Convergence rates of kernel density estimators for stationary time series are well studied. For invertible linear processes, we construct a new density estimator that converges, in the supremum norm, at the better, parametric, rate n. Our estimator is a convolution of two different residual-based kernel estimators. We obtain in particular convergence rates for such residual-based kernel estimat...

Multivariate kernel density estimation provides information about structure in data. Feature significance is a technique for deciding whether features – such as local extrema – are statistically significant. This paper proposes a framework for feature significance in d-dimensional data which combines kernel density derivative estimators and hypothesis tests for modal regions. For the gradient a...

Standard fixed symmetric kernel type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. We show that in such settings, alternatives of asymmetric gamma kernel estimators are superior but also differ in asymptotic and finite sample performance conditional on the shape of the density near zero and the exact form of the cho...

We consider kernel density and regression estimation for a wide class of nonlinear time series models. Asymptotic normality and uniform rates of convergence of kernel estimators are established under mild regularity conditions. Our theory is developed under the new framework of predictive dependence measures which are directly based on the data-generating mechanisms of the underlying processes....

Based on a random sample of size n from an unknown d-dimensional density f , the problem of selecting the variable (or adaptive) bandwidth in kernel estimation of f is investigated. The common strategy is to express the variable bandwidth at each observation as the product of a local bandwidth factor and a global smoothing parameter. For selecting the local bandwidth factor a method based on cl...

Convergence rates and central limit theorems for kernel estimators of the stationary density of a linear process have been obtained under the assumption that the innovation density is smooth (Lipschitz). We show that smoothness is not required. For example, it suffices that the innovation density has bounded variation.

Temperature is a quantity that indicates the degree of heat an object. The higher temperature object, hotter Thermal imagery image created with thermal infrared spectrum utilizes Sensing on this based differences in object and its emission power which reflected by difference hue or color. sensor used images noncamera scanning. In imagery, there problem, namely blurred / make it difficult to obt...

This paper introduces a blind method based on Bayesian maximum a posteriori estimation theory for restoring images corrupted by noise and blurred by one or more unknown point spread functions. Image and blur prior information is expressed in the form of parametric generalized Gauss Markov random field models. A method for estimating the GGMRF neighborhood influence parameters is presented, alon...

This paper describes an interpolation method that takes into account the edge orientation in order to avoid typical interpolation artifacts (jagging, staircase effects...). It is first based on an edge orientation estimation, performed in the wavelet domain. The estimation uses the multi-resolution features of wavelets to give an accurate and non-biased description of the frequency characterist...