The simplest experimental design is the completely randomized design with 1 factor. In this design, each experimental unit is randomly assigned to each factor level. This design is most useful for a homogeneous population (one that does not have major differences between any sub-populations). It is appealing because of its simplicity and flexibility – it can […]
A machine learning algorithm can be classified as either parametric or non-parametric. A parametric algorithm has a fixed number of parameters. A parametric algorithm is computationally faster, but makes stronger assumptions about the data; the algorithm may work well if the assumptions turn out to be correct, but it may perform badly if the assumptions […]
You produce a non-parametric distribution. Then you obtain, say, 10 random variables (RV) from this non-parametric distribution- much the same way as you would obtain random variables from a (parametric) normal distribution with stated mean and variance. But unlike the parametric distribution, where our RVs would occur around the mean (our parameter), RVs from a […]
We often use AIC to discern the best model among candidates. Now suppose we have two (non-parametric) models, which use mass points and weights to model a random variable: model A uses 4 mass points to model a random variable (i.e. the height of men in Singapore) model B uses 5 mass points to mode […]
Histogram: LM estimates of Intercepts Histogram: LM estimates of Gradient QQ Plot: LM estimates of Intercepts QQ Plot: LM estimates of Gradient Figure 1: Gradient appears to follow a normal distribution more than intercept . When do we use a parametric model, and when do we use a non-parametric one? In […]
Analyzing Likert scale responses really comes down to what you want to accomplish (e.g. Are you trying to provide a formal report with probabilities or are you trying to simply understand the data better). Sometimes a couple of graphs are sufficient and a formalize statistical test isn’t even necessary. However, with how easy it is […]
Becky Passonneau and colleagues at the Center for Computational Learning Systems (CCLS) at Columbia have been working on a project for ConEd (New York’s major electric utility) to rank structures based on vulnerability to secondary events (e.g., transformer explosions, cable meltdowns, electrical fires). They’ve been using the R implementation BayesTree of Chipman, George and McCulloch’s [...]
A variation of the standard definition of Kendall correlation coefficient is necessary in order to deal with data samples with tied ranks. It known as the Kendall’s tau-b coefficient and is more effective in determining whether two non-parametr...
The correlation coefficient is a measurement of correlation between two random variables. While its computation is straightforward, it is not readily applicable to non-parametric statistics. read more
