搜索结果: 1-15 共查到“统计学 Filtering”相关记录26条 . 查询时间(0.209 秒)
l_1 Trend Filtering
detrending regularization Hodrick–Prescott fi ltering piecewise linear fi tting
2015/7/9
The problem of estimating underlying trends in time series data arises in a variety of disciplines. In this paper we propose a variation on Hodrick-Prescott (H-P) filtering, a widely used method for t...
A general approach of least squares estimation and optimal filtering
Least squares Optimal filtering Matched filter Noise Optimization Power Spectrum Density
2013/6/17
The least squares method allows fitting parameters of a mathematical model from experimental data. This article proposes a general approach of this method. After introducing the method and giving a fo...
Optimal filtering and the dual process
auxiliary variables Bayesian conjugacy Dirichlet process finite mixture models Cox-Ingersoll-Ross process hidden Markov model,Kalman filters
2013/6/14
We link optimal filtering for hidden Markov models to the notion of duality for Markov processes. We show that when the signal is dual to a process that has two components, one deterministic and one a...
Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal data
Infinite-dimensional Bayesian filtering detection of quasi-periodic phenomena spatio-temporal data
2013/4/27
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a sp...
Efficient particle filtering through residual nudging
Efficient particle filtering residual nudging
2013/5/2
We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging is to monitor, and i...
State estimation under non-Gaussian Levy noise: A modified Kalman filtering method
Kalman filter modified Kalman filter Non-Gaussiannoise L′evy noise state estimation data assimilation
2013/4/28
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\'evy noise is present, the conventional Kalman filter may fail to be effective d...
Top-down particle filtering for Bayesian decision trees
Top-down particle filtering Bayesian decision trees
2013/4/27
Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees,...
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Re-Weighted Dynamic Filtering Time-Varying Signal Estimation
2012/9/17
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
Behavior patterns of online users and the effect on information filtering
bipartite networks reshuffling process information filtering
2011/7/19
Understanding the structure and evolution of web-based user-object bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus o...
Calibration and filtering for multi factor commodity models with seasonality: incorporating panel data from futures contracts
Multi-Factor Commodity Spot Price Stochastic Volatility Milstein Adaptive Markov chain Monte Carlo Particle filter Rao-Blackwellization
2011/6/21
We examine a general multi-factor model for commodity spot prices and futures valuation. We extend
the multi-factor long-short model in [1] and [2] in two important aspects: firstly we allow for both...
Confidence Sets in Time--Series Filtering
Information Theory (cs.IT) Statistics Theory (math.ST)
2010/12/17
The problem of filtering of finite--alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed, such that the resulting set ...
Optimal experiment design in a filtering context with application to sampled network data
Optimal design, Kalman filter random walks
2010/10/19
We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimizatio...
Local Optimality of User Choices and Collaborative Competitive Filtering
Local Optimality User Choices Collaborative Competitive Filtering
2010/10/14
We describe a novel framework for learning recommender models for recommendation systems, which views user-system-item interactions as an opportunity give-and-take process, and encodes both "collabora...
Estimation error for blind Gaussian time series filtering
Asymptotic Statistics Covariance Estimation Time Series
2010/3/10
In the frame of time series analysis, we compute the quadratic error in the
blind estimation of the projection operator for prediction with infinite past. The
estimation is made using only a single ...
Target Detection via Network Filtering
Sparse network Lasso regression network topology target detection
2010/3/18
A method of ‘network filtering’ has been proposed recently to detect the effects of certain external perturbations on the interacting members in a network. However, with large networks, the goal of de...