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Statistical inference for Sobol pick freeze Monte Carlo method
Statistical inference Sobol pick freeze Monte Carlo method
2013/4/28
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
Measures of serial extremal dependence and their estimation
Measures serial extremal dependence their estimation
2013/4/28
The goal of this paper is two-fold: 1. We review classical and recent measures of serial extremal dependence in a strictly stationary time series as well as their estimation. 2. We discuss recent conc...
A Robust Bayesian Dynamic Linear Model to Detect Abrupt Changes in an Economic Time Series: The Case of Puerto Rico
Dynamic Models Consumer Price Index Bayesian Robustness
2013/4/28
Economic indicators time series are usually complex with high frequency data. The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On...
On the Jeffreys-Lindley's paradox
Bayesian inference Testing statistical hypothe-ses Type I error significance level p-value
2013/4/28
This note reassess the dual nature of the Jeffreys-Lindley's paradox and of its considerable impact on both classical and Bayesian statistics, as well as on existing resolutions of the paradox. It als...
Sparse Factor Analysis for Learning and Content Analytics
factor analysis sparse probit regression sparse logistic regression Bayesian latent factor analysis personalized learning
2013/4/28
We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain, and content analytics, which estimate the...
Testing Exponentiality Based on Rényi Entropy With Progressively Type-II Censored Data
Renyi Entropy hazard function Monte Carlo simulation order statistics Type-II progressively censored data
2013/4/28
We express the joint R\'enyi entropy of progressively censored order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint R\'enyi entropy of...
Markov Switching Component ARCH Model: Stability and Forecasting
ARCH models Markov process Stability Component GARCH models Forecasting Bayesian inference Griddy Gibbs sampling
2013/4/28
This paper introduces an extension of the Markov switching ARCH model where the volatility in each state is a convex combination of two different ARCH components with time varying weights with differe...
`Truncate, replicate, sample': a method for creating integer weights for spatial microsimulation
microsimulation integerisation iterative proportional fitting
2013/4/28
Iterative proportional fitting (IPF) is a widely used method for spatial microsimulation. The technique results in non-integer weights for individual rows of data. This is problematic for certain appl...
A delimitation of the support of optimal designs for Kiefer's $φ_p$-class of criteria
Approximate design optimum design support points design algorithm MSC62K05 90C46
2013/4/28
The paper extends the result of Harman and Pronzato [Stat. & Prob. Lett., 77:90--94, 2007], which corresponds to $p=0$, to all strictly concave criteria in Kiefer's $\phi_p$-class. Let $\xi$ be any de...
Power of Change-Point Tests for Long-Range Dependent Data
Change-point problems nonparametric change-point tests Wilcoxon two-sample rank test power of test local alternatives asymptotic relativeefficiency of tests long-range dependent data,long memory functional limit theorem
2013/4/28
We investigate the power of the CUSUM test and the Wilcoxon change-point test for a shift in the mean of a process with long-range dependent noise. We derive analytiv formulas for the power of these t...
Ensembling Classification Models Based on Phalanxes of Variables with Applications in Drug Discovery
classification ranking ensemble random forest cluster pha-lanx
2013/4/28
We have proposed an ensemble method which aggregates over clusters of predictor variables. We form the clusters (we call phalanxes) by joining variables together. The variables in a phalanx are good t...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
A parameter estimation method based on random slow manifolds
Parameter estimation Slow-fast system Random slow manifold Quantifying uncer-tainty Numerical optimization
2013/5/2
A parameter estimation method is devised for a slow-fast stochastic dynamical system, where often only the slow component is observable. By using the observations only on the slow component, the syste...
Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference
Generalized Thompson Sampling Sequential Decision-Making Causal Inference
2013/5/2
Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problem...
Propagation of initial errors on the parameters for linear and Gaussian state space models
Kalman filter Extended Kalman filter State space mod-els Autoregressive process
2013/4/27
For linear and Gaussian state space models parametrized by $\theta_0 \in \Theta \subset \R^{r}, r \geq 1$ corresponding to the vector of parameters of the model, the Kalman filter gives exactly the so...