Much of the recent research has been aimed at improving web performance and scalability. For attaining the goal of improving web performance the basic need is the understanding of WWW work loads. In this paper we present a method useful for the system engineer to improve the service performance of a Web server through session-based Web workload, the best indicator of the users perception of the...

Modeling the failure times of processors and memories in computers is crucial for ensuring reliability robustness data science workflows. By understanding characteristics hardware components, scientists can develop strategies to mitigate impact failures on their computations, design systems that are more fault-tolerant resilient. In particular, time modeling allows predict likelihood frequency ...

This paper investigates the asymptotic properties of quasi-maximum likelihood (QML) estimators for random-effects panel data transformation models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoskedasticity, and simple model structure. We develop a QML-type procedure for model estimation and inference. We...

Bayesian Markov chain Monte Carlo sampling has become increasingly popular in phylogenetics as a method for both estimating the maximum likelihood topology and for assessing nodal confidence. Despite the growing use of posterior probabilities, the relationship between the Bayesian measure of confidence and the most commonly used confidence measure in phylogenetics, the nonparametric bootstrap p...

Motivated by sampling problems in forestry and related fields, we suggest a spatial sampling scheme for estimating the intensity of a point process. The technique is related to the ‘wandering quarter’ method. In applications where the cost of identifying random points is high relative to the cost of taking measurements, for example when identification involves travelling within a large region, ...

This paper provides a method for computing the asymptotic covariance matrix from a likelihood function with known maximum likelihood estimate of the parameters. Philosophically, the basic idea is to assume that the likelihood function should be well approximated by a normal density when asymptotic results about the maximum likelihood estimate are applied for statistical inference. Technically, ...

Few studies have examined the relation between usual physical activity level and rate of hip fracture in older men or applied semiparametric methods from the causal inference literature that estimate associations without assuming a particular parametric model. Using the Physical Activity Scale for the Elderly, the authors measured usual physical activity level at baseline (2000-2002) in 5,682 U...

Pseudo empirical likelihood ratio confidence intervals for finite population parameters are based on asymptotic χ2 approximation to an adjusted pseudo empirical likelihood ratio statistic, with the adjustment factor related to the design effect. Calculation of the design effect involves variance estimation and hence requires second order inclusion probabilities. It also depends on how auxiliary...

The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distrib...

This paper presents a confidence interval quantification of maximum likelihood estimation of participant reliability in social sensing applications. The work is motivated by the emergence of social sensing as a data collection paradigm, where humans perform the data collection tasks. A key challenge in social sensing applications lies in the uncertain nature of human measurements. Unlike well-c...