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Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/25
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
Maximum-Likelihood Estimation For Diffusion Processes Via Closed-Form Density Expansions
asymptotic expansion diffusion discrete observation maximum-likelihood estimation transition density
2016/1/20
This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data. A closed-form asymptotic expansion for tran...
Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions
Conditional characteristic function Diffusion processes Empirical likelihood Kernel smoothing L′ evy driven processes
2016/1/19
Markov processes are used in a wide range of disciplines including finance.The transition densities of these processes are often unknown. However, the conditionalcharacteristic functions are more like...
Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
Distributed Estimation via Dual Decomposition
Distributed Estimation via Dual Decomposition
2015/7/10
The focus of this paper is to develop a framework for distributed estimation via convex optimization. We deal with a network of complex sensor subsystems with local estimation and signal processing. M...
Generalized Chebyshev Bounds via Semidefinite Programming
semidefi nite programming convex optimization duality theory Chebyshev inequalities
2015/7/10
A sharp lower bound on the probability of a set defined by quadratic inequalities, given the first two moments of the distribution, can be efficiently computed using convex optimization. This result g...
Robust Beamforming via Worst-Case SINR Maximization
Beamforming convex optimization signal-to-interference-plus-noise ratio (SINR)
2015/7/9
Minimum variance beamforming, which uses a weight vector that maximizes the signal to interference plus noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, st...
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Distributed Optimization Statistical Learning via Alternating Direction Method Multipliers
2015/7/9
Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasi...
This paper is concerned with how coupling can be used to enhance the efficiency of a certain class of terminating simulations, in Markov process settings in which the stationary distribution is known....
Estimating Tail Probabilities in Queues via Extremal Statistics
Extreme values queues regenerative processes rare events estimation asymptotics
2015/7/8
We study the estimation of tail probabilities in a queue via a semi-parametric estimator based on the maximum value of the workload, observed over the sampled time interval. Logarithmic consistency an...
Modern statistical estimation via oracle inequalities
Modern statistical estimation oracle inequalities
2015/6/17
A number of fundamental results in modern statistical theory involve thresholding estimators. This survey paper aims at reconstructing the history of how thresholding rules came to be popular in stati...
Supplementary materials for Statistical Estimation and Testing via the Sorted 1 Norm
Supplementary materials Statistical Estimation Sorted 1 Norm
2015/6/17
In this note we give a proof showing that even though the number of false discoveries and the total number of discoveries are not continuous functions of the parameters, the formulas we obtain for the...
Controlling the False Discovery Rate via Knockoffs
Variable selection false discovery rate (FDR) sequential hypothesis testing martingale theory permutation methods Lasso
2015/6/17
In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to discover which variables are truly associated wi...
Asymptotic behavior of the magnetization near critical and tricritical points via Ginzburg-Landau polynomials
Asymptotic behavior of magnetization gold landau lattice model of the spin
2014/12/25
The purpose of this paper is to prove connections among the asymptotic behavior of the magnetization, the structure of the phase transitions, and a class of polynomials that we call the Ginzburg–Landa...
Nucleation of Breathers via Stochastic Resonance in Nonlinear Lattices
Staggered driving force the nonlinear discrete model discrete respirator resonance phenomenon
2014/12/24
By applying a staggered driving force in a prototypical discrete model with a quartic nonlinearity, we demonstrate the spontaneous formation and destruction of discrete breathers with a selected frequ...