Estimation in Functional Regression for General Exponential Families

Details Estimation in Functional Regression for General Exponential Families Estimation in Functional Regression for General Exponential Families

2011-08-17

arxiv.org/abs/1108.3552v1

Mathematics

Statistics Theory

arXiv admin note: significant text overlap with arXiv:1001.3742

Scientific paper

This paper studies a class of exponential family models whose canonical parameters are specified as linear functionals of an unknown infinite-dimensional slope function. The optimal minimax rates of convergence for slope function estimation are established. The estimators that achieve the optimal rates are constructed by constrained maximum likelihood estimation with parameters whose dimension grows with sample size. A change-of-measure argument, inspired by Le Cam's theory of asymptotic equivalence, is used to eliminate the bias caused by the non-linearity of exponential family models.

Affiliated with

Also associated with

No associations

Rating

Estimation in Functional Regression for General Exponential Families does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Estimation in Functional Regression for General Exponential Families, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimation in Functional Regression for General Exponential Families will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-541810