Search Results - subject:"High Dimensional Data"

The Incompleteness Problem of the APT Model

Karlsson, Peter - In: Computational Economics 38 (2011) 2, pp. 129-151

Persistent link: https://www.econbiz.de/10009326729

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu - In: Psychometrika 76 (2011) 4, pp. 584-611

Persistent link: https://www.econbiz.de/10009400064

On the fusion of threshold classifiers for categorization and dimensionality reduction

Kestler, Hans; Lausser, Ludwig; Lindner, Wolfgang; … - In: Computational Statistics 26 (2011) 2, pp. 321-340

Persistent link: https://www.econbiz.de/10009149750

On the advantages of the non-concave penalized likelihood model selection method with minimum prediction errors in large-scale medical studies

Karagrigoriou, A.; Koukouvinos, C.; Mylona, K. - In: Journal of Applied Statistics 37 (2010) 1, pp. 13-24

Variable and model selection problems are fundamental to high-dimensional statistical modeling in diverse fields of sciences. Especially in health studies, many potential factors are usually introduced to determine an outcome variable. This paper deals with the problem of high-dimensional...

Persistent link: https://www.econbiz.de/10008582924

Testing the null of a low dimensional growth model

Jensen, Peter - In: Empirical Economics 38 (2010) 1, pp. 193-215

Persistent link: https://www.econbiz.de/10008596071

Dependence of locally linear embedding on the regularization parameter

Karbauskaitė, Rasa; Dzemyda, Gintautas; … - In: TOP: An Official Journal of the Spanish Society of … 18 (2010) 2, pp. 354-376

Persistent link: https://www.econbiz.de/10008775626

Comparison of PLS algorithms when number of objects is much larger than number of variables

Alin, Aylin - In: Statistical Papers 50 (2009) 4, pp. 711-720

Persistent link: https://www.econbiz.de/10008566223

Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples

Binder, Harald; Schumacher, Martin - In: Statistical Applications in Genetics and Molecular Biology 7 (2008) 1, pp. 12-12

without having to set aside data for validation. This is especially important for high-dimensional data, e.g., arising from … information that can be extracted from high-dimensional data. Potential remedies for this complexity selection bias, such as …

Persistent link: https://www.econbiz.de/10005246500

Finding cancer subtypes in microarray data using random projections

Ghosh, Debashis - Berkeley Electronic Press - 2004

One of the benefits of profiling of cancer samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Such subgroups have typically been found in microarray data using hierarchical clustering. A major problem in interpretation of the output is...

Persistent link: https://www.econbiz.de/10005579274