Detecting outliers when fitting data with nonlinear regression - A new method based on robust nonlinear regression and the false discovery rate

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Motulsky, Harvey J ^a   Brown, Ronald E ^b  

^a GraphPad Software   (United States)

^b AISN Software Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FALSE DISCOVERY RATE; LEAST SQUARES REGRESSION; LORENTZIAN DISTRIBUTIONS; MULTIPLE COMPARISON; NON-LINEAR REGRESSION; NONLINEAR CURVE FIT; OUTLIER IDENTIFICATION; ROBUST REGRESSIONS;

CURVE FITTING; NORMAL DISTRIBUTION; REGRESSION ANALYSIS; STATISTICS;

NONLINEAR ANALYSIS;

ACCURACY; ANALYTICAL ERROR; ARTICLE; COMPUTER SIMULATION; DATA ANALYSIS; FALSE POSITIVE RESULT; LINEAR REGRESSION ANALYSIS; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; PLOTS AND CURVES; PROBABILITY; BIOLOGICAL MODEL; LABORATORY DIAGNOSIS; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

LORENTZIA;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FALSE POSITIVE REACTIONS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NONLINEAR DYNAMICS; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; REGRESSION ANALYSIS;

EID: 33744513096     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-123     Document Type: Article

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