GRD: An SPSS extension command for generating random data

An Extension to Imprecise Data Envelopment Analysis

The standard data envelopment analysis (DEA) method assumes that the values for inputs and outputs are exact. While DEA assumes exact data, the existing imprecise DEA (IDEA) assumes that the values for some inputs and outputs are only known to lie within bounded intervals, and other data are known only up to an order. In many real applications of DEA, there are cases in which some of the input ...

Generating Constrained Random Data with Uniform Distribution

We present a technique for automatically deriving test data generators from a predicate expressed as a Boolean function. The distribution of these generators is uniform over values of a given size. To make the generation efficient we rely on laziness of the predicate, allowing us to prune the space of values quickly. In contrast, implementing test data generators by hand is labour intensive and...

Command Forces: An Extension of DIS Virtual Simulation

Command Forces (CFOR) is a concept that incorporates explicit modeling of battlefield command and control (C2) in virtual simulation. CFOR extends the Distributed Interactive Simulation (DIS) architecture by adding a new class of DIS entities (command entities) and explicit representation of the information exchanged among these entities (Command and Control Simulation Interface Language). Comm...

Random Forests for Generating Partially Synthetic, Categorical Data

Several national statistical agencies are now releasing partially synthetic, public use microdata. These comprise the units in the original database with sensitive or identifying values replaced with values simulated from statistical models. Specifying synthesis models can be daunting in databases that includemany variables of diverse types. These variablesmay be related inways that can be diff...

TURBID: A Routine for Generating Random Turbulent Inflow Data