Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.

Bootstrap based goodness-of-fit-tests

Abstract: Let ℱ={Fθ} be a parametric family of distribution functions, and denote withFn the empirical df of an iid sample Goodness-of-fit tests of a composite hypothesis (contained in ℱ) are usually based on the so-called estimated empirical process Typically, they are not distribution-free In such a situation the bootstrap offers a useful alternative It is the purpose of this paper to show that this approximation holds with probability one A simulation study is included which demonstrates the validity of the bootstrap for several selected parametric families

An efficient method of moments estimator for discrete choice models with choice-based sampling

Abstract: In this paper, a new estimator is proposed for discrete choice models with choice-based sampling. The estimator is efficient and can incorporate information on the marginal choice probabilities in a straightforward manner and for that case leads to a procedure that is computationally and intuitively more appealing than the estimators that have been proposed before. The idea is to start with a flexible parametrization of the distribution of the explanatory variables and then rewrite the estimator to remove dependence on these parametric assumptions. Copyright 1992 by The Econometric Society.

An Aligned Rank Transform Procedure for Multifactor Contrast Tests

Abstract: Data from multifactor HCI experiments often violates the assumptions of parametric tests (i.e., nonconforming data). The Aligned Rank Transform (ART) has become a popular nonparametric analysis in HCI that can find main and interaction effects in nonconforming data, but leads to incorrect results when used to conduct post hoc contrast tests. We created a new algorithm called ART-C for conducting contrast tests within the ART paradigm and validated it on 72,000 synthetic data sets. Our results indicate that ART-C does not inflate Type I error rates, unlike contrasts based on ART, and that ART-C has more statistical power than a t-test, Mann-Whitney U test, Wilcoxon signed-rank test, and ART. We also extended an open-source tool called ARTool with our ART-C algorithm for both Windows and R. Our validation had some limitations (e.g., only six distribution types, no mixed factorial designs, no random slopes), and data drawn from Cauchy distributions should not be analyzed with ART-C.

Parametric methods for anomaly detection in aggregate traffic

Abstract: This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phase or manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit-rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less.

Spatial Variation of Seismic Ground Motions: Modeling and Engineering Applications

Abstract: Introduction Stochastic estimation of spatial variability Basic definitions Stochastic processes Bi-variate stochastic processes Coherency Multi-variate stochastic processes and stochastic fields Parametric modeling of spatial variability Parametric power spectral densities Early studies on spatial variability Dependence of coherency on physical parameters Parametric coherencymodeling Parametric cross spectrum modeling Physical characterization of spatial variability Frequency-wavenumber (F-K) spectra Amplitude and phase variability Seismic ground-surface strains Semi-empirical estimation of the propagation velocity Estimation of the surface strain field Accuracy of single-station strain estimation Incoherence vs propagation effects in surface strains Displacement gradient estimation from array data Considerations in the estimation of seismic ground strains Random vibrations for multi-support excitations Introduction to random vibrations Discrete-parameter systems Distributed-parameter systems Analysis of rms lifeline response Additional random vibration considerations Simulations of spatially variable ground motions Simulation of random processes Simulation of random fields Simulation ofmulti-variate stochastic vector processes Conditionally simulated ground motions Conditional simulation of random processes Processing of simulated acceleration time series Example applications Engineering Applications Large, mat, rigid foundations Dams Suspension and cable-stayed bridges Highway bridges Some concluding remarks References