Local linear regression for functional data
نویسندگان
چکیده
منابع مشابه
Functional Linear Regression Analysis for Longitudinal Data
We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Pre-dictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects. In longitudina...
متن کاملLocal linear regression for generalized linear models with missing data
Fan, Heckman and Wand (1995) proposed locally weighted kernel polynomial regression methods for generalized linear models and quasilikelihood functions. When the covariate variables are missing at random, we propose a weighted estimator based on the inverse selection probability weights. Distribution theory is derived when the selection probabilities are estimated nonparametrically. We show tha...
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 کاملDistance-based local linear regression for functional predictors
The problem of nonparametrically predicting a scalar response variable from a functional predictor is considered. A sample of pairs (functional predictor and response) is observed. When predicting the response for a new functional predictor value, a semi-metric is used to compute the distances between the new and the previously observed functional predictors. Then each pair in the original samp...
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: Annals of the Institute of Statistical Mathematics
سال: 2010
ISSN: 0020-3157,1572-9052
DOI: 10.1007/s10463-010-0275-8