We consider nonparametric estimation of marginal density functions of linear processes by using kernel density estimators. We assume that the innovation processes are i.i.d. and have infinite-variance. We present the asymptotic distributions of the kernel density estimators with the order of bandwidths fixed as h = cn−1/5, where n is the sample size. The asymptotic distributions depend on both ...

The paper is concerned with stochastic approximation procedures having three main characteristics: truncations with random moving bounds, a matrix valued random step-size sequence, and a dynamically changing random regression function. We study convergence and rate of convergence. Main results are supplemented with corollaries to establish various sets of sufficient conditions, with the main em...

This paper unifies “line-process” approaches for regularization with discontinuities and robust estimation techniques. We generalize the notion of a “line process” to that of an analog “outlier process” and show that a problem formulated in terms of outlier processes can be viewed an terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent ...

A method is given to estimate the geometry and motion of a moving body surface from image sequences. To this aim a parametric model of the surface is used, in order to reformulate the problem to one of parameter estimation. After linearization of the model standard linear estimation methods can be used to estimate the parameters. The main contribution of this paper is that a method is provided ...

We consider the data stream model where an n-dimensional vector x is updated coordinate-wise by a stream of updates. The frequency estimation problem is to process the stream in a single pass and using small memory such that an estimate for xi for any i can be retrieved. We present the first algorithms for `2-based frequency estimation that exhibit a tradeoff between the precision (additive err...

In the image parameter estimation by the linear regression, it has very high degrees of freedom for the decision of regression coefficients, because the dimension of image vector is huge high. In this paper, we discuss its potential by the learning of the dense samples. For the learning process, we employed a sequential regression coefficient calculation algorithm and realize its calculation fo...

We propose an approach to 3D facial pose estimation that, unlike most state-of-the-art techniques, can handle arbitrary poses, including extreme out-of-plane rotations, background clutter and facial expressions. It relies on a large database of registered face images of different people viewed from several perspectives. We use a powerful image retrieval technique to match the input image agains...

This paper investigates the validity of LR estimation for long-term F0 using Aitkin (1995)’s formula. Although this formula has been developed to estimate the LR of reflective index of glass fragments, previous studies such as Kinoshita (2001) and Rose, Osanai, and Kinoshita (2003) have shown that Aitkin’s formula can be applied to speech data. The experiments in this study revealed, however, t...

This project aims to estimate the pose of an object in the image. Pose estimation problem is known to be an open problem and also a crucial problem in computer vision field. Many real-world tasks depend heavily on or can be improved by a good pose estimation. For example, by knowing the exact pose of an object, robots will know where to sit on, how to grasp, or avoid collision when walking arou...

In a number of practical scenarios, such as video conferencing and visual human/computer interaction, objects that belong to a well defined class are segmented, normalized, and encoded, after which they are stored and/or transmitted, and subsequently reconstructed. The Karhunen-Loève Transform (KLT) optimally concentrates the signal power in a relatively small number of uncorrelated coefficient...