Search results
Results from the WOW.Com Content Network
Missing not at random (MNAR) (also known as nonignorable nonresponse) is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). [5] To extend the previous example, this would occur if men failed to fill in a depression survey because of their level of depression.
An example of this behavior is given in the section analyzing the missing-value semantics of Nulls. The SQL COALESCE function or CASE expressions can be used to "simulate" Null equality in join criteria, and the IS NULL and IS NOT NULL predicates can be used in the join criteria as well. The following predicate tests for equality of the values ...
That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information.
The p-values of the rejected null hypothesis (i.e. declared discoveries) are colored in red. Note that there are rejected p-values which are above the rejection line (in blue) since all null hypothesis of p-values which are ranked before the p-value of the last intersection are rejected. The approximations MFDR = 0.02625 and AFDR = 0.00730, here.
The q-value can be interpreted as the false discovery rate (FDR): the proportion of false positives among all positive results. Given a set of test statistics and their associated q-values, rejecting the null hypothesis for all tests whose q-value is less than or equal to some threshold ensures that the expected value of the false discovery rate is .
The null distribution of the Pearson statistic with j rows and k columns is approximated by the chi-squared distribution with (k − 1)(j − 1) degrees of freedom. [12] This approximation arises as the true distribution, under the null hypothesis, if the expected value is given by a multinomial distribution.
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
Thus, the Q–Q plot is a parametric curve indexed over [0,1] with values in the real plane R 2. Typically for an analysis of normality, the vertical axis shows the values of the variable of interest, say x with CDF F(x), and the horizontal axis represents N −1 (F(x)), where N −1 (.) represents the inverse cumulative normal distribution ...