14 research outputs found
Hierarchical adaptive polynomial chaos expansions
Polynomial chaos expansions (PCE) are widely used in the framework of
uncertainty quantification. However, when dealing with high dimensional complex
problems, challenging issues need to be faced. For instance, high-order
polynomials may be required, which leads to a large polynomial basis whereas
usually only a few of the basis functions are in fact significant. Taking into
account the sparse structure of the model, advanced techniques such as sparse
PCE (SPCE), have been recently proposed to alleviate the computational issue.
In this paper, we propose a novel approach to SPCE, which allows one to exploit
the model's hierarchical structure. The proposed approach is based on the
adaptive enrichment of the polynomial basis using the so-called principle of
heredity. As a result, one can reduce the computational burden related to a
large pre-defined candidate set while obtaining higher accuracy with the same
computational budget
Structured variable selection and estimation
In linear regression problems with related predictors, it is desirable to do
variable selection and estimation by maintaining the hierarchical or structural
relationships among predictors. In this paper we propose non-negative garrote
methods that can naturally incorporate such relationships defined through
effect heredity principles or marginality principles. We show that the methods
are very easy to compute and enjoy nice theoretical properties. We also show
that the methods can be easily extended to deal with more general regression
problems such as generalized linear models. Simulations and real examples are
used to illustrate the merits of the proposed methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS254 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The moderating effects of governance on the relationship between investment opportunites, leverage and ownership indentity with firm performance in the UAE
This study evaluates corporate governance practices of listed firms in the UAE and
examines the hypothesized influence of investment opportunities, leverage, foreign and
institutional ownership on firm performance. Corporate governance strength is also investigated as a moderator between investment opportunities, leverage, foreign, institutional ownership and firm performance. The moderating impact of corporate governance strength is also examined during the global financial crisis. After constructing an index to measure corporate governance strength, the fixed effects
regression in panel data was used to analyze the data. The data included 101 firms with
a total of 501 firm-year observations that spanned the period 2008 to 2012, of all the
firms listed on the Abu Dhabi Stock Exchange and the Dubai Financial Market. The results show a significant influence of investment opportunities, leverage and institutional ownership on firm performance represented by Return on Assets (ROA) and Refined Economic Value Added (REVA). However, the results find no influence of foreign ownership on ROA, and a negative influence on REVA. The governance index
shows a dramatic improvement in the corporate governance practices over time. In addition, corporate governance strength is found to significantly moderate the relationship between investment opportunities, leverage, foreign and institutional ownership with ROA, but only moderates the relationship between leverage and REVA. During the crisis, corporate governance strength appears to play a more efficient
moderating role. The findings of this study provide some insights to the regulators and
other related parties about the status of corporate governance practices in the UAE and
show that good corporate governance is indirectly able to improve the performance of
firms during different time periods
Recent Developments in Nonregular Fractional Factorial Designs
Nonregular fractional factorial designs such as Plackett-Burman designs and
other orthogonal arrays are widely used in various screening experiments for
their run size economy and flexibility. The traditional analysis focuses on
main effects only. Hamada and Wu (1992) went beyond the traditional approach
and proposed an analysis strategy to demonstrate that some interactions could
be entertained and estimated beyond a few significant main effects. Their
groundbreaking work stimulated much of the recent developments in design
criterion creation, construction and analysis of nonregular designs. This paper
reviews important developments in optimality criteria and comparison, including
projection properties, generalized resolution, various generalized minimum
aberration criteria, optimality results, construction methods and analysis
strategies for nonregular designs.Comment: Submitted to the Statistics Surveys (http://www.i-journals.org/ss/)
by the Institute of Mathematical Statistics (http://www.imstat.org
A lasso for hierarchical interactions
We add a set of convex constraints to the lasso to produce sparse interaction
models that honor the hierarchy restriction that an interaction only be
included in a model if one or both variables are marginally important. We give
a precise characterization of the effect of this hierarchy constraint, prove
that hierarchy holds with probability one and derive an unbiased estimate for
the degrees of freedom of our estimator. A bound on this estimate reveals the
amount of fitting "saved" by the hierarchy constraint. We distinguish between
parameter sparsity - the number of nonzero coefficients - and practical
sparsity - the number of raw variables one must measure to make a new
prediction. Hierarchy focuses on the latter, which is more closely tied to
important data collection concerns such as cost, time and effort. We develop an
algorithm, available in the R package hierNet, and perform an empirical study
of our method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1096 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Least angle and penalized regression: A review
Least Angle Regression is a promising technique for variable selection
applications, offering a nice alternative to stepwise regression. It provides
an explanation for the similar behavior of LASSO (-penalized
regression) and forward stagewise regression, and provides a fast
implementation of both. The idea has caught on rapidly, and sparked a great
deal of research interest. In this paper, we give an overview of Least Angle
Regression and the current state of related research.Comment: Published in at http://dx.doi.org/10.1214/08-SS035 the Statistics
Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical
Statistics (http://www.imstat.org