54,547 research outputs found
Estimation and Regularization Techniques for Regression Models with Multidimensional Prediction Functions
Boosting is one of the most important methods for fitting
regression models and building prediction rules from
high-dimensional data. A notable feature of boosting is that the
technique has a built-in mechanism for shrinking coefficient
estimates and variable selection. This regularization mechanism
makes boosting a suitable method for analyzing data characterized by
small sample sizes and large numbers of predictors. We extend the
existing methodology by developing a boosting method for prediction
functions with multiple components. Such multidimensional functions
occur in many types of statistical models, for example in count data
models and in models involving outcome variables with a mixture
distribution. As will be demonstrated, the new algorithm is suitable
for both the estimation of the prediction function and
regularization of the estimates. In addition, nuisance parameters
can be estimated simultaneously with the prediction function
Attribute oriented induction with star schema
This paper will propose a novel star schema attribute induction as a new
attribute induction paradigm and as improving from current attribute oriented
induction. A novel star schema attribute induction will be examined with
current attribute oriented induction based on characteristic rule and using non
rule based concept hierarchy by implementing both of approaches. In novel star
schema attribute induction some improvements have been implemented like
elimination threshold number as maximum tuples control for generalization
result, there is no ANY as the most general concept, replacement the role
concept hierarchy with concept tree, simplification for the generalization
strategy steps and elimination attribute oriented induction algorithm. Novel
star schema attribute induction is more powerful than the current attribute
oriented induction since can produce small number final generalization tuples
and there is no ANY in the results.Comment: 23 Pages, IJDM
New directions in the analysis of inequality and poverty
Over the last four decades, academic and wider public interest in inequality and poverty has grown substantially. In this paper we address the question: what have been the major new directions in the analysis of inequality and poverty over the last thirty to forty years? We draw attention to developments under seven headings: changes in the extent of inequality and poverty, changes in the policy environment, increased scrutiny of the concepts of ‘poverty’ and inequality’ and the rise of multidimensional approaches, the use of longitudinal perspectives, an increase in availability of and access to data, developments in analytical methods of measurement, and developments in modelling
The Frost Multidimensional Perfectionism Scale revisited: More perfect with four (instead of six) dimensions
The Frost Multidimensional Perfectionism Scale (FMPS; Frost, Marten, Lahart & Rosenblate, 1990) provides six subscales for a multidimensional assessment of perfectionism: Concern over Mistakes (CM), Personal Standards (PS), Parental Expectations (PE), Parental Criticism (PC), Doubts about actions (D), and Organization (O). Despite its increasing popularity in personality and clinical research, the FMPS has also drawn some criticism for its factorial instability across samples. The present article argues that this instability may be due to an overextraction of components. Whereas all previous analyses presented six-factor solutions for the FMPS items, a reanalysis with Horn's parallel analysis suggested only four or five underlying factors. To investigate the nature of these factors, item responses from N = 243 participants were subjected to principal component analysis. Again, parallel analysis retained only four components. Varimax rotation replicated PS and O as separate factors, whereas combining CM with D as well as PE with PC. Consequently, the present article suggests a reduction to four (instead of six) FMPS subscales. Differential correlations with anxiety, depression, parental representations and action tendencies underscore the advantage of this solution
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