搜索结果: 1-15 共查到“统计学 least squares”相关记录21条 . 查询时间(0.125 秒)
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...
A least-squares method for sparse low rank approximation of multivariate functions
least-squares method sparse low rank approximation multivariate functions
2013/6/14
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regul...
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Efficient Regularized；Least-Squares；Algorithms；Conditional Ranking；Relational Data
2012/11/23
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular ta...
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Ridge Regression dimensional subspace
2011/6/16
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which
one simply projects the data onto a finite dimensional subspace (as specified by a Principal
Component...
A Generalized Least Squares Matrix Decomposition
matrix decomposition,singular value decomposition,transposable data,principal components analysis,sparse principal components analysis,functional prin-cipal components analysis,spatio-temporal data
2011/3/21
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
A Generalized Least Squares Matrix Decomposition
matrix decomposition singular value decomposition transposable data principal components analysis, sparse principal components analysis functional prin-cipal components analysis spatio-temporal data
2011/3/23
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
We consider the problem of robustly predicting as well as the best linear combination of d given functions in least squares regression, and variants of this problem including constraints on the param...
The Degrees of Freedom of Partial Least Squares Regression
regression model selection Partial Least Squares Degrees of Freedom
2010/3/10
The derivation of statistical properties for Partial Least Squares regression
can be a challenging task. The reason is that the construction of latent compo-
nents from the predictor variables also ...
Kernel Partial Least Squares is Universally Consistent
Kernel Partial Least Squares Universally Consistent
2010/3/18
We prove the statistical consistency of kernel Partial Least Squares
Regression applied to a bounded regression learning problem on a re-
producing kernel Hilbert space. Partial Least Squares stands...
Conditional least squares estimation in nonstationary nonlinear stochastic regression models
Stochastic nonlinear regression heteroscedasticity nonstation-ary process time series branching process conditional least squares estimator quasi-likelihood estimator
2010/3/9
National Agronomical Research Institute (INRA)
Let {Zn} be a real nonstationary stochastic process such that
E(Zn|Fn−1)a.s.< 1 and E(Z2n |Fn−1)a.s.< 1, where {Fn} is an increas-
ing seq...
On the unified theory of least squares。
Weighted least-squares estimators of tail indices
Weighted least-squares estimators tail indices
2009/9/22
We propose a class of weighted least-squares estimators
for the tail index of a regularly varying upper tail of a distribution.
Universal asymptotic normality of the estimators is established over
...
KALMAN-TYPE RECURSIONS FOR TIME-VARYING ARMA MODELS AND THEIR IMPLICATION FOR LEAST SQUARES PROCEDURE
Kalman-type recursions least squares procedure state-space representations time-varying ARMA models
2009/9/18
This paper is devoted to ARMA models with timedependent
coefficients, including well-known periodic ARMA models. We
provide state-space representations and Kalman-type recursions to derive a
Wold–C...
Least squares type estimation of the transition density of a particular hidden Markov chain
Hidden Markov Chain Transition Density Nonparametric Estimation Model Selection Rate of convergence
2009/9/16
In this paper, we study the following model of hidden Markov chain: $Y_i=X_i+varepsilon_i$, $ i=1,dots,n+1$ with $(X_i)$ a real-valued stationary Markov chain and $(varepsilon_i)_{1leq ileq n+1}$ a no...
Weighted least squares methods for prediction in the functional data linear model
Cross-validation eigenfunction eigenvector functional data analysis mean squared error orthogonal series rate of convergence
2009/9/16
The problem of prediction in functional linear regression is conventionally addressed by reducing dimension via the standard principal component basis. In this paper we show that weighted least-square...