Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

A statistical framework for channel availability modelling in the polarisation domain

Cognitive radio has been proposed as a means of improving the spectrum utilisation and increasing spectrum efficiency of wireless systems. This can be achieved by allowing cognitive radio terminals to monitor their spectral environment and opportunistically access the unoccupied frequency channels. Due to the opportunistic nature of cognitive radio, the overall performance of such networks depends on the spectrum occupancy or availability patterns. Appropriate knowledge on channel availability can optimise the sensing performance in terms of spectrum and energy efficiency. This work proposes a statistical framework for the channel availability in the polarization domain. A Gaussian Normal approximation is used to model real-world occupancy data obtained through a measurement campaign in the cellular frequency bands within a realistic scenario.

Annual statistical report fiscal year 2013

The South Carolina Department of Probation, Parole and Pardon Services annually publishes a statistical report providing a description of the offender population and the agency's programs.

Annual statistical report fiscal year 2009

The South Carolina Department of Probation, Parole and Pardon Services annually publish a statistical report providing a description of the offender population and the agency's programs.

Annual statistical report fiscal year 2010

The South Carolina Department of Probation, Parole and Pardon Services annually publish a statistical report providing a description of the offender population and the agency's programs.

Annual statistical report fiscal year 2011

The South Carolina Department of Probation, Parole and Pardon Services annually publish a statistical report providing a description of the offender population and the agency's programs.

Annual statistical report fiscal year 2012

The South Carolina Department of Probation, Parole and Pardon Services annually publish a statistical report providing a description of the offender population and the agency's programs.

SOUTH CAROLINA STATISTICAL ABSTRACT 1986

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

SOUTH CAROLINA STATISTICAL ABSTRACT 1985

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

South Carolina Statistical Abstract 1995

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

SOUTH CAROLINA STATISTICAL ABSTRACT 1992

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

South Carolina Statistical Abstract 1993

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

South Carolina Statistical Abstract 1994

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

South Carolina Statistical Abstract 2000-2001

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.

South Carolina Statistical Abstract '96

The South Carolina Budget and Control Board, Office of Research and Statistics annually published Statistical Abstracts, a comprehensive, single-source reference of demographic and economic data pertinent to the state. This publication is now an online publication by the South Carolina Revenue and Fiscal Affairs Office.