Statistical Distributions

Generative models of pattern individuality attempt to represent the distribution of observed quantitative features, e.g., by learning parameters from a database, and then use such distributions to determine the probability of two random patterns being the same. Considering fingerprint patterns, Gaussian distributions have been previously used for minutiae location and von-Mises distributions for minutiae orientation so as to determine the probability of random correspondence (PRC) between two fingerprints. Motivated by the fact that ridges have not been modeled in generative models, and using representative ridge points in fingerprint matching, ridge information is incorporated into the generative model by using a third distribution for ridge types. The joint probability of minutiae location, minutiae orientation and ridge type is modeled as a mixture distribution. The proposed model offers a more accurate fingerprint representation from which more reliable PRCs can be computed. Based on parameters estimated from fingerprint databases, PRCs using ridge types are seen to be much smaller than PRCs computed with only minutiae.

Image blur and noise are difficult to avoid in many situations and can often ruin a photograph. We present a novel image deconvolution algorithm that deblurs and denoises an image given a known shift-invariant blur kernel. Our algorithm uses local color statistics derived from the image as a constraint in a unified framework that can be used for deblurring, denoising, and upsampling. A pixel's color is required to be a linear combination of the two most prevalent colors within a neighborhood of the pixel. This two-color prior has two major benefits: it is tuned to the content of the particular image and it serves to decouple edge sharpness from edge strength. Our unified algorithm for deblurring and denoising out-performs previous methods that are specialized for these individual applications. We demonstrate this with both qualitative results and extensive quantitative comparisons that show that we can out-perform previous methods by approximately 1 to 3 DB.

The aim of steganography is to conceal the very existence of hidden communication, so its demand on security is serious. The security includes both imperceptibility and undetectability. But most steganographic methods didn't pay enough attention to the undetectability. In this paper, we propose a novel DCT-based steganographic method for images. The method takes advantage of the similarities of DCT coefficients between the adjacent image blocks and makes the embedding distortion spread to the adjacent image blocks. Experimental results demonstrate that this proposed method can not only preserve good image quality, but also resist some typical statistical attacks.

The algebra of qualified relations proposed by Ceri and Pelagatti supports pruning of distributed database queries, minimizing the number of sites involved in evaluation. Domain interval typing in the framework of formal type inference subsumes this approach, pruning not only unsatisfiable subqueries involving fragmentation predicates, but also unsatisfiable subqueries mentioning any relation attribute

The sum of lognormal distributions is a well-known problem that no closed-form expression exists and it is difficult to evaluate numerically. In this paper, Log Shifted Gamma (LSG) approximation method is proposed to represent the sum of lognormal distributions and to derive a closed-form expression of the typical value of the sum. Illustrative results show that the LSG model provides much more accurate approximation than other previous methods for a wide range of lognormal variances.

In this study, the authors deal with a wireless healthcare monitoring solution based on a secure technology in hospital context. Actually, radio frequency (RF) networks can present electromagnetic disturbances in hospital environments. The authors thus investigate an alternative solution based on infrared (IR) technology. As patient mobility is inevitable, they focus on mobile IR communications considering line-of-sight (LOS) propagation between the transmitter coupled with medical sensors and the receiver. The authors study different mobility scenarios, one in two dimensions (2D) for a fixed transmitter height and another in three dimensions (3D) by considering transmitter height variations. In each case, they analyse the distributions of channel gain state to find the statistical model of the mobile IR channel for a given distribution of the patient locations within the room (uniform or gaussian). By calculating the outage probability from statistical analysis, they investigate the impact of the mobility on data rates and quality of service needed for this application in the case of an on-off keying (OOK) modulation before concluding on the reliability of the studied mobile healthcare monitoring system.

Encapsulated microbubble contrast agents incorporating an adhesion ligand in the microbubble shell are used for molecular imaging with ultrasound. Currently available microbubble agents are produced with techniques that result in a large size variance. Detection of these contrast agents depends on properties related to the microbubble diameter such as resonant frequency, and current ultrasound imaging systems have bandwidth limits that reduce their sensitivity to a polydisperse contrast agent population. For ultrasonic molecular imaging, in which only a limited number of targeted contrast agents may be retained at the site of pathology, it is important to optimize the sensitivity of the imaging system to the entire population of contrast agent. This article presents contrast agents with a narrow size distribution that are targeted for molecular imaging applications. The production of a functionalized, lipid-encapsulated, microbubble contrast agent with a monodisperse population is demonstrated, and we evaluate parameters that influence the size distribution and demonstrate initial acoustic testing.

A novel object detection algorithm is developed for automatically detecting road manhole and sewer well covers from mobile light detection and ranging point clouds. This algorithm takes advantage of a marked point process of disks and rectangles to model the locations of manhole and sewer well covers and their geometric dimensions. A reversible jump Markov chain Monte Carlo algorithm is implemented for simulating the posterior distribution obtained using a Bayesian paradigm. The detection results obtained from the road surface point clouds acquired by a RIEGL VMX-450 system show that the manhole and sewer well covers can be detected automatically and accurately. The performance achieved using the proposed algorithm is much more accurate and effective than those of the other three existing algorithms. Index Terms-Manhole, marked point process, mobile light detection and ranging (LiDAR), point cloud, reversible jump Markov chain Monte Carlo (RJMCMC), sewer well.

Prenatal care should improve infant health, yet research frequently finds only weak effects. If there are two kinds of pregnancies, 'complicated' and 'normal' ones, then combining these pregnancies may lead prenatal care to appear ineffective. Data from the National Maternal and Infant Health Survey (NMIHS) offers compelling evidence. The standard 2SLS approach yields obviously bimodal residuals and frequently insignificant prenatal care coefficients. In contrast, estimating birth weights with a finite mixture model yields estimates revealing that prenatal care has a substantial effect on 'normal' pregnancies. Our Monte Carlo experiment confirms that ignoring even a small proportion of 'complicated' pregnancies can lead prenatal care to appear unimportant.

Process-induced variations are an important consideration in the design of integrated circuits. Until recently, it was sufficient to model die-to-die shifts in device performance, leading to the well known worst-case modeling and design methodology . However, current and near-future integrated circuits are large enough that device and interconnect parameter variations within the chip are as important as those same variations from chip to chip. This presents a new set of challenges for process modeling and characterization and for the associated design tools and methodologies.

At the point of common coupling (PCC), the summation of two harmonic voltage vectors at same frequency is only certain if their amplitudes and phase angles are well known. Therefore, there are many cases where the phase angle difference between harmonic voltage vectors is unknown. In practice, for example, in calculating the steady state rating of the a.c. passive harmonic filter, arithmetic summation method is often used in determining maximal harmonic level. This summation law is very conservative as it may result in high cost of equipment ratings. Actually, a compromise should be made in taking into account the equipment safety and the risk of overrating as well as excessive costs. Based on uniform distribution of difference in phase angles, the paper provides an algorithm to calculate the probability upon which the magnitude of summation vector of two harmonic voltages may exceed a given value. On the basis of the probability and the given harmonic value, harmonic summation can be assessed. This may also result in a recommendation of another harmonic summation method for the standard IEC/TR 61000-3-6.

The assumption of normality underlies much of the standard statistical methodology. Knowing how to determine whether a sample of measurements is from a normally distributed population is crucial both in the development of statistical theory and in practice. W. Ware and J. Ferron have developed a new test statistic, modeled after the K-squared test of R. D' Agostino and E. Pearson (1973), the g-squared test statistic. This statistic has been used to estimate critical values for sample sizes up to 100, but a more extensive derivation and validation of the critical values are required, and the power of g-squared against a wide range of alternative distributions requires study. Monte Carlo simulations were performed to investigate these areas. The main advantage of g-squared is its conceptual and computational simplicity. The power study shows that g-squared is sensitive to a wide range of alternative distributions, especially peaked distributions, having absolute power for many distributions with a large "n." G-squared could be valuable for testing univariate normality in statistical routines, but it does have some weaknesses. One of its main disadvantages is its low power with small sample sizes except for peaked distributions. While g-squared can tell you about a departure from normality, it can not tell if the departure is due to a single outlier. It is recommended that when testing for departures from normality, g-squared should be used as a supplemental quantitative measure of normality to the information obtained from histograms, box plots, stem and leaf diagrams, and normality plots. (Contains 7 figures, 12 tables, and 28 references.) (SLD)

Background: Method comparison typically determines how well two methods agree. This is usually performed using the difference plot model, which measures absolute differences between two methods. This is often not applicable to data with wide ranges of absolute values. An alternative model is introduced that simplifies comparisons specifically for multiple methods compared to a gold standard.

The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and ...

When clinical data are subjected to statistical analysis, a common question is how to choose an appropriate significance test. Comparing two independent groups with observations measured on a continuous scale, the question is typically whether to choose the two-samplet test or the Wilcoxon-Mann-Whitney test (WMW test). Similar results are often obtained, but which conclusion can be drawn if significance tests give highly different P -values? The t test is optimal for normally distributed observations with common variance and robust to deviations from normality if sample sizes are not very small. The WMW test makes no distributional assumptions, but depends heavily on equal shape and variance of the two distributions (homoscedasticity). We have compared the properties of the traditional two-sample t test, a modified t test allowing unequal variance, and the WMW test by stochastic simulation. All show acceptable behaviour when the two distributions have similar variance. When variances differ, the modified t test is superior to the other two.

A weighted cepstral distance measure is proposed and is tested in a speaker-independent isolated word recognition system using standard DTW (dynamic time warping) techniques. The measure is a statistically weighted distance measure with weights equal to the inverse variance of the cepstral coefficients. The experimental results show that the weighted cepstral distance measure works substantially better than both the Euclidean cepstral distance and the log likelihood ratio distance measures across two different databases. The recognition error rate obtained using the weighted cepstral distance measure was about 1 percent for digit recognition. This result was less than onefourth of that obtained using the simple Euclidean cepstral distance measure and about one-third of the results using the log likelihood ratio distance measure. The most significant performance characteristic of the weighted cepstral distance was that it tended to equalize the performance of the recognizer across different talkers.

d e n t a l m a t e r i a l s 2 6 ( 2 0 1 0 ) 83-90 a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / d e m a A critical view on biaxial and short-beam uniaxial flexural strength tests applied to resin composites using Weibull, fractographic and finite element analyses Composite resin Flexural strength Weibull analysis Fractography Finite element analysis a b s t r a c t Objective. To evaluate the biaxial and short-beam uniaxial strength tests applied to resin composites based upon their Weibull parameters, fractographic features and stress distribution.

This paper provides a method for approzimating the probability distributions of stationary statistics in FIFO single server queues. The method is based on the Wiener Hopf factorization technique, and is applied to semi-Markov queues where the underlying state space is of unlimited size. A particularly important case is a model for a statistical multiplezer where the net input process forms a stationary ergodic Gaussian discretetime stochastic process.

This paper reviews graphical and nongraphical procedures for evaluating multivariate normality by guiding the reader through univariate and oivariate procedures that are necessary, but insufficient, indications 0± a multivariate normal distribution. A data set using three dependent variables for two groups provided by D. George and P. Mallery (1999) is used to analyze histograms, stem-and-leaf plots, box-and-whisker plots, kurtosis and skewness coefficients, Q-Q plots, the Shapiro-Wilk or Kolmogorov-Smirnov statistic, and bivariate scatterplots. A procedure programmed by B. Thompson

An increasing number of methods are being developed for the early detection of infectious disease outbreaks which could be naturally occurring or as a result of bioterrorism; however, no standardised framework for examining the usefulness of various outbreak detection methods exists. To promote comparability between studies, it is essential that standardised methods are developed for the evaluation of outbreak detection methods. This analysis aims to review approaches used to evaluate outbreak detection methods and provide a conceptual framework upon which recommendations for standardised evaluation methods can be based. We reviewed the recently published literature for reports which evaluated methods for the detection of infectious disease outbreaks in public health surveillance data. Evaluation methods identified in the recent literature were categorised according to the presence of common features to provide a conceptual basis within which to understand current approaches to eval...

BitTorrent is the most successful open Internet application for content distribution. Despite its importance, both in terms of its footprint in the Internet and the influence it has on emerging P2P applications, the BitTorrent Ecosystem is only partially understood. We seek to provide a nearly complete picture of the entire public BitTorrent Ecosystem. To this end, we crawl five of the most popular torrent-discovery sites over a nine-month period, identifying all of 4.6 million torrents and 38,996 trackers that the sites reference. We also develop a high-performance tracker crawler, and over a narrow window of twelve hours, crawl essentially all of the public Ecosystem's trackers, obtaining peer lists for all referenced torrents. Complementing the torrentdiscovery site and tracker crawling, we further crawl Azureus and Mainline DHTs for a random sample of torrents. Our resulting measurement data is more than an order of magnitude larger (in terms of number of torrents, trackers, or peers) than any earlier study. Using this extensive data set, we study in-depth the Ecosystem's torrent-discovery, tracker, peer, user behavior, and content landscapes. For peer statistics, the analysis is based on one typical snapshot obtained over twelve hours. We further analyze the fragility of the Ecosystem upon the removal of its most important tracker service.

Using the recently developed single molecule force-clamp technique we quantitatively measure the kinetics of conformational changes of polyprotein molecules at a constant force. In response to an applied force of 110 pN, we measure the dwell times of 1647 unfolding events of individual ubiquitin modules within each protein chain. We then establish a rigorous method for analyzing force-clamp data using order statistics. This allows us to test the success of an history independent, two-state model in describing the kinetics of the unfolding process. We find that the average unfolding trajectory is independent of the number of protein modules N in each trajectory, which varies between 3 and 12 (the engineered protein length), suggesting that the unfolding events in each chain are uncorrelated. We then derive a binomial distribution of dwell times to describe the stochastic dynamics of protein unfolding. This distribution successfully describes 81 % of the data with a single rate consta...

Data from 1155 Czech women (493 using oral contraception, 662 non-users), obtained from the Czech National Survey of Sexual Behavior, were used to investigate evolutionary-based hypotheses concerning the predictive value of current oral contraceptive (OC) use on extra-pair and dyadic (in-pair) sexual behavior of coupled women. Specifically, the aim was to determine whether current OC use was associated with lower extra-pair and higher in-pair sexual interest and behavior, because OC use suppresses cyclical shifts in mating psychology that occur in normally cycling women. Zero-inflated Poisson (ZIP) regression and negative binomial models were used to test associations between OC use and these sexual measures, controlling for other relevant predictors (e.g., age, parity, in-pair sexual satisfaction, relationship length). The overall incidence of having had an extra-pair partner or one-night stand in the previous year was not related to current OC use (the majority of the sample had not). However, among the women who had engaged in extra-pair sexual behavior, OC users had fewer one-night stands than non-users, and tended to have fewer partners, than non-users. OC users also had more frequent dyadic intercourse than non-users, potentially indicating higher commitment to their current relationship. These results suggest that suppression of fertility through OC use may alter important aspects of female sexual behavior, with potential implications for relationship functioning and stability.

A critique of Hankins, M: 'How discriminating are discriminative instruments?' Health and Quality of Life Outcomes 2008, 6:36.

In this contribution, we employ the Mellin transform to derive the expressions for probability density function (PDF) of the product of Rayleigh and Nakagami-m distributed random variables. We exploit the fact that the Mellin transform of a product of independent and identically distributed random variables is the product of the Mellin transforms of the individual random variables. Using this approach, the PDF of the product of random variables is expressed in the form of an easily computable infinite series.

Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.

Despite the widespread use of interrater agreement statistics for multilevel modeling and other types of research, the existing guidelines for inferring the statistical significance of interrater agreement are quite limited. They are largely relevant only under conditions that numerous researchers have argued rarely exist. Here we address this problem by generating guidelines for inferring statistical significance under a number of conditions via a computer simulation. As a set, these guidelines cover many of the conditions researchers commonly face. We discuss how researchers can use the guidelines presented to more reasonably infer the statistical significance of interrater agreement relative to using the limited guidelines available in the extant literature.

Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.

We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact model-averaged posterior distribution. This approach automatically identifies subsets of predictors having non-zero fixed effect coefficients or non-zero random effects variance, while allowing uncertainty in the model selection process. Default priors are proposed for the variance components and an efficient parameter expansion Gibbs sampler is developed for posterior computation. The approach is illustrated using simulated data and an epidemiologic example.

This paper considers the following questions: (1) what is the relationship between the method of paired comparisons and Rasch measurement theory? what is the relationship between the method of paired comparisons and graph theory? and what can graph theory contribute to the understanding of Rasch measurement theory? It is specifically shown how the method of paired comparisons can lead to the Rasch model, just as consideration of the Rasch model can lead to a pairwise algorithm for estimating the parameters of the Rasch model. Furthermore, both graph theory and previously unexplored aspects of the method of paired comparisons are used to increase understanding and utility of a pairwise algorithm for estimating parameters of the Rasch model as presented by B. Choppin (1985). Bringing together these three lines of inquiry enhances understanding of the Rasch model and provides more effective means of analysis. Appendixes present the conversion of the cumulative distribution LunccionaaggiStatistical Analysis System routines for the analysis. (Contains 8 figures, 7 and 66 references.) (Author/SLD) Abstract The purpose of this paper is to answer the following questions: (a) What is the relationship between the method of paired comparisons and Rasch measurement theory? (b) What is the relationship between the method of paired comparisons and graph theory? (c) What can graph theory contribute to our understanding of Rasch measurement theory? Specifically, it is shown how the method of paired comparisons can lead to the Rasch model, just as consideration of the Rasch model can lead to a pairwise algorithm for estimating the parameters of the Rasch model. Furthermore, both graph theory and previously unexplored aspects of the method of paired comparisons are used to increase understanding and utility of a pairwise algorithm for estimating parameters of the Rasch model as presented by . Bringing together these three lines of inquiry enhances our understanding of the Rasch model, as well as provides more effective means of analyzing the Rasch model.

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.