REGRESSION FOR COMPOSITIONAL DATA WITH COMPOSITIONAL DATA AS PREDICTOR VARIABLES WITH OR WITHOUT ZERO VALUES.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 219, doi. 10.6339/JDS.201901_17(1).0010
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- Publication type:
- Article
Works matching IS 1680743X AND DT 2019 AND VI 17 AND IP 1
Results: 10
1
2
MACHINE LEARNING ALGORITHMS TO PREDICT THE CHILDHOOD ANEMIA IN BANGLADESH.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 195, doi. 10.6339/JDS.201901_17(1).0009
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- Article
3
WEIBULL DISTRIBUTION AS AN ACTUARIAL RISK MODEL: COMPUTATION OF ITS PROBABILITY OF ULTIMATE RUIN AND THE MOMENTS OF THE TIME TO RUIN, DEFICIT AT RUIN AND SURPLUS PRIOR TO RUIN.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 161, doi. 10.6339/JDS.201901_17(1).0007
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- Publication type:
- Article
4
WEIGHTED QUANTILE REGRESSION THEORY AND ITS APPLICATION.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 145, doi. 10.6339/JDS.201901_17(1).0007
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- Publication type:
- Article
5
THE NON-LINEAR RELATIONSHIPS OF NUMERIC FACTORS ON HOUSING PRICES BY USING GAM.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 131, doi. 10.6339/JDS.201901_17(1).0006
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- Publication type:
- Article
6
DECODING OF CHARACTERISTICS OF URBAN CONSUMPTION BASED ON "BRAND CLUSTER".
- Published in:
- Journal of Data Science, 2019, v. 17, n. 1, p. 107, doi. 10.6339/JDS.201901_17(1).0005
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- Publication type:
- Article
7
A ENSEMBLE MACHINE LEARNING BASED SYSTEM FOR MERCHANT CREDIT RISK DETECTION IN MERCHANT MCC MISUSE.
- Published in:
- Journal of Data Science, 2019, v. 17, n. 1, p. 81, doi. 10.6339/JDS.201901_17(1).0004
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- Publication type:
- Article
8
AUTOMATING DATA ANALYSIS METHODS IN EPIDEMIOLOGY.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 55, doi. 10.6339/JDS.201901_17(1).0003
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- Publication type:
- Article
9
A SPACIAL-TEMPORAL GAUSSIAN MIXTURE MODEL FOR ANNUAL AVERAGE PM<sub>2.5</sub> CONCENTRATION ANALYSIS.
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- Journal of Data Science, 2019, v. 17, n. 1, p. 37, doi. 10.6339/JDS.201901_17(1).0002
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- Publication type:
- Article
10
A COMPARISON OF REGULARIZED LINEAR DISCRIMINANT FUNCTIONS FOR POORLY-POSED CLASSIFICATION PROBLEMS.
- Published in:
- Journal of Data Science, 2019, v. 17, n. 1, p. 1, doi. 10.6339/JDS.201901_17(1).0001
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- Publication type:
- Article