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Non-Parametric Statistical Tests
2018Chapter 10 introduces the most commonly used non-parametric tests, as well as the appropriate situations for their use. It also examines concepts of sample size, power, and validity when using these tests. In addition, this chapter discusses the major advantages and disadvantages of non-parametric tests, and how they compare to their optimal parametric
Felipe Fregni +2 more
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Non-parametric Statistical Methods
1987Basic statistics and econometrics courses stress methods based on assuming that the data or error term in regression models follow the normal distribution. Indeed, the efficiency of least squares estimates relies on the assumption of normality. In order to lessen the dependence of statistical inference on that assumption statisticians developed methods
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2012
Beginning statistics students are usually introduced to what are called “parametric” statistics methods. Those methods utilize “models” of score distributions such as the normal (Gaussian) distribution, Poisson distribution, binomial distribution, etc.
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Beginning statistics students are usually introduced to what are called “parametric” statistics methods. Those methods utilize “models” of score distributions such as the normal (Gaussian) distribution, Poisson distribution, binomial distribution, etc.
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Non-parametric statistical static timing analysis
Proceedings of the 45th annual Design Automation Conference, 2008We present a new statistical STA framework based on Monte Carlo analysis that can deal with arbitrary statistical distribution and delay models. Order statistics (non-parametrics) is consistently adopted by which the timing analysis and criticality calculation become distribution-independent.
Masanori Imai +3 more
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