Split-sample method

The split-sample method is often used with so few samples in the test set, however, that the validation is almost meaningless. One can evaluate the adequacy of the size of the test set by computing the statistical significance of the classification error rate on the test set or by computing a confidence interval for the test set error rate. Because the test set is separate from the training set, the number of errors on the test set has a binomial distribution. 

Cross-validation is an alternative to the split-sample method of estimating prediction accuracy (5). Molinaro et al. describe and evaluate many variants of cross-validation and bootstrap re-sampling for classification problems where the number of candidate predictors vastly exceeds the number of cases (13). The cross-validated prediction error is an estimate of the prediction error associated with application of the algorithm for model building to the entire dataset. 

The main advantage of the split-sample method is ease of computation. In addition, since the prediction mle is developed only once, a completely specified algorithm for classifier development need not be available thus, the development can be more informal and subjective. Regardless of the computational ease, there... 

Analysis of Duplicate Samples An effective method for determining the precision of an analysis is to analyze duplicate samples. In most cases the duplicate samples are taken from a single gross sample (also called a split sample), although in some cases the duplicates must be independently collected gross samples. The results from the duplicate samples, Xi and X2, are evaluated by determining the difference, d, or the relative difference, d) between the samples... 

Fig. 4.1. Stratification consists of splitting the interval of interest into subintervals, thereby reducing the free energy barriers inside each window. The umbrella sampling method can bias the sampling and attempt to make it more uniform...

Interlaboratory Quality Control. In addition to the mandatory quality control practices just outlined, the laboratory is encouraged to participate in interlaboratory programs such as relevant performance evaluation (PE) studies, analysis of standard reference materials, and split sample analyses. Participation in interlaboratory analytical method validation studies is also encouraged. 

For a more realistic estimate of the future error one splits the total data set into a training and a prediction part. With the training set the discriminant functions are calculated and with the objects of the prediction or validation set, the error rate is then calculated. If one has insufficient samples for this splitting, other methods of cross-validation are useful, especially the holdout method of LACHENBRUCH [1975] which is also called jackknifing or leaving one out . The last name explains the procedure For every class of objects the discriminant function is developed using all the class mem-... 

Comparability is established by splitting samples between two different laboratories for analysis by the same method. The samples sent to the second laboratory are known as QA or referee samples. Data comparability is assessed as a qualitative comparison of the results. If the same analytes have been detected and the concentrations are of the same order of magnitude, the data are comparable. 

All documents related to and created during onsite sampling, sample splitting, sample preparation, and analysis are attached to the on-site analysis report (see below). The on-site analysis report is part of the report created by the IT concerning the inspection activities and subject to the same confidentiality regime as the report itself. Under no circumstances are any of these documents sent together with samples off-site. The on-site analysis report has to provide data to validate the method as well as the analysis result in accordance with OPCW procedures and the Quality System requirements. 

Injection of the protein sample (antigen or antibody). In frontal analysis the sample is applied continuously to the column by switching to the eluent containing the protein at the desired concentration. With the split-peak method, small pulses are repeatedly injected into the column. 

The immunoassay will be compared with an existing tested nonimmunochemical analytical method, but this need not be a one-to-one evaluation performed on split samples. A sufficient number of samples will be split to be statistically meaningful. It is the intent of an evaluation to verify the statistical reliability of the Immunoassay as an analytical method. The appropriate matrices to be analyzed will be determined by the data quality objectives. Analysis of the same material, on different days, will be performed by the immunoassay to... 

Thienpont LM, Van Nuwenborg JE, Stockl D. Intrinsic and routine quality of serum total potassium measurement as investigated by split-sample measurement with an ion chromatography candidate reference method. Clin Chem 1998 44 849-57. 

Split sample is a popular resampling method, also referred to as the learning-test split or holdout method (McLachlan, 1992), which entails a single partition of the observed data into a training set and a test set. The size of the two sets is based on a predetermined proportion p for the test set. For example, if p =, this method allots two-thirds of the observed data to the training set and one-third to the test set. Figure 10.5 illustrates this resampling method. 

Monte Carlo CV was performed on the lymphoma dataset resulting in error rates similar to the other methods. The fe-values are similar to the other methods except for split-sample and two-fold CV, which had smaller values for k. 

Standard deviation, MSE, and bias of all methods. The small k chosen for two-fold CV and split sample with p = is due to the reduced training set size. For r-fold CV, a significant decrease in prediction error, bias, and MSE is seen as v increases from 2 to 10. Tenfold CV has a slightly decreased error estimate compared to LOOCV as well as a smaller standard deviation, bias, and MSE however, the LOOCV k is smaller than that of 10-fold CV. Repeated 5-fold CV decreases the standard deviation and MSE over 5-fold CV however, values for the bias and k are slightly larger. In comparison to 10-fold CV, the 0.632-1- bootstrap has a smaller standard deviation and MSE with a larger prediction error, bias, and k. 

The first method, shown in the flow diagram of Fig. 1, used a split sample flow which passed simultaneously through two 5 A molecular sieve columns and recombined before entering the thermal conductivity detector. Molecular sieves have been used previously [ ]... 

In the split/pool method of combinatorial synthesis, mixtures of compounds are made that are difficult to characterize. The LC/CLND of a nominally equimolar pool (based on nitrogen) should yield equal-sized chromatographic peaks of compounds. In the early stages of a lead development project, weighable quantities of authentic pure samples of a compound are not available, and yet quantitative measurements such as IC50, solubility, or plasma stability need to be made. LC/CLND can be used to calibrate solutions made from submilligram synthetic samples. LC/CLND is an important new technique to add to the arsenal of the organic analytical laboratory. 

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