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Renorming divergent perpetuities
convergence in distribution perpetuity stochastic difference equation
2011/7/19
We consider a sequence of random variables $(R_n)$ defined by the recurrence $R_n=Q_n+M_nR_{n-1}$, $n\ge1$, where $R_0$ is arbitrary and $(Q_n,M_n)$, $n\ge1$, are i.i.d. copies of a two-dimensional ra...
A central limit theorem for adaptive and interacting Markov chains
MCMC interacting MCMC Limit theorems
2011/7/19
Adaptive and interacting Markov Chains Monte Carlo (MCMC) algorithms are a novel class of non-Markovian algorithms aimed at improving the simulation efficiency for complicated target distributions.
Functional kernel estimators of large conditional quantiles
Conditional quantiles heavy-tailed distributions functional kernel estimator
2011/7/19
We address the estimation of conditional quantiles when the covariate is functional and when the order of the quantiles converges to one as the sample size increases.
Maximum-Likelihood Non-Decreasing Response Estimates
maximum likelihood unimodal PDF families
2011/7/19
Let $x_{i,j}$, $1 \le i \le m$, $1 \le j \le n_i$, be observations from a doubly-indexed sequence $\{X_{i,j}\}$ of independent random variables (all of them discrete, or all of them absolutely continu...
Model selection by LASSO methods in a change-point model
change-points selection criterion asymptotic behavior
2011/7/19
The paper considers a linear regression model with multiple change-points occurring at unknown times.
Estimating Failure Probabilities
asymptotic normality exceedance probability failure set homogeneity
2011/7/19
In risk management often the probability must be estimated that a random vector falls into an extreme failure set. In the framework of bivariate extreme value theory, we construct an estimator for suc...
Expectiles for subordinated Gaussian processes with applications
expectiles robustness local shift sensitivity
2011/7/19
In this paper, we introduce a new class of estimators of the Hurst exponent of the fractional Brownian motion (fBm) process.
Riesz measures and Wishart laws associated to quadratic maps
convex cones homogeneous cones Riesz measures Wishart laws
2011/7/19
We introduce a natural definition of Riesz measures and Wishart laws associated to an $\Omega$-positive (virtual) quadratic map, where $\Omega \subset \real^n$ is a regular open convex cone.
Proportionate vs disproportionate distribution of wealth of two individuals in a tempered Paretian ensemble
Pareto law Paretian ensemble Truncated wealth distribution
2011/7/7
We study the distribution P(\omega) of the random variable \omega = x_1/(x_1 + x_2), where x_1 and x_2 are the wealths of two individuals selected at random from the same tempered Paretian ensemble ch...
abc: an R package for Approximate Bayesian Computation (ABC)
abc package Approximate Bayesian Computation
2011/7/7
Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data.
Co-evolution of Selection and Influence in Social Networks
Co-evolution Selection Influence Social Networks
2011/7/7
Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both nod...
Censored Truncated Sequential Spectrum Sensing for Cognitive Radio Networks
Censored Truncated Sequential Spectrum Sensing Cognitive Radio Networks
2011/7/6
Reliable spectrum sensing is a key functionality of a cognitive radio network. Cooperative spectrum sensing improves the detection reliability of a cognitive radio system but also increases the system...
Large-Scale Convex Minimization with a Low-Rank Constraint
Large-Scale Convex Minimization Low-Rank Constraint
2011/7/6
We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient greedy algorithm and ...
Bayesian and L1 Approaches to Sparse Unsupervised Learning
Bayesian L1 Approaches Sparse Unsupervised Learning
2011/7/6
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborat...
The reinforcing influence of recommendations on global diversification
reinforcing influence recommendations global diversification
2011/7/6
Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate populari...