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Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/26
When the functional data are not homogeneous, e.g., there exist multiple classes of func-tional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimati...
On Smoothing Estimation For Seasonal Times Series With Long Cycles
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/25
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/20
When the functional data are not homogeneous, e.g., there exist multiple classes of func-tional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimati...
On Smoothing Estimation For Seasonal Times Series With Long Cycles
Kernel estimator M-dependent seasonal-dummy ap- proach
2016/1/20
We consider a kernel smoothing estimator to the periodic component of seasonal time series which have quite large periodicity relative to the length of the time series. The estimator is formulated by ...
Thee Essays on Hedge Fund Fee Structure, Return Smoothing and Gross Performance
Three Essays Hedge Fund Fee Structure Return Smoothing Gross Performance
2014/10/28
Hedge funds feature sp ecial comp ensation structure compared to traditional investments. Previous studies mainly fo cus on the provisions and incentive structure of hedge fund contract, such as 2/20,...
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
Optimization viewpoint Kalman smoothing applications robust sparse estimation
2013/4/28
In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate ...
Smoothing effect of Compound Poisson approximation to distribution of weighted sums
characteristic function concentration function compound Poisson distribution Kolmogorov norm weighted random variables.
2013/4/27
The accuracy of compound Poisson approximation to the sum $S=w_1S_1+w_2S_2+...+w_NS_N$ is estimated.
Here $S_i$ are sums of independent or weakly dependent random variables, and $w_i$ denote weights...
Bayesian Adaptive Smoothing Spline using Stochastic Differential Equations
Adaptive smoothing Markov chain Monte Carlo Smoothing spline Stochastic dierential equation
2012/11/22
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. How...
Spline Smoothing for Estimation of Circular Probability Distributions via Spectral Isomorphism and its Spatial Adaptation
Non-parametric density estimation circular data Smoothing Spline empirical Fourier coeffcients Fourier Basis Detection of Localisation Edge preserving function estima-tion
2012/11/22
Consider the problem when $X_1,X_2,..., X_n$ are distributed on a circle following an unknown distribution $F$ on $S^1$. In this article we have consider the absolute general set-up where the density ...
Refining Genetically Inferred Relationships Using Treelet Covariance Smoothing
covariance estimation cryptic relatedness genome-wide associ-ation heritability kinship.
2012/9/17
Recent technological advances coupled with large sample sets have un-covered many factors underlying the genetic basis of traits and the predis-position to complex disease, but much is left to discove...
Iterative bias reduction multivariate smoothing in R: The ibr package
multivariate smoothing L2 boosting thin-plate splines kernel regression R
2011/6/20
In multivariate nonparametric analysis, sparseness of the co-
variates also called curse of dimensionality, forces one to use large smoothing
parameters. This leads to a biased smoother. Instead of ...
Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing
Analysis of variance Bayesian inference conditionally autore gressive model hierarchical model smoothing
2010/11/8
Rapid developments in geographical information systems (GIS)continue to generate interest in analyzing complex spatial datasets.One area of activity is in creating smoothed disease maps to de-scribe t...
Particle Learning and Smoothing
Mixture Kalman filter parameter learning particle learning sequential inference smoothing state filtering
2010/11/9
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models.Our approach extends existing particle methods by incorporating th...
Smoothing ℓ₁-penalized estimators for high-dimensional time-course data
Lasso Local least squares Multivariate regression Variable selection Weighted likelihood
2009/9/16
When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use ℓ₁-penalized estimato...
Extensions of smoothing via taut strings
conditional means conditional quantiles modality penalization uniform consistency total variation tube method
2009/9/16
Suppose that we observe independent random pairs $(X_1,Y_1)$, $(X_2,Y_2)$, ..., $(X_n,Y_n)$. Our goal is to estimate regression functions such as the conditional mean or $beta$--quantile of $Y$ given ...