Long Term Earnings Inequality, Earnings Instability and Temporary Employment in Spain: 1993–2000 This paper provides a longitudinal perspective on changes in Spanish male earnings inequality for the period 1993-2000, by decomposing the earnings covariance structure into its permanent and transitory parts. According to the Spanish sample of the European Community Household Panel, cross-sectional...

Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny published results. In lung nodule classification, for example, many works report results on LIDC dataset. theory, this should allow a direct comparison proposed methods assess impact individual contributions. When analyzing seven recent wo...

the objective of this study was to classify the shoosh aquifer to several zones with different water quality in khuzestan province, iran. in this regard, the performance of classification methods (discriminant function and cluster analysis) for the classification of groundwater based on the level of pollution with an emphasis on the problem of over-fitting in training data were considered. an o...

Post-Secondary Education in Canada: Can Ability Bias Explain the Earnings Gap Between College and University Graduates? Using the Canadian General Social Survey we compute returns to post-secondary education relative to high-school. Unlike previous research using Canadian data, our dataset allows us to control for ability selection into higher education. We find strong evidence of positive abil...

Sampling of large datasets for data mining is important for at least two reasons. The processing of large amounts of data results in increased computational complexity. The cost of this additional complexity may not be justifiable. On the other hand, the use of small samples results in fast and efficient computation for data mining algorithms. Statistical methods for obtaining sufficient sample...

microarray data have an important role in identification and classification of the cancer tissues. having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. for this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microa...

Feature selection becomes a central task when ’signature’ profiles specific to a pathological status have to be extracted from high dimensional gene expression or proteomic data. In the present paper, we propose a feature selection method based on Singular Value Decomposition (SVD) and apply it to SELDI-TOF/MS proteomic data from a cohort of Type 2 Diabetics affected by Glomerulosclerosis and M...