8,062 research outputs found
Real estate portfolio construction and estimation risk
The use of MPT in the construction real estate portfolios has two serious limitations when used in an ex-ante framework: (1) the intertemporal instability of the portfolio weights and (2) the sharp deterioration in performance of the optimal portfolios outside the sample period used to estimate asset mean returns. Both problems can be traced to wide fluctuations in sample means Jorion (1985). Thus the use of a procedure that ignores the estimation risk due to the uncertain in mean returns is likely to produce sub-optimal results in subsequent periods. This suggests that the consideration of the issue of estimation risk is crucial in the use of MPT in developing a successful real estate portfolio strategy. Therefore, following Eun & Resnick (1988), this study extends previous ex-ante based studies by evaluating optimal portfolio allocations in subsequent test periods by using methods that have been proposed to reduce the effect of measurement error on optimal portfolio allocations
An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommendation approach by combining a Naive Bayes classification approach with the collaborative filtering. Experimental results on two different data sets, show that the proposed algorithm is scalable and provide better performance – in terms of accuracy and coverage – than other algorithms while at the same time eliminates some recorded problems with the recommender systems
Effect Size Estimation and Misclassification Rate Based Variable Selection in Linear Discriminant Analysis
Supervised classifying of biological samples based on genetic information,
(e.g. gene expression profiles) is an important problem in biostatistics. In
order to find both accurate and interpretable classification rules variable
selection is indispensable. This article explores how an assessment of the
individual importance of variables (effect size estimation) can be used to
perform variable selection. I review recent effect size estimation approaches
in the context of linear discriminant analysis (LDA) and propose a new
conceptually simple effect size estimation method which is at the same time
computationally efficient. I then show how to use effect sizes to perform
variable selection based on the misclassification rate which is the data
independent expectation of the prediction error. Simulation studies and real
data analyses illustrate that the proposed effect size estimation and variable
selection methods are competitive. Particularly, they lead to both compact and
interpretable feature sets.Comment: 21 pages, 2 figure
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Carbon portfolio management
The aim of the European Union's Emissions Trading Scheme (EU ETS) is that by 2020, emissions from sectors covered by the EU ETS will be 21% lower than in 2005. In addition to large CO 2 emitting companies covered by the scheme, other participants have entered the market with a view of using emission allowances for the diversification of their investment portfolios. The performance of this asset as a stand alone investment and its portfolio diversification implications will be investigated in this paper. Our results indicate that the market views Phases 1, 2, and 3 European Union allowance futures as unattractive as stand alone investments. In a portfolio context, in Phase 1, once the short-selling option is added, there are considerable portfolio benefits. However, our results indicate that these benefits only existed briefly during the pilot stage of the EU ETS. There is no evidence to suggest portfolio diversification benefits exist for Phase 2 or the early stages of Phase 3
Gene ranking and biomarker discovery under correlation
Biomarker discovery and gene ranking is a standard task in genomic high
throughput analysis. Typically, the ordering of markers is based on a
stabilized variant of the t-score, such as the moderated t or the SAM
statistic. However, these procedures ignore gene-gene correlations, which may
have a profound impact on the gene orderings and on the power of the subsequent
tests.
We propose a simple procedure that adjusts gene-wise t-statistics to take
account of correlations among genes. The resulting correlation-adjusted
t-scores ("cat" scores) are derived from a predictive perspective, i.e. as a
score for variable selection to discriminate group membership in two-class
linear discriminant analysis. In the absence of correlation the cat score
reduces to the standard t-score. Moreover, using the cat score it is
straightforward to evaluate groups of features (i.e. gene sets). For
computation of the cat score from small sample data we propose a shrinkage
procedure. In a comparative study comprising six different synthetic and
empirical correlation structures we show that the cat score improves estimation
of gene orderings and leads to higher power for fixed true discovery rate, and
vice versa. Finally, we also illustrate the cat score by analyzing metabolomic
data.
The shrinkage cat score is implemented in the R package "st" available from
URL http://cran.r-project.org/web/packages/st/Comment: 18 pages, 5 figures, 1 tabl
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