Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Semiparametric M-quantile regression for count data
by
Ranalli, M Giovanna
, Dreassi, Emanuela
, Salvati, Nicola
in
Additives
/ Cancer
/ England - epidemiology
/ Humans
/ Incidence
/ Lung cancer
/ Lung Neoplasms - epidemiology
/ Regression Analysis
/ Specification
2014
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Semiparametric M-quantile regression for count data
by
Ranalli, M Giovanna
, Dreassi, Emanuela
, Salvati, Nicola
in
Additives
/ Cancer
/ England - epidemiology
/ Humans
/ Incidence
/ Lung cancer
/ Lung Neoplasms - epidemiology
/ Regression Analysis
/ Specification
2014
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Semiparametric M-quantile regression for count data
2014
Request Book From Autostore
and Choose the Collection Method
Overview
Lung cancer incidence over 2005–2010 for 326 Local Authority Districts in England is investigated by ecological regression. Motivated from mis-specification of a Negative Binomial additive model, a semiparametric Negative Binomial M-quantile regression model is introduced. The additive part relates to those univariate or bivariate smoothing components, which are included in the model to capture nonlinearities in the predictor or to account for spatial dependence. All such components are estimated using penalized splines. The results show the capability of the semiparametric Negative Binomial M-quantile regression model to handle data with a strong spatial structure.
Publisher
SAGE Publications,Sage Publications Ltd
Subject
This website uses cookies to ensure you get the best experience on our website.