20,512 research outputs found
Model Selection for High Dimensional Quadratic Regression via Regularization
Quadratic regression (QR) models naturally extend linear models by
considering interaction effects between the covariates. To conduct model
selection in QR, it is important to maintain the hierarchical model structure
between main effects and interaction effects. Existing regularization methods
generally achieve this goal by solving complex optimization problems, which
usually demands high computational cost and hence are not feasible for high
dimensional data. This paper focuses on scalable regularization methods for
model selection in high dimensional QR. We first consider two-stage
regularization methods and establish theoretical properties of the two-stage
LASSO. Then, a new regularization method, called Regularization Algorithm under
Marginality Principle (RAMP), is proposed to compute a hierarchy-preserving
regularization solution path efficiently. Both methods are further extended to
solve generalized QR models. Numerical results are also shown to demonstrate
performance of the methods.Comment: 37 pages, 1 figure with supplementary materia
Pulsar interpretation of the lepton spectra measured by AMS-02
AMS-02 recently published its lepton spectra measurement. The results show
that the positron fraction no longer increases above 200 GeV. The aim of
this work is to investigate the possibility that the excess of positron
fraction is due to pulsars. Nearby known pulsars from ATNF catalogue are
considered as a possible primary positron source of the high energy positrons.
We find that the pulsars with age yr and
distance kpc can explain the behavior of positron fraction of AMS-02 in
the range of high energy. We show that each of the four pulsars --- Geminga,
J1741-2054, Monogem and J0942-5552 --- is able to be a single source satisfying
all considered physical requirements. We also discuss the possibility that
these high energy are from multiple pulsars. The multiple pulsars
contribution predicts a positron fraction with some structures at higher
energies.Comment: 27 pages, 5 figures, 3 tables, accepted for publication in EPJ
School Quality and Housing Prices: Empirical Evidence Based on a Natural Experiment in Shanghai, China
The extent to which the quantity and quality of education is capitalized into housing prices is a key issue in understanding the relationship between allocation of educational resources and the housing market. Using monthly panel data of 52 residential areas in Shanghai and employing a natural experiment of designating Shanghai Experimental Model Senior High Schools (EMSHS), we find that housing prices in Shanghai have capitalized the access to quality schools and other public goods. One quality school per square kilometer raises average housing prices by approximately 19%, and one best EMSHS per square kilometer increases housing prices by 21%. We also match the schools designated for EMSHS with schools of similar quality but not designated for EMSHS, and compare housing prices in the corresponding areas. We find that the designation increased the housing prices, showing that dissemination of information about school quality was significantly affected by the designation.education, housing market, capitalization, public goods, natural experiment
Long Short-Term Memory Spatial Transformer Network
Spatial transformer network has been used in a layered form in conjunction
with a convolutional network to enable the model to transform data spatially.
In this paper, we propose a combined spatial transformer network (STN) and a
Long Short-Term Memory network (LSTM) to classify digits in sequences formed by
MINST elements. This LSTM-STN model has a top-down attention mechanism profit
from LSTM layer, so that the STN layer can perform short-term independent
elements for the statement in the process of spatial transformation, thus
avoiding the distortion that may be caused when the entire sequence is
spatially transformed. It also avoids the influence of this distortion on the
subsequent classification process using convolutional neural networks and
achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional
Neural Network with STN layer
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