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Moment based estimation of stochastic Kronecker graph parameters
Moment based estimation stochastic Kronecker graph
2011/7/5
Stochastic Kronecker graphs supply a parsimonious model for large sparse real world graphs. They can specify the distribution of a large random graph using only three or four parameters.
Maximum Likelihood Estimation in Network Models
beta model polytope of degree sequences random graphs Rasch model p1 model
2011/6/20
We study maximum likelihood estimation for the statistical model for both directed and undirected
random graph models in which the degree sequences areminimal sufficient statistics. In the undirected...
Message-Passing Estimation from Quantized Samples
analog-to-digital conversion approximate message passing belief propagation compressed sensing frames
2011/6/21
Estimation of a vector from quantized linear measurements
is a common problem for which simple linear techniques
are suboptimal—sometimes greatly so. This paper develops
generalized approximate mes...
A semiparametric estimation of copula models based on the method of moments
Moments Copulas Dependence Parametric estimation Archimedean copulas
2011/6/20
Using the classical estimation method of moments, we propose a new semiparametric estima-
tion procedure for multi-parameter copula models. Consistency and asymptotic normality of
the obtained estim...
Coupled risk measures and their empirical estimation when losses follow heavy-tailed distributions
Risk measure Heavy-tailed distribution Distortion risk measure Weighted risk measure Proportional hazards transform Conditional tail expectation Premium calculation principle Index of economic inequality Statistical inference
2011/6/20
Considerable literature has been devoted to developing statistical
inferential results for risk measures, especially for those that are
of the form of L-functionals. However, practical and theoretic...
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Dimension reduction Classi er Variable selection Nonparametric Bayes
2011/6/20
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine
vision, image analysis and many others. When analyzing such data, parametric models are often to...
Parameter estimation in high dimensional Gaussian distributions
high dimensional Gaussian Parameter estimation massive memory
2011/6/20
In order to compute the log-likelihood for high dimensional spatial Gaussian models, it is
necessary to compute the determinant of the large, sparse, symmetric positive definite precision
matrix, Q....
On improved estimation in a conditionally Gaussian regression
Conditionally Gaussian regression model Improved estimation James–Stein procedure Non-Gaussian Ornstein–Uhlenbeck process
2011/6/20
The paper considers the problem of estimating a p ≥ 2 dimensional
mean vector of a multivariate conditionally normal distribution under
quadratic loss. The problem of this type arises when estimatin...
Covariance Matrix Estimation for Stationary Time Series
Autocovariance matrix banding large deviation physical dependence mea-sure short range dependence spectral density stationary process tapering thresholding Toeplitz matrix
2011/6/20
We obtain a sharp convergence rate for banded covariance matrix estimates of stationary
processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices.
We also ...
High Dimensional Covariance Matrix Estimation in Approximate Factor Models
sparse estimation thresholding cross-sectional correlation common factors idiosyncratic seemingly unrelated regression
2011/6/20
The variance covariance matrix plays a central role in the inferential theories
of high dimensional factor models in finance and economics. Popular
regularization methods of directly exploiting spar...
Intensity estimation of non-homogeneous Poisson processes from shifted trajectories
Poisson processes Random shifts Intensity estimation Deconvolution Meyer wavelets Adaptive estimation Besov space Minimax rate
2011/6/20
This paper considers the problem of adaptive estimation of a non-homogeneous intensity
function from the observation of n independent Poisson processes having a common intensity
that is randomly shi...
Recursive bias estimation for multivariate regression smoothers
nonparametric regression；smoother；kernel；thin-plate splines；stopping rules
2011/6/17
This paper presents a practical and simple fully nonparametric multivariate smooth-
ing procedure that adapts to the underlying smoothness of the true regression function. Our
estimator is easily co...
Testing composite hypotheses, Hermite polynomials and optimal estimation of a nonsmooth functional
Best polynomial approximation ℓ 1 norm composite hypothe-ses Hermite polynomial minimax lower bound nonsmooth functional optimal rate of convergence
2011/6/17
A general lower bound is developed for the minimax risk when
estimating an arbitrary functional. The bound is based on testing
two composite hypotheses and is shown to be effective in estimating
th...
Hidden Markov Mixture Autoregressive Models: Parameter Estimation
Hidden Markov Model Mixture Autoregressive Model Parameter Estimation
2011/6/17
This report introduces a parsimonious structure for mixture of au-
toregressive models, where the weighting coefficients are determined
through latent random variables as functions of all past obser...
Estimation for Lévy processes from high frequency data within a long time interval
Adaptive nonparametric estimation high frequency data L´ evy processes projection estimators power variation
2011/6/17
In this paper, we study nonparametric estimation of the L´evy
density for L´evy processes, with and without Brownian component.
For this, we consider n discrete time observations with st...