Second-class citizens and experimental design

Article Abstract:

Mechanistic and empirical models both contribute equally in the development of scientific knowledge. Mechanistic models are more parsimonious and often yield better predictions than empirical ones. However, an empirical model developed from a designed experiment or a good data base can lead to an improved mechanistic understanding. Nonlinear design of experiment methods help to bring about a balance between mechanistic and empirical approaches. The relationship between theory, observation and experimentation is discussed.

Evaluation, Science, Scientific method, Experimental design, Research design, Prediction theory

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Tree-based classification and regression, Part 2: Assessing classification performance

Article Abstract:

The classification rule/algorithm will be trained and developed in the supervised learning situation, with adjustable parameters focused on existing and historical data. If only half the training data was used to train the algorithm, the hidden half could then be used to determine the performance. This validates the fitted results by use of part of the data to fit and part to validate. Rules developed in the cross-validation are only used to estimate misclassification rate.

Discriminant analysis

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Bias-corrected intervals

Article Abstract:

The bootstrap confidence interval procedure possesses certain practical and theoretical limitations, despite the numerous benefits it provides for statistical computations. Consequently, several methods have been developed to overcome these flaws and permit practical applications to more complex situations. One such adjustment procedure is Efron's bias-corrected intervals, a very simple method which often finds application in life testing problems.

Statistics (Mathematics), Mathematical statistics, Accelerated life testing, Confidence intervals

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