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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...
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...
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...
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 ...
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,...
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 ...
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...
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...
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 ...
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...
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 ...
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 (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...
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...
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 ...

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