Finding Exogenous Variation in Data

Finding Outlier DMUs in Data Envelopment Analysis

Random and Exogenous Variation in the Model

Random and exogenous sources of variation play a central role in discrete event simulation. The Sampler, Server, MServer, and formulas are the principle sources of this variation in the QSIM models. However, there are other situations where variation can be meaningfully incorporated into models. For example, you may want to delay the effect of a Trigger for some random or fixed amount of time. ...

DHPLC Applications: Finding DNA Variation on the Y Chromosome

Denaturing High-Performance Liquid Chromatography (DHPLC) is a recently developed technique forthe detection of single nucleotide polymorphisms (SNPs) and mutations. It involves the comparisonbetween two or more DNAs as a mixture of denatured and reannealed PCR products. The methodologyis based on the principle of reversed phase liquid chromatography and uses a unique DNA sepa...

Finding Holes in Data

Attempts are made to employ persistent homology to infer topological properties of point cloud data. Two potential approaches are introducted and briefly evaluated, both relying on Monte Carlo estimation and the program PLEX [9]. First, homology signatures are compared to a null distribution that exhibits complete spatial randomness. This can be regarded as similar to hypothesis testing with th...

Finding rules in data

In the first year of my preparation for doctor thesis at INRIA in the group of Edwin, I worked on the construction of an inference engine and a knowledge base, by consulting various group members, for building an expert system guiding the data analysis package SICLA of the group. One day, Edwin asked me whether one can automatically generate rules for expert systems from data, and I started my ...