There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age gr...

We consider the least-square regression problem with regularization by a block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1-norm where all spaces have dimension one, where it is commonly referred to as the Lasso. In this paper, we study the asymptotic model consistency of...

In this dissertation we studied asymptotic properties of shrinkage estimators, and compared their performance with absolute penalty estimators (APE) in linear and partially linear models (PLM). A robust shrinkage M-estimator is proposed for PLM, and asymptotic properties are investigated, both analytically and through simulation studies. In Chapter 2, we compared the performance of shrinkage an...

Penalized estimation has become an established tool for regularization and model selection in regression models. A variety of penalties with specific features are available and effective algorithms for specific penalties have been proposed. But not much is available to fit models with a combination of different penalties. When modeling the rent data of Munich as in our application, various type...

The LASSO (Tibshirani, J R Stat Soc Ser B 58(1):267–288, 1996, [30]) and the adaptive LASSO (Zou, J Am Stat Assoc 101:1418–1429, 2006, [37]) are popular in regression analysis for their advantage of simultaneous variable selection and parameter estimation, and also have been applied to autoregressive time series models. We propose the doubly adaptive LASSO (daLASSO), or PLAC-weighted adaptive L...

in this work the electrooxidation half-wave potentials of some benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (qsar) approaches. the dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by dc-polarography. descriptors which were selected by stepwise multiple selection procedure ar...