Sensitive micro data protection using Latin hypercube sampling technique

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2316, Issue , 2002, Pages 117-125

  Dandekar, Ramesh A ^a   Cohen, Michael ^b   Kirkendall, Nancy ^a  

^a Energy Information Administration   (United States)

^b NATIONAL ACADEMY OF SCIENCES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS;

ENERGY INFORMATION ADMINISTRATION; ESSENTIAL FEATURES; LATIN HYPERCUBE SAMPLING; MULTIPLICATIVE NOISE; PEARSON CORRELATION; RANK CORRELATION; SYNTHETIC DATA; SYNTHETIC DATASETS;

GEOMETRY;

EID: 84949463843     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47804-3_9     Document Type: Conference Paper

References (4)

1. Dandekar, Ramesh Ch A. (1993), “Performance Improvement of Restricted Pairing Algorithm for Latin Hypercube Sampling”, ASA Summer conference (unpublished).
2. Iman R.L. and Conover W. J. (1982), “A Distribution-Free Approach to Inducing Rank Correlation Among Input Variables”, Commun. Stat., B11(3): pp. 311-334.
3. McKay M.D., Conover W. J., and Beckman, R. J. (1979), “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code”, Technometrics 21(2): pp. 239-245.
4. Stein M. (1987), “Large Sample Properties of Simulations Using Latin Hypercube Sampling”, Technometrics (29)2: pp. 143-151.