Abstract. The estimation of a density and its derivatives from a finite mixture under the pairwise positive quadrant dependence assumption is considered. A new wavelet based linear estimator is constructed. We evaluate its asymptotic performance by determining an upper bound of the mean integrated squared error. We prove that it attains a sharp rate of convergence for a wide class of unknown de...

This article concerns the canonical empirical Bayes problem of estimating normal means under squared-error loss. General empirical estimators are derived which are asymptotically minimax and optimal. Uniform convergence and the speed of convergence are considered. The general empirical Bayes estimators are compared with the shrinkage estimators of Stein (1956) and James and Stein (1961). Estima...

Given any Banach space X, let L X 2 denote the Banach space of all measurable functions f : [0, 1] → X for which f 2 := 1 0 f (t) 2 dt

We investigate weighted L p mean convergence of Grünwald interpolation operators based on the zeros of orthogonal polynomials with respect to a general weight and generalized Jacobi weights. We give necessary and sufficient conditions for such convergence for all continuous functions. 1. Introduction. In this paper, we study weighted L p (0 < p < ∞) mean con

The research of its convergence of Mean Shift algorithm is the foundation of its application. Comaniciu and Li Xiang-ru have respectively provided the proof for the convergence of Mean Shift but they both made a mistake in their proofs. In this paper, the imprecise proofs existing in some literatures are firstly pointed out. Then, the local convergence is proved in a new way and the condition o...

Epanechnikov Mean Shift is a simple yet empirically very effective algorithm for clustering. It localizes the centroids of data clusters via estimating modes of the probability distribution that generates the data points, using the ‘optimal’ Epanechnikov kernel density estimator. However, since the procedure involves non-smooth kernel density functions, the convergence behavior of Epanechnikov ...

Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it c...