4,140 research outputs found
Multivariate Location: Robust Estimators And Inference
The sample mean can have poor efficiency relative to various alternative estimators under arbitrarily small departures from normality. In the multivariate case, (affine equivariant) estimators have been proposed for dealing with this problem, but a comparison of various estimators by Massé and Plante (2003) indicated that the small-sample efficiency of some recently derived methods is rather poor. This article reports that a skipped mean, where outliers are removed via a projection-type outlier detection method, is found to be more satisfactory. The more obvious method for computing a confidence region based on the skipped estimator (using a slight modification of the method in Liu & Singh, 1997) is found to be unsatisfactory except in the bivariate case, at least when the sample size is small. A much more effective method is to use the Bonferroni inequality in conjunction with a standard percentile bootstrap technique applied to the marginal distributions
Within Groups Multiple Comparisons Based On Robust Measures Of Location
Consider the problem of performing all pair-wise comparisons among J dependent groups based on measures of location associated with the marginal distributions. It is well known that the standard error of the sample mean can be large relative to other estimators when outliers are common. Two general strategies for addressing this problem are to trim a fixed proportion of observations or empirically check for outliers and remove (or down-weight) any that are found. However, simply applying conventional methods for means to the data that remain results in using the wrong standard error. Methods that address this problem have been proposed, but among the situations considered in published studies, no method has been found that gives good control over the probability of a Type I error when sample sizes are small (less than or equal to thirty); the actual probability of a Type I error can drop well below the nominal level. The paper suggests using a slight generalization of a percentile bootstrap method to address this problem
Study of fuel cell on-site, integrated energy systems in residential/commercial applications
Three building applications were selected for a detailed study: a low rise apartment building; a retail store, and a hospital. Building design data were then specified for each application, based on the design and construction of typical, actual buildings. Finally, a computerized building loads analysis program was used to estimate hourly end use load profiles for each building. Conventional and fuel cell based energy systems were designed and simulated for each building in each location. Based on the results of a computer simulation of each energy system, levelized annual costs and annual energy consumptions were calculated for all systems
A Study of Giant Pulses from PSR J1824-2452A
We have searched for microsecond bursts of emission from millisecond pulsars
in the globular cluster M28 using the Parkes radio telescope. We detected a
total of 27 giant pulses from the known emitter PSR J1824-2452A. At wavelengths
around 20 cm the giant pulses are scatter-broadened to widths of around 2
microseconds and follow power-law statistics. The pulses occur in two narrow
phase-windows which correlate in phase with X-ray emission and trail the peaks
of the integrated radio pulse-components. Notably, the integrated radio
emission at these phase windows has a steeper spectral index than other
emission. The giant pulses exhibit a high degree of polarization, with many
being 100% elliptically polarized. Their position angles appear random.
Although the integrated emission of PSR J1824-2452A is relatively stable for
the frequencies and bandwidths observed, the intensities of individual giant
pulses vary considerably across our bands. Two pulses were detected at both
2700 and 3500 MHz. The narrower of the two pulses is 20 ns wide at 3500 MHz. At
2700 MHz this pulse has an inferred brightness temperature at maximum of 5 x
10^37 K. Our observations suggest the giant pulses of PSR J1824-2452A are
generated in the same part of the magnetosphere as X-ray emission through a
different emission process to that of ordinary pulses.Comment: Accepted by Ap
Preliminary Testing for Normality: Is This a Good Practice?
Normality is a distributional requirement of classical test statistics. In order for the test statistic to provide valid results leading to sound and reliable conclusions this requirement must be satisfied. In the not too distant past, it was claimed that violations of normality would not likely jeopardize scientific findings (See Hsu & Feldt, 1969; Lunney, 1970). Recent revelations suggest otherwise (See e.g., Micceri, 1989; Keselman, Huberty, Lix et al., 1998; Erceg-Hurn, Wilcox, & Keselman, 2013; Wilcox and Keselman, 2003; Wilcox, 2012a, b). Unfortunately the data obtained in psychological investigations rarely, if ever, meet the requirement of normally distributed data (Micceri, 1989; Wilcox, 2012a, b). Consequently, it could be the case that the results from many of the investigations conducted in psychology provide invalid results. Accordingly, authors recommend that researchers attempt to assess the validity of assuming data are normal in form prior to conducting a test of significance (Erceg-Hurn, et al., 2013; Keselman, et al., 1998). Present evidence suggests that a popular fit-statistic, the Kolmogorov-Smirnov test does a poor job of evaluating whether data are normal. Our investigation based on this statistic and other fit-statistics provides a more favorable picture of preliminary testing for normality
Trimming, Transforming Statistics, And Bootstrapping: Circumventing the Biasing Effects Of Heterescedasticity And Nonnormality
Researchers can adopt different measures of central tendency and test statistics to examine the effect of a treatment variable across groups (e.g., means, trimmed means, M-estimators, & medians. Recently developed statistics are compared with respect to their ability to control Type I errors when data were nonnormal, heterogeneous, and the design was unbalanced: (1) a preliminary test for symmetry which determines whether data should be trimmed symmetrically or asymmetrically, (2) two different transformations to eliminate skewness, (3) the accuracy of assessing statistical significance with a bootstrap methodology was examined, and (4) statistics that use a robust measure of the typical score that empirically determined whether data should be trimmed, and, if so, in which direction, and by what amount were examined. The 56 procedures considered were remarkably robust to extreme forms of heterogeneity and nonnormality. However, we recommend a number of Welch-James heteroscedastic statistics which are preceded by the Babu, Padmanaban, and Puri (1999) test for symmetry that either symmetrically trimmed 10% of the data per group, or asymmetrically trimmed 20% of the data per group, after which either Johnson\u27s (1978) or Hall\u27s (1992) transformation was applied to the statistic and where significance was assessed through bootstrapping. Close competitors to the best methods were found that did not involve a transformation
Geometric scaling in high-energy QCD at nonzero momentum transfer
We show how one can obtain geometric scaling properties from the
Balitsky-Kovchegov (BK) equation. We start by explaining how, this property
arises for the b-independent BK equation. We show that it is possible to extend
this model to the full BK equation including momentum transfer. The saturation
scale behaves like max(q,Q_T) where q is the momentum transfer and Q_T a
typical scale of the target.Comment: 4 pages, 2 figures. Talk given by G. Soyez at the "Rencontres de
Moriond", 12-19 March 2005, La Thuile, Ital
Breakdown of Conformal Invariance at Strongly Random Critical Points
We consider the breakdown of conformal and scale invariance in random systems
with strongly random critical points. Extending previous results on
one-dimensional systems, we provide an example of a three-dimensional system
which has a strongly random critical point. The average correlation functions
of this system demonstrate a breakdown of conformal invariance, while the
typical correlation functions demonstrate a breakdown of scale invariance. The
breakdown of conformal invariance is due to the vanishing of the correlation
functions at the infinite disorder fixed point, causing the critical
correlation functions to be controlled by a dangerously irrelevant operator
describing the approach to the fixed point. We relate the computation of
average correlation functions to a problem of persistence in the RG flow.Comment: 9 page
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