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Comparing composite likelihood methods based on pairs for spatial Gaussian random fieldsM
Covariance estimation Geostatistics Large datasets Tapering
2013/6/14
In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when...
In this note the relation between the range-renewal speed and entropy for i.i.d. models is discussed.
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
On adaptive posterior concentration rates
Bayesian nonparametrics minimax adaptive estimation poste-rior concentration rates sup-norm rates of convergence
2013/6/14
We investigate the problem of deriving posterior concentration rates under different loss functions in nonparametric Bayes. We first provide a lower bound on posterior coverages of shrinking neighbour...
A Semiparametric Estimator for Long-Range Dependent Multivariate Processes
Multivariate processes Long-range dependence Semiparametric estimation VARFIMA processes Asymptotic theory
2013/6/14
In this paper we propose a generalization of a class of Gaussian Semiparametric Estimators (GSE) of the fractional differencing parameter for long-range dependent multivariate time series. We generali...
Regularity Properties of High-dimensional Covariate Matrices
high-dimensional regression instrumental variables sparse estimation compressed sensing random matrix re-stricted eigenvalue compatibility,ℓ q sensitivity computational complex-ity NP-hardness
2013/6/14
Regularity properties such as the incoherence condition, the restricted isometry property, compatibility, restricted eigenvalue and $\ell_q$ sensitivity of covariate matrices play a pivotal role in hi...
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Divide and Conquer Kernel Ridge Regression A Distributed Algorithm Minimax Optimal Rates
2013/6/14
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While Wang-Landau and othe...
Risk Measure Estimation On Fiegarch Processes
Long Memory Models Volatility Risk Measure Estimation FIEGARCH Processes
2013/6/17
We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We pre...
Confidence in a Neutrino Mass Hierarchy Determination
Confidence Neutrino Mass Hierarchy Determination
2013/6/17
In the next decade, a number of experiments will attempt to determine the neutrino mass hierarchy. Feasibility studies for such experiments generally determine the expected value of Delta chi^2. As th...
Parametric Stein operators and variance bounds
Chernoff inequality Cramer-Rao inequality parameter of interest Stein charac-terization Stein’s method
2013/6/17
Stein operators are differential operators which arise within the so-called Stein's method for stochastic approximation. We propose a new mechanism for constructing such operators for arbitrary (conti...
Parallelizing Gaussian Process Calculations in R
distributed computation kriging linear algebra
2013/6/14
We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of datasets that can be analyzed. Using a hybrid parallelization approac...
Methods to Calculate the Upper Bound of Gini Coefficient Based on Grouped Data and the Result for China
Gini coefficient Grouped data Upper bound China
2013/6/14
How to give an upper bound, especially the smallest upper bound of Gini coefficient based on grouped data in the absence of income brackets is still a problem not properly solved. This article provide...
We review the Bayesian theory of semiparametric inference following Bickel and Kleijn (2012) and Kleijn and Knapik (2013). After an overview of efficiency in parametric and semiparametric estimation p...