Premium
Penalized likelihood regression: General formulation and efficient approximation
Author(s) -
Gu Chong,
Kim YoungJu
Publication year - 2002
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316100
Subject(s) - mathematics , regression , cover (algebra) , simple (philosophy) , exponential function , convergence (economics) , exponential family , scale (ratio) , rate of convergence , regression analysis , computer science , statistics , mathematical optimization , mathematical analysis , key (lock) , mechanical engineering , philosophy , physics , epistemology , quantum mechanics , engineering , economics , economic growth , computer security
The authors consider a formulation of penalized likelihood regression that is sufficiently general to cover canonical and noncanonical links for exponential families as well as accelerated life models with censored survival data. They present an asymptotic analysis of convergence rates to justify a simple approach to the lower‐dimensional approximation of the estimates. Such an approximation allows for much faster numerical calculation, paving the way to the development of algorithms that scale well with large data sets.