Elizabeth Meckes
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There is a result of Diaconis and Freedman which says that, in a limiting sense, for large collections of high-dimensional data most one-dimensional projections of the data are approximately Gaussian. This paper gives quantitati
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We investigate the tail behaviour of the steady state distribution of a stochastic recursion that generalises Lindleys recursion. This recursion arises in queuing systems with dependent interarrival and service times, and includes alternating service systems and carousel storage systems as special cases. We obtain precise tail asymptotics in three qualitatively different cases, and compare these with existing results for Lindleys recursion and for alternating service systems.
An urn contains black and red balls. Let $Z_n$ be the proportion of black balls at time $n$ and $0leq L<Uleq 1$ random barriers. At each time $n$, a ball $b_n$ is drawn. If $b_n$ is black and $Z_{n-1}<U$, then $b_n$ is replaced together with a random number $B_n$ of black balls. If $b_n$ is red and $Z_{n-1}>L$, then $b_n$ is replaced together with a random number $R_n$ of red balls. Otherwise, no additional balls are added, and $b_n$ alone is replaced. In this paper, we assume $R_n=B_n$. Then, under mild conditions, it is shown that $Z_noverset{a.s.}longrightarrow Z$ for some random variable $Z$, and begin{gather*} D_n:=sqrt{n},(Z_n-Z)longrightarrowmathcal{N}(0,sigma^2)quadtext{conditionally a.s.} end{gather*} where $sigma^2$ is a certain random variance. Almost sure conditional convergence means that begin{gather*} Pbigl(D_nincdotmidmathcal{G}_nbigr)overset{weakly}longrightarrowmathcal{N}(0,,sigma^2)quadtext{a.s.} end{gather*} where $Pbigl(D_nincdotmidmathcal{G}_nbigr)$ is a regular version of the conditional distribution of $D_n$ given the past $mathcal{G}_n$. Thus, in particular, one obtains $D_nlongrightarrowmathcal{N}(0,sigma^2)$ stably. It is also shown that $L<Z<U$ a.s. and $Z$ has non-atomic distribution.
We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties. Our approach facilitates the unified study of a wide range of density estimators which are subsumed under these two general classes of kernel density estimators. We demonstrate our method by deriving the asymptotic bias, variance, and mean (integrated) squared error of density estimators with gamma, log-normal, Birnbaum-Saunders, inverse Gaussian and reciprocal inverse Gaussian kernels. We propose two new density estimators for positive random variables that yield properly-normalised density estimates. Plugin expressions for bandwidth estimation are provided to facilitate easy exploratory data analysis.
The notion of multivariate total positivity has proved to be useful in finance and psychology but may be too restrictive in other applications. In this paper we propose a concept of local association, where highly connected components in a graphical model are positively associated and study its properties. Our main motivation comes from gene expression data, where graphical models have become a popular exploratory tool. The models are instances of what we term mixed convex exponential families and we show that a mixed dual likelihood estimator has simple exact properties for such families as well as asymptotic properties similar to the maximum likelihood estimator. We further relax the positivity assumption by penalizing negative partial correlations in what we term the positive graphical lasso. Finally, we develop a GOLAZO algorithm based on block-coordinate descent that applies to a number of optimization procedures that arise in the context of graphical models, including the estimation problems described above. We derive results on existence of the optimum for such problems.
Though Gaussian graphical models have been widely used in many scientific fields, limited progress has been made to link graph structures to external covariates because of substantial challenges in theory and computation. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our framework facilitates estimation of both population- and subject-level gene regulatory networks, and detection of how subject-level networks vary with genetic variants and clinical conditions. Our framework accommodates high dimensional responses and covariates, and encourages covariate effects on both the mean and the precision matrix to be sparse. In particular for the precision matrix, we stipulate simultaneous sparsity, i.e., group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and show in both cases that the convergence rate of the estimated precision parameters is faster than that obtained by lasso or group lasso, a desirable property for the sparse group lasso estimation. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients.
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