搜索结果: 16-30 共查到“统计学 prediction”相关记录61条 . 查询时间(0.194 秒)
On the prediction of functional time series
Dimension reduction Forecasting, Functional autoregressions Functional principal components, Functional time series Particulate matter Vector autoregressions
2012/9/17
This paper addresses the prediction of functional time series. Existing contributions to this problem have largely focused on the special case of rst-order functional autoregressive processes because...
Prediction and Computer Model Calibration Using Outputs From Multi-fidelity Simulators
Computer Experiment Gaussian process Markov Chain Monte Carlo.
2012/9/17
Computer codes are widely used to describe physical processes in lieu of physical observations.In some cases, more than one computer simulator, each with di erent degrees of delity, can be used to ex...
PAC-Bayesian Estimation and Prediction in Sparse Additive Models
Additive models sparsity regression estimation PAC-Bayesian bounds oracle inequality MCMC stochastic search.
2012/9/17
The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p nparadigm).A PAC-Bayesian strategy is investigated, delivering oracle inequaliti...
A statistical view on team handball results: home advantage, team fitness and prediction of match outcomes
Time series analysis of sports results statistics interdisciplinary applications of physics
2012/9/18
We analyze the results of the German Team Handball Bundesliga for ten seasons in a model-free statistical time series approach. We will show that the home advantage is nearly negligible compared to th...
Comparison of SCIPUFF Plume Prediction with Particle Filter Assimilated Prediction for Dipole Pride 26 Data
Data Assimilation Particle Filter
2011/7/19
This paper presents the application of a particle filter for data assimilation in the context of puff-based dispersion models. Particle filters provide estimates of the higher moments, and are well su...
Identifying and understanding modular organizations is centrally important in the study of complex systems. Several approaches to this problem have been advanced, many framed in information-theoretic ...
Minimax Policies for Combinatorial Prediction Games
Minimax Policies Combinatorial Prediction Games
2011/6/20
We address the online linear optimization problem when the actions of the forecaster are represented by
binary vectors. Our goal is to understand the magnitude of the minimax regret for the worst pos...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
high-dimensional linear model out-of-sample estimators
2011/3/21
We study the unconditional out-of-sample prediction error (predictive risk) associated with two classes of smooth shrinkage estimators for the linear model: James-Stein type shrinkage estimators and r...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
Shrinkage estimators for out-of-sample high-dimensional linear models
2011/3/23
We study the unconditional out-of-sample prediction error (predictive risk) associated with two classes of smooth shrinkage estimators for the linear model: James-Stein type shrinkage estimators and r...
We study the problem of sequential prediction of categorical data and discuss a generalisation of Blackwell's algorithm on 0-1 data. The arguments are based on Blackwell's approachability results give...
An Introduction to Artificial Prediction Markets for Classification
online learning supervised learning random forest implicit online learning
2011/3/18
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learn...
No-Regret Reductions for Imitation Learning and Structured Prediction
No-Regret Reductions for Imitation Learning Structured Prediction
2010/11/9
Sequential prediction problems such as imitation learning, where future observations depend on
previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning.
Online Multiple Kernel Learning for Structured Prediction
Online Multiple Kernel Learning r Structured Prediction
2010/10/19
Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning...
Error Prediction and Model Selection via Unbalanced Expander Graphs
Error Prediction Model Selection Unbalanced Expander Graphs
2010/10/19
We investigate deterministic design matrices for the fundamental problems of error prediction and model selection. Our deterministic design matrices are constructed from unbalanced expander graphs, a...
Prediction and variable selection with the adaptive Lasso
adaptive Lasso prediction restricted eigenvalue thresholding variable selection
2010/3/9
We revisit the adaptive Lasso in a high-dimensional linear model,
and provide bounds for its prediction error and for its number of false positive
selections. We compare the adaptive Lasso with an “...