TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 159

SAS/Ets User's Guide 159. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 1572 F Chapter 22 The SEVERITY Procedure Experimental The results shown in Output indicate that the Burr distribution has now converged and that the Burr and Weibull distributions have an almost identical fit for the data. The NORMAL_S distribution is still the best distribution according to the likelihood-based criteria. Output Summary of Results after Changing Maximum Number of Iterations The SEVERITY Procedure Input Data Set Name Model Selection Table Distribution Converged -2 Log Likelihood Selected Normal_s Yes Yes Burr Yes No Weibull Yes No All Fit Statistics Table Distribution -2 Log Likelihood AIC AICC BIC KS Normal_s Burr Weibull All Fit Statistics Table Distribution AD CvM Normal_s Burr Weibull The comparison of the PDF estimates of all the candidates is shown in Output . Each plotted PDF estimate is an average computed over the N PDF estimates that are obtained with the scale parameter determined by each of the N observations in the input data set. The PDF plot shows that the Burr and Weibull models result in almost identical estimates. All the estimates have a slight left skew with the mode closer to Y 25 which is the mean of the simulated sample. Example Defining a Model for Gaussian Distribution with a Scale Parameter F 1573 Output Comparison of EDF and CDF Estimates of the Fitted Models The P-P plots for the Normal_s and Burr distributions are shown in Output . These plots show how the EDF estimates compare against the CDF estimates. Each plotted CDF estimate is an average computed over the N CDF estimates that are obtained with the scale parameter determined by each of the N observations in the input data set. Comparing the P-P plots of Normal_s and Burr distributions .

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• Hướng dẫn sử dụng SAS ETS
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