In this talk, we introduce several algorithms using singular spectrum analysis for gap filling in univariate time series. In these algorithms, the data before missing values and the data after (in the reverse order) missing values treated as two separate time series. Then using SSA forecasting, two estimations of the missing values will be provided. Finally, we combine these estimations and produce a unique estimation for missing values.

A talk was given by the Head of the SDAT Education Department at the of the Statistics and its Aplications, Lisbon, Portugal under the title of "Bonus-Malus Systems: A Discussion of Some of the Problems". 

A Bonus-Malus System (BMS) is one of the ratemaking methods generally used in automobile insurance. BMS penalizes policyholders at faults for one or more claims by a premium surcharge, known as malus, and rewards the policyholders with a claim-free year by granting discount in the premium say bonus. An optimal BMS should be possibly fair for policyholders and financially balanced for the insurer. While much progress has been made on the development of BMS but for practical applications, many questions remain, including the reliability and efficiency of the systems. To deal with system efficiency ‘rigorously’ an open portfolio assumption must be considered. The approximation techniques proposed for this purpose are largely at a conceptual stage. In this talk, we categorize the existing BMS and then discuss the challenges in both theory and practice.

R: Data Management Calculations and Statistical Analysis Book has been published by Dr. Javad Fardmal, associate professor of biostatistics at Hamedan university of Biomedical Sciences and Dr. Rahim Mahmoudvand, assistant professor of statisitcs at Hamedan University. This book is in Persian and contains various topics in scientific data analysis with R. The table of content is available form here and you can buy the book from here.