Resampling Using the SAS® System
نویسندگان
چکیده
This paper shows how to calculate jackknife and bootstrap estimates using the SAS System By using some simple data step manipulations and by taking advantage of the SAS Macro facility, it is fairly simple to calculate these estimates for complex data sets.
منابع مشابه
A SAS Macro for Calculating Bootstrapped Confidence Intervals About a Kappa Coefficient
Cohen’s kappa coefficient has become a standard method for measuring the degree of agreement between two raters. Confidence intervals for kappa and weighted kappa based on its asymptotic variance are available in the SAS system through the FREQ procedure. However, this variance can become unreliable as sample size decreases or as kappa approaches unity. This paper presents a SAS macro for calcu...
متن کاملEmpirical Distributions of Parameter Estimates in Binary Logistic Regression Using Bootstrap
Bootstrapping is a famous statistical tool that involves resampling procedure to select sample from a population. In this study, we applied randomx bootstrap in binary logistic regression for published data set namely Umaru Impact data. We conducted bootstrap for the coefficient by using SAS (Statistical Analysis System). We observe the distribution of the estimated coefficients with different ...
متن کاملImplementing Resampling Methods for Design-based Variance Estimation in Multilevel Models: Using HLM6 and SAS together
Multilevel models (MLMs) are often used to analyse clustered survey data. Several commercial software packages developed for fitting such models to survey data have implemented a model-based sandwich estimator to estimate the design-based variance (DBV) of the model parameters. However, there is empirical evidence that this estimator underestimates the DBV due to ignoring the stochastic adjustm...
متن کاملImproved Methods for Early Fault Detection in Enterprise Computing Servers Using SAS Tools
Advanced telemetry systems are being developed to collect and archive hundreds of system performance, throughput, quality-of-service (QoS), and physical variables for the purpose of enhancing the reliability, availability, serviceability, scalability, and security of business-critical enterprise computing servers. SAS software was chosen for this project because of the language's powerful codin...
متن کاملComparing the importance of prognostic factors in Cox and logistic regression using SAS
Two SAS macro programs are presented that evaluate the relative importance of prognostic factors in the proportional hazards regression model and in the logistic regression model. The importance of a prognostic factor is quantified by the proportion of variation in the outcome attributable to this factor. For proportional hazards regression, the program %RELIMPCR uses the recently proposed meas...
متن کامل