Interpolation And Regression Models For The Chemical Engineer

In survival analysis there has long been a need for models that goes beyond the Cox model as the proportional hazards assumption often fails in practice. This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the specific aim of describing time-varying effects of explanatory variables.

A thorough review of the most current regression methods in time series analysis
Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.

In survival analysis there has long been a need for models that goes beyond the Cox model as the proportional hazards assumption often fails in practice.

In every market with free floating prices, all market participants are interested in the future developments of these prices. However, there is an evident research gap for forecasting models for the German office market. Alexander Bönner closes this gap by focusing on an empirical investigation of several rent and total yield forecasting models for nine major German cities. The applicability and performance of ARIMA, GARCH and multivariate regression models are analyzed and city as well as forecasting horizon-specific patterns are determined and interpreted. Uni-variate rent forecasting models generally outperform multivariate rent forecasting regression models in a short run. In the long run, multivariate regression models dominate. However, one must bear in mind that in some cities one model permanently outperforms the other. Eventually, the rent level is mainly determined by its economic fundamentals, which is also demonstrated for the total yield examination.