In the presence of a number of algorithms for classi ̄cation and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classi ̄cation studies in software engineering, there is a lack ...

The zero-in ated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-in ated Poisson model. We consider a frequentist analysis, a jackknife estimator and non-parametric bootstrap for parameter estimation of zero-in ated negative binomial regression models. In addition, an EM-type algorithm is developed to perform maximum like...

This result is obvious but also useful because it tells us that the sample mean is an unbiased estimate for the exact mean, in the sense that the average of the sample mean, over many repetitions, is the exact mean. An unbiased estimate will become more and more accurate as the number of data points is increased. However, a biased estimate not continue to improve with increasing N once the erro...

Artificial neural networks (ANN) are nonparametric models and like all nonparametric models require a large number of observations to build and evaluate the model. But data is always finite and most often scarce in real world applications. The question now becomes: How does a modeler build an ANN model on a limited sample size and obtain an accurate and reliable estimate the model’s error? The ...

In this paper, we propose new resampling models in data envelopment analysis (DEA). Input/output values are subject to change for several reasons, e.g., measurement errors, hysteretic factors, arbitrariness and so on. Furthermore, these variations differ in their input/output items and their decision-making units (DMU). Hence, DEA efficiency scores need to be examined by considering these facto...

A method for resampling time series generated by a deterministic chaotic data generating process (DGP) is proposed. Given an observed time series, this method potentially allows one to obtain an arbitrary number of time series of arbitrary length which can be considered as a product of the same unknown DGP. The notion of shadowing and brittleness of the pseudo-orbit proves to be particularly us...

ELAINE BORLAND HOFFMAN: Within Cluster Resampling (Under the direction of Clarice R. Weinberg) Dependence among observations from the same group is known as within-cluster correlation. Typically, distinct clusters are independent, but special methods are required to account for the existence of the within-cluster correlation. Although not accounting for the within-cluster correlation may produc...

Receiver operating characteristic (ROC) curves are very popular for evaluating a diagnostic test or score performances in various decision making applications: medicine, marketing, credit scoring etc. The ROC curve provides a concise graphical representation of the trade off between sensitivity and specificity. We will focus here on supervised classification into two groups. Error rate estimati...

The well known problem among most random access protocols in wireless networks is that the throughput drops rapidly in heavy load. To cope with this problem, one has to control to the load ooered to a network. Unlike the traditional backoo policy in Ethernet where backoo occurs after collision, in this paper we propose various control schemes based on the new idea of resampling, where carrier s...