last call for early bird registration for the two-day course "FLEXIBLE REGRESSION AND SMOOTHING: Using GAMLSS in R", that will be held by one of the creators of the method Professor Mikis Stasinopoulos 23-24 November in Verona, Italy.
Course Fees: Member SISMEC Non-Member SISMEC
Through 23 October 2017
Standard € 220 € 250
Postgraduate/Student € 120 € 150
After 23 October 2017
Standard € 270 € 300
Postgraduate/Student € 170 € 200
To sign up for the course, please follow the instruction on the attached leaflet or contact me for further information:
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About the course:
Flexible Regression and Smoothing. The GAMLSS packages in R
In all fields of science and technology the amount of data collected is growing rapidly. Analyzing different data sets can be very challenging. Two important issues often arise in any statistical modelling technique: i) the choice of an appropriate distribution for the response variable and ii) explaining how this distribution (and its parameters) varies over different values of the explanatory variables. The Generalized Additive Models for Location Scale Shape, (GAMLSS), provides a framework where those two problems can be addressed.
GAMLSS is a regression tool box appropriate for medium to large data sets where the distribution of the response variable is allowed to be a very flexible parametric distribution and where all the parameters of the distribution (not only the mean) can be modelled using linear or smooth functions of the explanatory variables. GAMLSS allows flexibility in univariate statistical modelling far beyond other currently available methods. This short course will be an exposition of the GAMLSS framework using practical examples throughout. In particular the following topics will be covered:
• An introduction to GAMLSS and its statistical modelling philosophy.
• An introduction to the R implementation of GAMLSS.
• A description of the different distributions which can be used for modelling the response variable, and their properties. This includes: i) continuous (positively or negatively skewed and with high or low kurtosis) ii) discrete (over-dispersed or zero inflated) and iii) mixed distributions.
• The different additive terms for modelling the parameters of the distribution will be explored including: linear, non-parametric smoothing and random effects terms.
• A description of different modelling selection techniques and diagnostics for checking the model adequacy.
• Further statistical modelling examples (including centile estimation).
The course is designed for applied statisticians and PhD students in the field of social statistics, biostatistics, medical statistics and other related fields, where the data requires modelling the response variable using a flexible distribution.
For more information about GAMLSS look at http://www.gamlss.org
Looking forward to seeing you in Verona!
Dr Liliya Chamitava
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