A new frequency estimator for a single complex sinusoid in complex white Gaussian noise is proposed. The estimator is more computationally efficient than the optimal maximum likelihood estimator yet attains as good performance at moderately high signal-tonoise ratios. Also, the estimator is shown to be related to the linear prediction estimator. This relationship is exploited to reveal why the ...

For a general H 2 optimal control problem, at rst all H 2 optimal measurement feedback controllers are characterized and parame-terized, and then attention is focused on controllers with estimator based architecture. The H 2 optimal control problem with strictly proper controllers and the H 2 optimal control problem with proper controllers are essentially diierent and hence clearly delineated. ...

There are various types of classifiers that can be trained on gene expression data with class labels. Many of them have an embedded mechanism for feature selection, by which they distinguish a subset of significant genes that are used for future prediction. When dealing with more than two class labels, especially when the number goes up to a dozen or more, people find it useful to know the rela...

Spatial covariance matrix estimation is of great significance in many applications in climatology, econometrics and many other fields with complex data structures involving spatial dependencies. High dimensionality brings new challenges to this problem, and no theoretical optimal estimator has been proved for the spatial high-dimensional covariance matrix. Over the past decade, the method of re...

Abstract. In a pioneer work, Révész (1973) introduces the stochastic approximation method to build up a recursive kernel estimator of the regression function x 7→ E(Y |X = x). However, according to Révész (1977), his estimator has two main drawbacks: on the one hand, its convergence rate is smaller than that of the nonrecursive Nadaraya-Watson’s kernel regression estimator, and, on the other ha...

The statistical analysis of spatial data is usually done under Gaussian assumption for the underlying random field model. When this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. One of the crucial problems in this setting is specifying the block sizes. In this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...

We show that the model-calibration estimator for the finite population mean, which was proposed by Wu & Sitter (2001) through an intuitive argument, is optimal among a class of calibration estimators. We also present optimal calibration estimators for the finite population distribution function, the population variance, the variance of a linear estimator and other quadratic finite population fu...