NDLI: Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm

Content Provider	Springer Nature Link
Author	Rodríguez Álvarez, María Xosé Lee, Dae Jin Kneib, Thomas Durbán, María Eilers, Paul
Copyright Year	2014
Abstract	A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized linear model with anisotropic penalty is presented. This new proposal is based on the mixed model representation of a multidimensional P-spline, in which the smoothing parameter for each covariate is expressed in terms of variance components. On the basis of penalized quasi-likelihood methods, closed-form expressions for the estimates of the variance components are obtained. This formulation leads to an efficient implementation that considerably reduces the computational burden. The proposed algorithm can be seen as a generalization of the algorithm by Schall (1991)—for variance components estimation—to deal with non-standard structures of the covariance matrix of the random effects. The practical performance of the proposed algorithm is evaluated by means of simulations, and comparisons with alternative methods are made on the basis of the mean square error criterion and the computing time. Finally, we illustrate our proposal with the analysis of two real datasets: a two dimensional example of historical records of monthly precipitation data in USA and a three dimensional one of mortality data from respiratory disease according to the age at death, the year of death and the month of death.
Starting Page	941
Ending Page	957
Page Count	17
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	25
Issue Number	5
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2014-04-18
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Smoothing P-splines Tensor product Anisotropic penalty Mixed models Statistics and Computing/Statistics Programs Artificial Intelligence (incl. Robotics) Statistical Theory and Methods Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data

P-splines regression smoothing and difference type of penalty

Anisotropic multidimensional smoothing using Bayesian tensor product P-splines

P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data

Shape constrained additive models

Straightforward intermediate rank tensor product smoothing in mixed models

Estimating mixed-effects differential equation models

Smoothing noisy data for irregular regions using penalized bivariate splines on triangulations

Random effects selection in generalized linear mixed models via shrinkage penalty function

Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm

Similar Documents

P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data

P-splines regression smoothing and difference type of penalty

Anisotropic multidimensional smoothing using Bayesian tensor product P-splines

P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data

Shape constrained additive models

Straightforward intermediate rank tensor product smoothing in mixed models

Estimating mixed-effects differential equation models

Smoothing noisy data for irregular regions using penalized bivariate splines on triangulations

Random effects selection in generalized linear mixed models via shrinkage penalty function

Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm