On local linear regression for strongly mixing random fields
نویسندگان
چکیده
منابع مشابه
Random Projection-Based Anderson-Darling Test for Random Fields
In this paper, we present the Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) goodness of fit statistics for stationary and non-stationary random fields. Namely, we adopt an easy-to-apply method based on a random projection of a Hilbert-valued random field onto the real line R, and then, applying the well-known AD and KS goodness of fit tests. We conclude this paper by studying the behavior o...
متن کاملKernel Inverse Regression for Spatial Random Fields
In this paper, we propose a dimension reduction model for spatially dependent variables. Namely, we investigate an extension of the inverse regression method under strong mixing condition. This method is based on estimation of the matrix of covariance of the expectation of the explanatory given the dependent variable, called the inverse regression. Then, we study, under strong mixing condition,...
متن کاملLocal Linear Spatial Regression
A local linear kernel estimator of the regression function x 7→ g(x) := E[Yi|Xi = x], x ∈ R , of a stationary (d+1)-dimensional spatial process {(Yi,Xi), i ∈ Z } observed over a rectangular domain of the form In := {i = (i1, . . . , iN ) ∈ Z N |1 ≤ ik ≤ nk, k = 1, . . . ,N}, n = (n1, . . . , nN ) ∈ Z N , is proposed and investigated. Under mild regularity assumptions, asymptotic normality of th...
متن کاملLocal Whittle estimator for anisotropic random fields
AMS 1991 subject classifications: primary 62G07 secondary 62M10 Keywords: Spatial long memory Local Whittle method a b s t r a c t A local Whittle estimator is developed to simultaneously estimate the long memory parameters for stationary anisotropic scalar random fields. It is shown that these estimators are consistent and asymptotically normal, under some weak technical conditions. A brief si...
متن کاملLocal Linear Functional Regression based on Weighted Distance-Based Regression
We consider the problem of nonparametrically predicting a scalar response variable y from a functional predictor χ. We have n observations (χi, yi) and we assign a weight wi ∝ K (d(χ, χi)/h) to each χi, where d( · , · ) is a semi-metric, K is a kernel function and h is the bandwidth. Then we fit a Weighted (Linear) Distance-Based Regression, where the weights are as above and the distances are ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2017
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2017.02.002