Density probability plots

Probability plots are popular and effective tools for the graphical assessment of the goodness-of-fit of a given dataset to a hypothesised probability distribution, F say, with density f. The user can easily see any departures from F that there are, but the interpretation of such departures may not be immediately apparent. (It may take some time to work out their meaning, or else to resort to a set of rules for the interpretation of probability plots.) We investigate whether instant interpretability of these tools can be aided by a simple transformation which transfers visual assessment to the realms of familiar and immediate comparisons between densities. We seek to do so without smoothing, and see how far this allows us to go. The idea is very effective, and rather more so than quantile-quantile plots, for f's that have a single peak when the density g from which the data truly come is also unimodal. The idea is much less appealing, however, when f and g have different modalities from each other

On the Equivalence of Cohen’s Kappa and the Hubert-Arabie Adjusted Rand Index

Correction for chance agreement, Partitions, Clustering method, Matching table, Simple matching coefficient, Similarity indices, Resemblance measures,

A variable-selection heuristic for K-means clustering

cluster analysis, K-means partitioning, variable selection, heuristics,

A robust statistical procedure to discover expression biomarkers using microarray genomic expression data

Microarray has become increasingly popular biotechnology in biological and medical researches, and has been widely applied in classification of treatment subtypes using expression patterns of biomarkers. We developed a statistical procedure to identify expression biomarkers for treatment subtype classification by constructing an F-statistic based on Henderson method III. Monte Carlo simulations were conducted to examine the robustness and efficiency of the proposed method. Simulation results showed that our method could provide satisfying power of identifying differentially expressed genes (DEGs) with false discovery rate (FDR) lower than the given type I error rate. In addition, we analyzed a leukemia dataset collected from 38 leukemia patients with 27 samples diagnosed as acute lymphoblastic leukemia (ALL) and 11 samples as acute myeloid leukemia (AML). We compared our results with those from the methods of significance analysis of microarray (SAM) and microarray analysis of variance (MAANOVA). Among these three methods, only expression biomarkers identified by our method can precisely identify the three human acute leukemia subtypes