Energy Technology Data Exchange (ETDEWEB)
Suh, M. Y.; Jee, K. Y.; Park, K. K.; Park, Y. J.; Kim, W. H
1999-08-01
This report is intended to describe the statistical methods necessary to design and conduct radiation counting experiments and evaluate the data from the experiment. The methods are described for the evaluation of the stability of a counting system and the estimation of the precision of counting data by application of probability distribution models. The methods for the determination of the uncertainty of the results calculated from the number of counts, as well as various statistical methods for the reduction of counting error are also described. (Author). 11 refs., 8 tabs., 8 figs.
Energy Technology Data Exchange (ETDEWEB)
Suh, M. Y.; Jee, K. Y.; Park, K. K. [Korea Atomic Energy Research Institute, Taejon (Korea)
1999-08-01
This report is intended to describe the statistical methods necessary to design and conduct radiation counting experiments and evaluate the data from the experiments. The methods are described for the evaluation of the stability of a counting system and the estimation of the precision of counting data by application of probability distribution models. The methods for the determination of the uncertainty of the results calculated from the number of counts, as well as various statistical methods for the reduction of counting error are also described. 11 refs., 6 figs., 8 tabs. (Author)
International Nuclear Information System (INIS)
Suh, M. Y.; Jee, K. Y.; Park, K. K.; Park, Y. J.; Kim, W. H.
1999-08-01
This report is intended to describe the statistical methods necessary to design and conduct radiation counting experiments and evaluate the data from the experiment. The methods are described for the evaluation of the stability of a counting system and the estimation of the precision of counting data by application of probability distribution models. The methods for the determination of the uncertainty of the results calculated from the number of counts, as well as various statistical methods for the reduction of counting error are also described. (Author). 11 refs., 8 tabs., 8 figs
Non-Poisson counting statistics of a hybrid G-M counter dead time model
International Nuclear Information System (INIS)
Lee, Sang Hoon; Jae, Moosung; Gardner, Robin P.
2007-01-01
The counting statistics of a G-M counter with a considerable dead time event rate deviates from Poisson statistics. Important characteristics such as observed counting rates as a function true counting rates, variances and interval distributions were analyzed for three dead time models, non-paralyzable, paralyzable and hybrid, with the help of GMSIM, a Monte Carlo dead time effect simulator. The simulation results showed good agreements with the models in observed counting rates and variances. It was found through GMSIM simulations that the interval distribution for the hybrid model showed three distinctive regions, a complete cutoff region for the duration of the total dead time, a degraded exponential and an enhanced exponential regions. By measuring the cutoff and the duration of degraded exponential from the pulse interval distribution, it is possible to evaluate the two dead times in the hybrid model
Experimental investigation of statistical models describing distribution of counts
International Nuclear Information System (INIS)
Salma, I.; Zemplen-Papp, E.
1992-01-01
The binomial, Poisson and modified Poisson models which are used for describing the statistical nature of the distribution of counts are compared theoretically, and conclusions for application are considered. The validity of the Poisson and the modified Poisson statistical distribution for observing k events in a short time interval is investigated experimentally for various measuring times. The experiments to measure the influence of the significant radioactive decay were performed with 89 Y m (T 1/2 =16.06 s), using a multichannel analyser (4096 channels) in the multiscaling mode. According to the results, Poisson statistics describe the counting experiment for short measuring times (up to T=0.5T 1/2 ) and its application is recommended. However, analysis of the data demonstrated, with confidence, that for long measurements (T≥T 1/2 ) Poisson distribution is not valid and the modified Poisson function is preferable. The practical implications in calculating uncertainties and in optimizing the measuring time are discussed. Differences between the standard deviations evaluated on the basis of the Poisson and binomial models are especially significant for experiments with long measuring time (T/T 1/2 ≥2) and/or large detection efficiency (ε>0.30). Optimization of the measuring time for paired observations yields the same solution for either the binomial or the Poisson distribution. (orig.)
Counting statistics in radioactivity measurements
International Nuclear Information System (INIS)
Martin, J.
1975-01-01
The application of statistical methods to radioactivity measurement problems is analyzed in several chapters devoted successively to: the statistical nature of radioactivity counts; the application to radioactive counting of two theoretical probability distributions, Poisson's distribution law and the Laplace-Gauss law; true counting laws; corrections related to the nature of the apparatus; statistical techniques in gamma spectrometry [fr
Sileshi, G
2006-10-01
Researchers and regulatory agencies often make statistical inferences from insect count data using modelling approaches that assume homogeneous variance. Such models do not allow for formal appraisal of variability which in its different forms is the subject of interest in ecology. Therefore, the objectives of this paper were to (i) compare models suitable for handling variance heterogeneity and (ii) select optimal models to ensure valid statistical inferences from insect count data. The log-normal, standard Poisson, Poisson corrected for overdispersion, zero-inflated Poisson, the negative binomial distribution and zero-inflated negative binomial models were compared using six count datasets on foliage-dwelling insects and five families of soil-dwelling insects. Akaike's and Schwarz Bayesian information criteria were used for comparing the various models. Over 50% of the counts were zeros even in locally abundant species such as Ootheca bennigseni Weise, Mesoplatys ochroptera Stål and Diaecoderus spp. The Poisson model after correction for overdispersion and the standard negative binomial distribution model provided better description of the probability distribution of seven out of the 11 insects than the log-normal, standard Poisson, zero-inflated Poisson or zero-inflated negative binomial models. It is concluded that excess zeros and variance heterogeneity are common data phenomena in insect counts. If not properly modelled, these properties can invalidate the normal distribution assumptions resulting in biased estimation of ecological effects and jeopardizing the integrity of the scientific inferences. Therefore, it is recommended that statistical models appropriate for handling these data properties be selected using objective criteria to ensure efficient statistical inference.
Statistical Methods for Unusual Count Data
DEFF Research Database (Denmark)
Guthrie, Katherine A.; Gammill, Hilary S.; Kamper-Jørgensen, Mads
2016-01-01
microchimerism data present challenges for statistical analysis, including a skewed distribution, excess zero values, and occasional large values. Methods for comparing microchimerism levels across groups while controlling for covariates are not well established. We compared statistical models for quantitative...... microchimerism values, applied to simulated data sets and 2 observed data sets, to make recommendations for analytic practice. Modeling the level of quantitative microchimerism as a rate via Poisson or negative binomial model with the rate of detection defined as a count of microchimerism genome equivalents per...
Statistical tests to compare motif count exceptionalities
Directory of Open Access Journals (Sweden)
Vandewalle Vincent
2007-03-01
Full Text Available Abstract Background Finding over- or under-represented motifs in biological sequences is now a common task in genomics. Thanks to p-value calculation for motif counts, exceptional motifs are identified and represent candidate functional motifs. The present work addresses the related question of comparing the exceptionality of one motif in two different sequences. Just comparing the motif count p-values in each sequence is indeed not sufficient to decide if this motif is significantly more exceptional in one sequence compared to the other one. A statistical test is required. Results We develop and analyze two statistical tests, an exact binomial one and an asymptotic likelihood ratio test, to decide whether the exceptionality of a given motif is equivalent or significantly different in two sequences of interest. For that purpose, motif occurrences are modeled by Poisson processes, with a special care for overlapping motifs. Both tests can take the sequence compositions into account. As an illustration, we compare the octamer exceptionalities in the Escherichia coli K-12 backbone versus variable strain-specific loops. Conclusion The exact binomial test is particularly adapted for small counts. For large counts, we advise to use the likelihood ratio test which is asymptotic but strongly correlated with the exact binomial test and very simple to use.
Humeniuk, Stephan; Büchler, Hans Peter
2017-12-08
We present a method for computing the full probability distribution function of quadratic observables such as particle number or magnetization for the Fermi-Hubbard model within the framework of determinantal quantum Monte Carlo calculations. Especially in cold atom experiments with single-site resolution, such a full counting statistics can be obtained from repeated projective measurements. We demonstrate that the full counting statistics can provide important information on the size of preformed pairs. Furthermore, we compute the full counting statistics of the staggered magnetization in the repulsive Hubbard model at half filling and find excellent agreement with recent experimental results. We show that current experiments are capable of probing the difference between the Hubbard model and the limiting Heisenberg model.
DEFF Research Database (Denmark)
Nielsen, Martin Krarup; Vidyashankar, Anand N.; Hanlon, Bret
statistical model was therefore developed for analysis of FECRT data from multiple farms. Horse age, gender, zip code and pre-treatment egg count were incorporated into the model. Horses and farms were kept as random effects. Resistance classifications were based on model-based 95% lower confidence limit (LCL...
Statistical method for resolving the photon-photoelectron-counting inversion problem
International Nuclear Information System (INIS)
Wu Jinlong; Li Tiejun; Peng, Xiang; Guo Hong
2011-01-01
A statistical inversion method is proposed for the photon-photoelectron-counting statistics in quantum key distribution experiment. With the statistical viewpoint, this problem is equivalent to the parameter estimation for an infinite binomial mixture model. The coarse-graining idea and Bayesian methods are applied to deal with this ill-posed problem, which is a good simple example to show the successful application of the statistical methods to the inverse problem. Numerical results show the applicability of the proposed strategy. The coarse-graining idea for the infinite mixture models should be general to be used in the future.
Hybrid statistics-simulations based method for atom-counting from ADF STEM images
Energy Technology Data Exchange (ETDEWEB)
De wael, Annelies, E-mail: annelies.dewael@uantwerpen.be [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium); De Backer, Annick [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium); Jones, Lewys; Nellist, Peter D. [Department of Materials, University of Oxford, Parks Road, OX1 3PH Oxford (United Kingdom); Van Aert, Sandra, E-mail: sandra.vanaert@uantwerpen.be [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium)
2017-06-15
A hybrid statistics-simulations based method for atom-counting from annular dark field scanning transmission electron microscopy (ADF STEM) images of monotype crystalline nanostructures is presented. Different atom-counting methods already exist for model-like systems. However, the increasing relevance of radiation damage in the study of nanostructures demands a method that allows atom-counting from low dose images with a low signal-to-noise ratio. Therefore, the hybrid method directly includes prior knowledge from image simulations into the existing statistics-based method for atom-counting, and accounts in this manner for possible discrepancies between actual and simulated experimental conditions. It is shown by means of simulations and experiments that this hybrid method outperforms the statistics-based method, especially for low electron doses and small nanoparticles. The analysis of a simulated low dose image of a small nanoparticle suggests that this method allows for far more reliable quantitative analysis of beam-sensitive materials. - Highlights: • A hybrid method for atom-counting from ADF STEM images is introduced. • Image simulations are incorporated into a statistical framework in a reliable manner. • Limits of the existing methods for atom-counting are far exceeded. • Reliable counting results from an experimental low dose image are obtained. • Progress towards reliable quantitative analysis of beam-sensitive materials is made.
Hybrid statistics-simulations based method for atom-counting from ADF STEM images.
De Wael, Annelies; De Backer, Annick; Jones, Lewys; Nellist, Peter D; Van Aert, Sandra
2017-06-01
A hybrid statistics-simulations based method for atom-counting from annular dark field scanning transmission electron microscopy (ADF STEM) images of monotype crystalline nanostructures is presented. Different atom-counting methods already exist for model-like systems. However, the increasing relevance of radiation damage in the study of nanostructures demands a method that allows atom-counting from low dose images with a low signal-to-noise ratio. Therefore, the hybrid method directly includes prior knowledge from image simulations into the existing statistics-based method for atom-counting, and accounts in this manner for possible discrepancies between actual and simulated experimental conditions. It is shown by means of simulations and experiments that this hybrid method outperforms the statistics-based method, especially for low electron doses and small nanoparticles. The analysis of a simulated low dose image of a small nanoparticle suggests that this method allows for far more reliable quantitative analysis of beam-sensitive materials. Copyright © 2017 Elsevier B.V. All rights reserved.
Unifying quantum heat transfer in a nonequilibrium spin-boson model with full counting statistics
Wang, Chen; Ren, Jie; Cao, Jianshu
2017-02-01
To study the full counting statistics of quantum heat transfer in a driven nonequilibrium spin-boson model, we develop a generalized nonequilibrium polaron-transformed Redfield equation with an auxiliary counting field. This enables us to study the impact of qubit-bath coupling ranging from weak to strong regimes. Without external modulations, we observe maximal values of both steady-state heat flux and noise power in moderate coupling regimes, below which we find that these two transport quantities are enhanced by the finite-qubit-energy bias. With external modulations, the geometric-phase-induced heat flux shows a monotonic decrease upon increasing the qubit-bath coupling at zero qubit energy bias (without bias). While under the finite-qubit-energy bias (with bias), the geometric-phase-induced heat flux exhibits an interesting reversal behavior in the strong coupling regime. Our results unify the seemingly contradictory results in weak and strong qubit-bath coupling regimes and provide detailed dissections for the quantum fluctuation of nonequilibrium heat transfer.
De Backer, A; Martinez, G T; Rosenauer, A; Van Aert, S
2013-11-01
In the present paper, a statistical model-based method to count the number of atoms of monotype crystalline nanostructures from high resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images is discussed in detail together with a thorough study on the possibilities and inherent limitations. In order to count the number of atoms, it is assumed that the total scattered intensity scales with the number of atoms per atom column. These intensities are quantitatively determined using model-based statistical parameter estimation theory. The distribution describing the probability that intensity values are generated by atomic columns containing a specific number of atoms is inferred on the basis of the experimental scattered intensities. Finally, the number of atoms per atom column is quantified using this estimated probability distribution. The number of atom columns available in the observed STEM image, the number of components in the estimated probability distribution, the width of the components of the probability distribution, and the typical shape of a criterion to assess the number of components in the probability distribution directly affect the accuracy and precision with which the number of atoms in a particular atom column can be estimated. It is shown that single atom sensitivity is feasible taking the latter aspects into consideration. © 2013 Elsevier B.V. All rights reserved.
Statistical data filtration in neutron coincidence counting
International Nuclear Information System (INIS)
Beddingfield, D.H.; Menlove, H.O.
1992-11-01
We assessed the effectiveness of statistical data filtration to minimize the contribution of matrix materials in 200-ell drums to the nondestructive assay of plutonium. Those matrices were examined: polyethylene, concrete, aluminum, iron, cadmium, and lead. Statistical filtration of neutron coincidence data improved the low-end sensitivity of coincidence counters. Spurious data arising from electrical noise, matrix spallation, and geometric effects were smoothed in a predictable fashion by the statistical filter. The filter effectively lowers the minimum detectable mass limit that can be achieved for plutonium assay using passive neutron coincidence counting
Theory of overdispersion in counting statistics caused by fluctuating probabilities
International Nuclear Information System (INIS)
Semkow, Thomas M.
1999-01-01
It is shown that the random Lexis fluctuations of probabilities such as probability of decay or detection cause the counting statistics to be overdispersed with respect to the classical binomial, Poisson, or Gaussian distributions. The generating and the distribution functions for the overdispersed counting statistics are derived. Applications to radioactive decay with detection and more complex experiments are given, as well as distinguishing between the source and background, in the presence of overdispersion. Monte-Carlo verifications are provided
Counting statistics and loss corrections for the APS
International Nuclear Information System (INIS)
Lee, W.K.; Mills, D.M.
1992-01-01
It has been suggested that for timing experiments, it might be advantageous to arrange the bunches in the storage ring in an asymmetrical mode. In this paper, we determine the counting losses from pulsed x-ray sources from basic probabilistic arguments and from Poisson statistics. In particular the impact on single photon counting losses of a variety of possible filling modes for the Advanced Photon Source (APS) is examined. For bunches of equal current, a loss of 10% occurs whenever the count rate exceeds 21% of the bunch repetition rate. This changes slightly when bunches containing unequal numbers of particles are considered. The results are applied to several common detector/electronics systems
Counting statistics and loss corrections for the APS
International Nuclear Information System (INIS)
Lee, W.K.; Mills, D.M.
1992-01-01
It has been suggested that for timing experiments, it might be advantageous to arrange the bunches in the storage ring in an asymmetrical mode. In this paper, we determine the counting losses from pulsed x-ray sources from basic probabilistic arguments and from Poisson statistics. In particular the impact on single-photon counting losses of a variety of possible filling modes for the Advanced Photon Source (APS) is examined. For bunches of equal current, a loss of 10% occurs whenever the count rate exceeds 21% of the bunch repetition rate. This changes slightly when bunches containing unequal numbers of particles are considered. The results are applied to several common detector/electronics systems
Counting statistics of many-particle quantum walks
Mayer, Klaus; Tichy, Malte C.; Mintert, Florian; Konrad, Thomas; Buchleitner, Andreas
2011-06-01
We study quantum walks of many noninteracting particles on a beam splitter array as a paradigmatic testing ground for the competition of single- and many-particle interference in a multimode system. We derive a general expression for multimode particle-number correlation functions, valid for bosons and fermions, and infer pronounced signatures of many-particle interferences in the counting statistics.
Counting statistics of many-particle quantum walks
International Nuclear Information System (INIS)
Mayer, Klaus; Tichy, Malte C.; Buchleitner, Andreas; Mintert, Florian; Konrad, Thomas
2011-01-01
We study quantum walks of many noninteracting particles on a beam splitter array as a paradigmatic testing ground for the competition of single- and many-particle interference in a multimode system. We derive a general expression for multimode particle-number correlation functions, valid for bosons and fermions, and infer pronounced signatures of many-particle interferences in the counting statistics.
Modelling the Covariance Structure in Marginal Multivariate Count Models
DEFF Research Database (Denmark)
Bonat, W. H.; Olivero, J.; Grande-Vega, M.
2017-01-01
The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined...... be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited...... with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson...
International Nuclear Information System (INIS)
Samuelsson, P.
2007-01-01
We present a theory for the full distribution of current fluctuations in incoherent diffusive superconducting junctions, subjected to a voltage bias. This theory of full counting statistics of incoherent multiple Andreev reflections is valid for an arbitrary applied voltage. We present a detailed discussion of the properties of the first four cumulants as well as the low and high voltage regimes of the full counting statistics. (orig.)
Sakhr, Jamal; Nieminen, John M.
2018-03-01
Two decades ago, Wang and Ong, [Phys. Rev. A 55, 1522 (1997)], 10.1103/PhysRevA.55.1522 hypothesized that the local box-counting dimension of a discrete quantum spectrum should depend exclusively on the nearest-neighbor spacing distribution (NNSD) of the spectrum. In this Rapid Communication, we validate their hypothesis by deriving an explicit formula for the local box-counting dimension of a countably-infinite discrete quantum spectrum. This formula expresses the local box-counting dimension of a spectrum in terms of single and double integrals of the NNSD of the spectrum. As applications, we derive an analytical formula for Poisson spectra and closed-form approximations to the local box-counting dimension for spectra having Gaussian orthogonal ensemble (GOE), Gaussian unitary ensemble (GUE), and Gaussian symplectic ensemble (GSE) spacing statistics. In the Poisson and GOE cases, we compare our theoretical formulas with the published numerical data of Wang and Ong and observe excellent agreement between their data and our theory. We also study numerically the local box-counting dimensions of the Riemann zeta function zeros and the alternate levels of GOE spectra, which are often used as numerical models of spectra possessing GUE and GSE spacing statistics, respectively. In each case, the corresponding theoretical formula is found to accurately describe the numerically computed local box-counting dimension.
Statistical Methods for Unusual Count Data: Examples From Studies of Microchimerism
Guthrie, Katherine A.; Gammill, Hilary S.; Kamper-Jørgensen, Mads; Tjønneland, Anne; Gadi, Vijayakrishna K.; Nelson, J. Lee; Leisenring, Wendy
2016-01-01
Natural acquisition of small amounts of foreign cells or DNA, referred to as microchimerism, occurs primarily through maternal-fetal exchange during pregnancy. Microchimerism can persist long-term and has been associated with both beneficial and adverse human health outcomes. Quantitative microchimerism data present challenges for statistical analysis, including a skewed distribution, excess zero values, and occasional large values. Methods for comparing microchimerism levels across groups while controlling for covariates are not well established. We compared statistical models for quantitative microchimerism values, applied to simulated data sets and 2 observed data sets, to make recommendations for analytic practice. Modeling the level of quantitative microchimerism as a rate via Poisson or negative binomial model with the rate of detection defined as a count of microchimerism genome equivalents per total cell equivalents tested utilizes all available data and facilitates a comparison of rates between groups. We found that both the marginalized zero-inflated Poisson model and the negative binomial model can provide unbiased and consistent estimates of the overall association of exposure or study group with microchimerism detection rates. The negative binomial model remains the more accessible of these 2 approaches; thus, we conclude that the negative binomial model may be most appropriate for analyzing quantitative microchimerism data. PMID:27769989
Analysis of dental caries using generalized linear and count regression models
Directory of Open Access Journals (Sweden)
Javali M. Phil
2013-11-01
Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.
International Nuclear Information System (INIS)
Kawano, Takao
2014-01-01
It is known that radiation is detected at random and the radiation counts fluctuate statistically. In the present study, a radiation measurement experiment was performed to understand the randomness and statistical fluctuation of radiation counts. In the measurement, three natural radiation sources were used. The sources were fabricated from potassium chloride chemicals, chemical fertilizers and kelps. These materials contain naturally occurring potassium-40 that is a radionuclide. From high schools, junior high schools and elementary schools, nine teachers participated to the radiation measurement experiment. Each participant measured the 1-min integration counts of radiation five times using GM survey meters, and 45 sets of data were obtained for the respective natural radiation sources. It was found that the frequency of occurrence of radiation counts was distributed according to a Gaussian distribution curve, although the obtained 45 data sets of radiation counts superficially looked to be fluctuating meaninglessly. (author)
Comparing distribution models for small samples of overdispersed counts of freshwater fish
Vaudor, Lise; Lamouroux, Nicolas; Olivier, Jean-Michel
2011-05-01
The study of species abundance often relies on repeated abundance counts whose number is limited by logistic or financial constraints. The distribution of abundance counts is generally right-skewed (i.e. with many zeros and few high values) and needs to be modelled for statistical inference. We used an extensive dataset involving about 100,000 fish individuals of 12 freshwater fish species collected in electrofishing points (7 m 2) during 350 field surveys made in 25 stream sites, in order to compare the performance and the generality of four distribution models of counts (Poisson, negative binomial and their zero-inflated counterparts). The negative binomial distribution was the best model (Bayesian Information Criterion) for 58% of the samples (species-survey combinations) and was suitable for a variety of life histories, habitat, and sample characteristics. The performance of the models was closely related to samples' statistics such as total abundance and variance. Finally, we illustrated the consequences of a distribution assumption by calculating confidence intervals around the mean abundance, either based on the most suitable distribution assumption or on an asymptotical, distribution-free (Student's) method. Student's method generally corresponded to narrower confidence intervals, especially when there were few (≤3) non-null counts in the samples.
Counting statistics in low level radioactivity measurements fluctuating counting efficiency
International Nuclear Information System (INIS)
Pazdur, M.F.
1976-01-01
A divergence between the probability distribution of the number of nuclear disintegrations and the number of observed counts, caused by counting efficiency fluctuation, is discussed. The negative binominal distribution is proposed to describe the probability distribution of the number of counts, instead of Poisson distribution, which is assumed to hold for the number of nuclear disintegrations only. From actual measurements the r.m.s. amplitude of counting efficiency fluctuation is estimated. Some consequences of counting efficiency fluctuation are investigated and the corresponding formulae are derived: (1) for detection limit as a function of the number of partial measurements and the relative amplitude of counting efficiency fluctuation, and (2) for optimum allocation of the number of partial measurements between sample and background. (author)
The statistical interpretations of counting data from measurements of low-level radioactivity
International Nuclear Information System (INIS)
Donn, J.J.; Wolke, R.L.
1977-01-01
The statistical model appropriate to measurements of low-level or background-dominant radioactivity is examined and the derived relationships are applied to two practical problems involving hypothesis testing: 'Does the sample exhibit a net activity above background' and 'Is the activity of the sample below some preselected limit'. In each of these cases, the appropriate decision rule is formulated, procedures are developed for estimating the preset count which is necessary to achieve a desired probability of detection, and a specific sequence of operations is provided for the worker in the field. (author)
International Nuclear Information System (INIS)
Lopez de la Cruz, J.; Gutierrez, M.A.
2008-01-01
This paper presents a stochastic analysis of spatial point patterns as effect of localized pitting corrosion. The Quadrat Counts method is studied with two empirical pit patterns. The results are dependent on the quadrat size and bias is introduced when empty quadrats are accounted for the analysis. The spatially inhomogeneous Poisson process is used to improve the performance of the Quadrat Counts method. The latter combines Quadrat Counts with distance-based statistics in the analysis of pit patterns. The Inter-Event and the Nearest-Neighbour statistics are here implemented in order to compare their results. Further, the treatment of patterns in irregular domains is discussed
Counting in Lattices: Combinatorial Problems from Statistical Mechanics.
Randall, Dana Jill
In this thesis we consider two classical combinatorial problems arising in statistical mechanics: counting matchings and self-avoiding walks in lattice graphs. The first problem arises in the study of the thermodynamical properties of monomers and dimers (diatomic molecules) in crystals. Fisher, Kasteleyn and Temperley discovered an elegant technique to exactly count the number of perfect matchings in two dimensional lattices, but it is not applicable for matchings of arbitrary size, or in higher dimensional lattices. We present the first efficient approximation algorithm for computing the number of matchings of any size in any periodic lattice in arbitrary dimension. The algorithm is based on Monte Carlo simulation of a suitable Markov chain and has rigorously derived performance guarantees that do not rely on any assumptions. In addition, we show that these results generalize to counting matchings in any graph which is the Cayley graph of a finite group. The second problem is counting self-avoiding walks in lattices. This problem arises in the study of the thermodynamics of long polymer chains in dilute solution. While there are a number of Monte Carlo algorithms used to count self -avoiding walks in practice, these are heuristic and their correctness relies on unproven conjectures. In contrast, we present an efficient algorithm which relies on a single, widely-believed conjecture that is simpler than preceding assumptions and, more importantly, is one which the algorithm itself can test. Thus our algorithm is reliable, in the sense that it either outputs answers that are guaranteed, with high probability, to be correct, or finds a counterexample to the conjecture. In either case we know we can trust our results and the algorithm is guaranteed to run in polynomial time. This is the first algorithm for counting self-avoiding walks in which the error bounds are rigorously controlled. This work was supported in part by an AT&T graduate fellowship, a University of
Regression models for categorical, count, and related variables an applied approach
Hoffmann, John P
2016-01-01
Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapte...
Full counting statistics of a charge pump in the Coulomb blockade regime
Andreev, A. V.; Mishchenko, E. G.
2001-12-01
We study full charge counting statistics (FCCS) of a charge pump based on a nearly open single electron transistor. The problem is mapped onto an exactly soluble problem of a nonequilibrium g=1/2 Luttinger liquid with an impurity. We obtain an analytic expression for the generating function of the transmitted charge for an arbitrary pumping strength. Although this model contains fractionally charged excitations only integer transmitted charges can be observed. In the weak pumping limit FCCS correspond to a Poissonian transmission of particles with charge e*=e/2 from which all events with odd numbers of transferred particles are excluded.
Reducing bias in the analysis of counting statistics data
International Nuclear Information System (INIS)
Hammersley, A.P.; Antoniadis, A.
1997-01-01
In the analysis of counting statistics data it is common practice to estimate the variance of the measured data points as the data points themselves. This practice introduces a bias into the results of further analysis which may be significant, and under certain circumstances lead to false conclusions. In the case of normal weighted least squares fitting this bias is quantified and methods to avoid it are proposed. (orig.)
International Nuclear Information System (INIS)
Buckman, S.M.
1995-03-01
The major part of the thesis is devoted to the theoretical development of a comprehensive PC-based statistical package for the analysis of data from coincidence-counting experiments. This analysis is applied to primary standardizations of Co performed in Australia and Japan. The Australian standardisation, the accuracy of which is confirmed through international comparison, is used to re-calibrate the ionisation chamber. Both Australian and Japanese coincidence-counting systems are interfaced to personal computers to enable replicated sets of measurements to be made under computer control. Further research to confirm the validity of the statistical model includes an experimental investigation into the non-Poisson behaviour of radiation detectors due to the effect of deadtime. Experimental investigation is conducted to determine which areas are most likely to limit the ultimate accuracy achievable with coincidence counting. The thesis concludes by discussing the possibilities of digital coincidence counting and outlines the design of a prototype system presently under development. The accuracy of the Australian standardisation is confirmed by international comparison. From this result a more accurate Co calibration is obtained for the Australian working standard. Based on the work of this thesis, uncertainties in coincidence counting experiments can be better handled with resulting improvements in measurement reliability. The concept and benefits of digital coincidence counting are discussed and a proposed design is given for such a system. All of the data and software associated with this thesis is provided on computer discs. 237 refs., figs., tabs
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
International Nuclear Information System (INIS)
Khayat, Omid; Afarideh, Hossein; Mohammadnia, Meisam
2015-01-01
In the solid state nuclear track detectors of chemically etched type, nuclear tracks with center-to-center neighborhood of distance shorter than two times the radius of tracks will emerge as overlapping tracks. Track overlapping in this type of detectors causes tracks count losses and it becomes rather severe in high track densities. Therefore, tracks counting in this condition should include a correction factor for count losses of different tracks overlapping orders since a number of overlapping tracks may be counted as one track. Another aspect of the problem is the cases where imaging the whole area of the detector and counting all tracks are not possible. In these conditions a statistical generalization method is desired to be applicable in counting a segmented area of the detector and the results can be generalized to the whole surface of the detector. Also there is a challenge in counting the tracks in densely overlapped tracks because not sufficient geometrical or contextual information are available. It this paper we present a statistical counting method which gives the user a relation between the tracks overlapping probabilities on a segmented area of the detector surface and the total number of tracks. To apply the proposed method one can estimate the total number of tracks on a solid state detector of arbitrary shape and dimensions by approximating the tracks averaged area, whole detector surface area and some orders of tracks overlapping probabilities. It will be shown that this method is applicable in high and ultra high density tracks images and the count loss error can be enervated using a statistical generalization approach. - Highlights: • A correction factor for count losses of different tracks overlapping orders. • For the cases imaging the whole area of the detector is not possible. • Presenting a statistical generalization method for segmented areas. • Giving a relation between the tracks overlapping probabilities and the total tracks
Radon counting statistics - a Monte Carlo investigation
International Nuclear Information System (INIS)
Scott, A.G.
1996-01-01
Radioactive decay is a Poisson process, and so the Coefficient of Variation (COV) of open-quotes nclose quotes counts of a single nuclide is usually estimated as 1/√n. This is only true if the count duration is much shorter than the half-life of the nuclide. At longer count durations, the COV is smaller than the Poisson estimate. Most radon measurement methods count the alpha decays of 222 Rn, plus the progeny 218 Po and 214 Po, and estimate the 222 Rn activity from the sum of the counts. At long count durations, the chain decay of these nuclides means that every 222 Rn decay must be followed by two other alpha decays. The total number of decays is open-quotes 3Nclose quotes, where N is the number of radon decays, and the true COV of the radon concentration estimate is 1/√(N), √3 larger than the Poisson total count estimate of 1/√3N. Most count periods are comparable to the half lives of the progeny, so the relationship between COV and count time is complex. A Monte-Carlo estimate of the ratio of true COV to Poisson estimate was carried out for a range of count periods from 1 min to 16 h and three common radon measurement methods: liquid scintillation, scintillation cell, and electrostatic precipitation of progeny. The Poisson approximation underestimates COV by less than 20% for count durations of less than 60 min
Uhlemann, C.; Feix, M.; Codis, S.; Pichon, C.; Bernardeau, F.; L'Huillier, B.; Kim, J.; Hong, S. E.; Laigle, C.; Park, C.; Shin, J.; Pogosyan, D.
2018-02-01
Starting from a very accurate model for density-in-cells statistics of dark matter based on large deviation theory, a bias model for the tracer density in spheres is formulated. It adopts a mean bias relation based on a quadratic bias model to relate the log-densities of dark matter to those of mass-weighted dark haloes in real and redshift space. The validity of the parametrized bias model is established using a parametrization-independent extraction of the bias function. This average bias model is then combined with the dark matter PDF, neglecting any scatter around it: it nevertheless yields an excellent model for densities-in-cells statistics of mass tracers that is parametrized in terms of the underlying dark matter variance and three bias parameters. The procedure is validated on measurements of both the one- and two-point statistics of subhalo densities in the state-of-the-art Horizon Run 4 simulation showing excellent agreement for measured dark matter variance and bias parameters. Finally, it is demonstrated that this formalism allows for a joint estimation of the non-linear dark matter variance and the bias parameters using solely the statistics of subhaloes. Having verified that galaxy counts in hydrodynamical simulations sampled on a scale of 10 Mpc h-1 closely resemble those of subhaloes, this work provides important steps towards making theoretical predictions for density-in-cells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST.
Modeling Zero-Inflated and Overdispersed Count Data: An Empirical Study of School Suspensions
Desjardins, Christopher David
2016-01-01
The purpose of this article is to develop a statistical model that best explains variability in the number of school days suspended. Number of school days suspended is a count variable that may be zero-inflated and overdispersed relative to a Poisson model. Four models were examined: Poisson, negative binomial, Poisson hurdle, and negative…
Application of statistical methods to the testing of nuclear counting assemblies
International Nuclear Information System (INIS)
Gilbert, J.P.; Friedling, G.
1965-01-01
This report describes the application of the hypothesis test theory to the control of the 'statistical purity' and of the stability of the counting batteries used for measurements on activation detectors in research reactors. The principles involved and the experimental results obtained at Cadarache on batteries operating with the reactors PEGGY and AZUR are given. (authors) [fr
Optimization of statistical methods for HpGe gamma-ray spectrometer used in wide count rate ranges
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Gervino, G., E-mail: gervino@to.infn.it [UNITO - Università di Torino, Dipartimento di Fisica, Turin (Italy); INFN - Istituto Nazionale di Fisica Nucleare, Sez. Torino, Turin (Italy); Mana, G. [INRIM - Istituto Nazionale di Ricerca Metrologica, Turin (Italy); Palmisano, C. [UNITO - Università di Torino, Dipartimento di Fisica, Turin (Italy); INRIM - Istituto Nazionale di Ricerca Metrologica, Turin (Italy)
2016-07-11
The need to perform γ-ray measurements with HpGe detectors is a common technique in many fields such as nuclear physics, radiochemistry, nuclear medicine and neutron activation analysis. The use of HpGe detectors is chosen in situations where isotope identification is needed because of their excellent resolution. Our challenge is to obtain the “best” spectroscopy data possible in every measurement situation. “Best” is a combination of statistical (number of counts) and spectral quality (peak, width and position) over a wide range of counting rates. In this framework, we applied Bayesian methods and the Ellipsoidal Nested Sampling (a multidimensional integration technique) to study the most likely distribution for the shape of HpGe spectra. In treating these experiments, the prior information suggests to model the likelihood function with a product of Poisson distributions. We present the efforts that have been done in order to optimize the statistical methods to HpGe detector outputs with the aim to evaluate to a better order of precision the detector efficiency, the absolute measured activity and the spectra background. Reaching a more precise knowledge of statistical and systematic uncertainties for the measured physical observables is the final goal of this research project.
PREFACE: Counting Complexity: An international workshop on statistical mechanics and combinatorics
de Gier, Jan; Warnaar, Ole
2006-07-01
On 10-15 July 2005 the conference `Counting Complexity: An international workshop on statistical mechanics and combinatorics' was held on Dunk Island, Queensland, Australia in celebration of Tony Guttmann's 60th birthday. Dunk Island provided the perfect setting for engaging in almost all of Tony's life-long passions: swimming, running, food, wine and, of course, plenty of mathematics and physics. The conference was attended by many of Tony's close scientific friends from all over the world, and most talks were presented by his past and present collaborators. This volume contains the proceedings of the meeting and consists of 24 refereed research papers in the fields of statistical mechanics, condensed matter physics and combinatorics. These papers provide an excellent illustration of the breadth and scope of Tony's work. The very first contribution, written by Stu Whittington, contains an overview of the many scientific achievements of Tony over the past 40 years in mathematics and physics. The organizing committee, consisting of Richard Brak, Aleks Owczarek, Jan de Gier, Emma Lockwood, Andrew Rechnitzer and Ole Warnaar, gratefully acknowledges the Australian Mathematical Society (AustMS), the Australian Mathematical Sciences Institute (AMSI), the ARC Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS), the ARC Complex Open Systems Research Network (COSNet), the Institute of Physics (IOP) and the Department of Mathematics and Statistics of The University of Melbourne for financial support in organizing the conference. Tony, we hope that your future years in mathematics will be numerous. Count yourself lucky! Tony Guttman
Gaussian point count statistics for families of curves over a fixed finite field
Kurlberg, Par; Wigman, Igor
2010-01-01
We produce a collection of families of curves, whose point count statistics over F_p becomes Gaussian for p fixed. In particular, the average number of F_p points on curves in these families tends to infinity.
Farnsworth, G.L.; Nichols, J.D.; Sauer, J.R.; Fancy, S.G.; Pollock, K.H.; Shriner, S.A.; Simons, T.R.; Ralph, C. John; Rich, Terrell D.
2005-01-01
Point counts are a standard sampling procedure for many bird species, but lingering concerns still exist about the quality of information produced from the method. It is well known that variation in observer ability and environmental conditions can influence the detection probability of birds in point counts, but many biologists have been reluctant to abandon point counts in favor of more intensive approaches to counting. However, over the past few years a variety of statistical and methodological developments have begun to provide practical ways of overcoming some of the problems with point counts. We describe some of these approaches, and show how they can be integrated into standard point count protocols to greatly enhance the quality of the information. Several tools now exist for estimation of detection probability of birds during counts, including distance sampling, double observer methods, time-depletion (removal) methods, and hybrid methods that combine these approaches. Many counts are conducted in habitats that make auditory detection of birds much more likely than visual detection. As a framework for understanding detection probability during such counts, we propose separating two components of the probability a bird is detected during a count into (1) the probability a bird vocalizes during the count and (2) the probability this vocalization is detected by an observer. In addition, we propose that some measure of the area sampled during a count is necessary for valid inferences about bird populations. This can be done by employing fixed-radius counts or more sophisticated distance-sampling models. We recommend any studies employing point counts be designed to estimate detection probability and to include a measure of the area sampled.
A hierarchical spatiotemporal analog forecasting model for count data.
McDermott, Patrick L; Wikle, Christopher K; Millspaugh, Joshua
2018-01-01
Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model-based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.
Directory of Open Access Journals (Sweden)
Thomas Weidinger
2016-01-01
Full Text Available This work proposes a dedicated statistical algorithm to perform a direct reconstruction of material-decomposed images from data acquired with photon-counting detectors (PCDs in computed tomography. It is based on local approximations (surrogates of the negative logarithmic Poisson probability function. Exploiting the convexity of this function allows for parallel updates of all image pixels. Parallel updates can compensate for the rather slow convergence that is intrinsic to statistical algorithms. We investigate the accuracy of the algorithm for ideal photon-counting detectors. Complementarily, we apply the algorithm to simulation data of a realistic PCD with its spectral resolution limited by K-escape, charge sharing, and pulse-pileup. For data from both an ideal and realistic PCD, the proposed algorithm is able to correct beam-hardening artifacts and quantitatively determine the material fractions of the chosen basis materials. Via regularization we were able to achieve a reduction of image noise for the realistic PCD that is up to 90% lower compared to material images form a linear, image-based material decomposition using FBP images. Additionally, we find a dependence of the algorithms convergence speed on the threshold selection within the PCD.
A Frank mixture copula family for modeling higher-order correlations of neural spike counts
International Nuclear Information System (INIS)
Onken, Arno; Obermayer, Klaus
2009-01-01
In order to evaluate the importance of higher-order correlations in neural spike count codes, flexible statistical models of dependent multivariate spike counts are required. Copula families, parametric multivariate distributions that represent dependencies, can be applied to construct such models. We introduce the Frank mixture family as a new copula family that has separate parameters for all pairwise and higher-order correlations. In contrast to the Farlie-Gumbel-Morgenstern copula family that shares this property, the Frank mixture copula can model strong correlations. We apply spike count models based on the Frank mixture copula to data generated by a network of leaky integrate-and-fire neurons and compare the goodness of fit to distributions based on the Farlie-Gumbel-Morgenstern family. Finally, we evaluate the importance of using proper single neuron spike count distributions on the Shannon information. We find notable deviations in the entropy that increase with decreasing firing rates. Moreover, we find that the Frank mixture family increases the log likelihood of the fit significantly compared to the Farlie-Gumbel-Morgenstern family. This shows that the Frank mixture copula is a useful tool to assess the importance of higher-order correlations in spike count codes.
Adrion, Christine; Mansmann, Ulrich
2012-09-10
A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions
Statistical properties of several models of fractional random point processes
Bendjaballah, C.
2011-08-01
Statistical properties of several models of fractional random point processes have been analyzed from the counting and time interval statistics points of view. Based on the criterion of the reduced variance, it is seen that such processes exhibit nonclassical properties. The conditions for these processes to be treated as conditional Poisson processes are examined. Numerical simulations illustrate part of the theoretical calculations.
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Zhang Zijing; Song Jie; Zhao Yuan; Wu Long
2017-01-01
Single-photon detectors possess the ultra-high sensitivity, but they cannot directly respond to signal intensity. Conventional methods adopt sampling gates with fixed width and count the triggered number of sampling gates, which is capable of obtaining photon counting probability to estimate the echo signal intensity. In this paper, we not only count the number of triggered sampling gates, but also record the triggered time position of photon counting pulses. The photon counting probability density distribution is obtained through the statistics of a series of the triggered time positions. Then Minimum Variance Unbiased Estimation (MVUE) method is used to estimate the echo signal intensity. Compared with conventional methods, this method can improve the estimation accuracy of echo signal intensity due to the acquisition of more detected information. Finally, a proof-of-principle laboratory system is established. The estimation accuracy of echo signal intensity is discussed and a high accuracy intensity image is acquired under low-light level environments. (paper)
Use of a mixture statistical model in studying malaria vectors density.
Directory of Open Access Journals (Sweden)
Olayidé Boussari
Full Text Available Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with "village" as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%. The NPMP model had a good aptitude to predict the observed values and showed that: i proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.
Directory of Open Access Journals (Sweden)
Adrion Christine
2012-09-01
Full Text Available Abstract Background A statistical analysis plan (SAP is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs. The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC or probability integral transform (PIT, and by using proper scoring rules (e.g. the logarithmic score. Results The instruments under study
Central Limit Theorem for Exponentially Quasi-local Statistics of Spin Models on Cayley Graphs
Reddy, Tulasi Ram; Vadlamani, Sreekar; Yogeshwaran, D.
2018-04-01
Central limit theorems for linear statistics of lattice random fields (including spin models) are usually proven under suitable mixing conditions or quasi-associativity. Many interesting examples of spin models do not satisfy mixing conditions, and on the other hand, it does not seem easy to show central limit theorem for local statistics via quasi-associativity. In this work, we prove general central limit theorems for local statistics and exponentially quasi-local statistics of spin models on discrete Cayley graphs with polynomial growth. Further, we supplement these results by proving similar central limit theorems for random fields on discrete Cayley graphs taking values in a countable space, but under the stronger assumptions of α -mixing (for local statistics) and exponential α -mixing (for exponentially quasi-local statistics). All our central limit theorems assume a suitable variance lower bound like many others in the literature. We illustrate our general central limit theorem with specific examples of lattice spin models and statistics arising in computational topology, statistical physics and random networks. Examples of clustering spin models include quasi-associated spin models with fast decaying covariances like the off-critical Ising model, level sets of Gaussian random fields with fast decaying covariances like the massive Gaussian free field and determinantal point processes with fast decaying kernels. Examples of local statistics include intrinsic volumes, face counts, component counts of random cubical complexes while exponentially quasi-local statistics include nearest neighbour distances in spin models and Betti numbers of sub-critical random cubical complexes.
Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model
Energy Technology Data Exchange (ETDEWEB)
Walker, M D; Asselin, M-C; Julyan, P J; Feldmann, M; Matthews, J C [School of Cancer and Enabling Sciences, Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Talbot, P S [Mental Health and Neurodegeneration Research Group, Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Jones, T, E-mail: matthew.walker@manchester.ac.uk [Academic Department of Radiation Oncology, Christie Hospital, University of Manchester, Manchester M20 4BX (United Kingdom)
2011-02-21
Iterative image reconstruction methods such as ordered-subset expectation maximization (OSEM) are widely used in PET. Reconstructions via OSEM are however reported to be biased for low-count data. We investigated this and considered the impact for dynamic PET. Patient listmode data were acquired in [{sup 11}C]DASB and [{sup 15}O]H{sub 2}O scans on the HRRT brain PET scanner. These data were subsampled to create many independent, low-count replicates. The data were reconstructed and the images from low-count data were compared to the high-count originals (from the same reconstruction method). This comparison enabled low-statistics bias to be calculated for the given reconstruction, as a function of the noise-equivalent counts (NEC). Two iterative reconstruction methods were tested, one with and one without an image-based resolution model (RM). Significant bias was observed when reconstructing data of low statistical quality, for both subsampled human and simulated data. For human data, this bias was substantially reduced by including a RM. For [{sup 11}C]DASB the low-statistics bias in the caudate head at 1.7 M NEC (approx. 30 s) was -5.5% and -13% with and without RM, respectively. We predicted biases in the binding potential of -4% and -10%. For quantification of cerebral blood flow for the whole-brain grey- or white-matter, using [{sup 15}O]H{sub 2}O and the PET autoradiographic method, a low-statistics bias of <2.5% and <4% was predicted for reconstruction with and without the RM. The use of a resolution model reduces low-statistics bias and can hence be beneficial for quantitative dynamic PET.
Theory of photoelectron counting statistics
International Nuclear Information System (INIS)
Blake, J.
1980-01-01
The purpose of the present essay is to provide a detailed analysis of those theoretical aspects of photoelectron counting which are capable of experimental verification. Most of our interest is in the physical phenomena themselves, while part is in the mathematical techniques. Many of the mathematical methods used in the analysis of the photoelectron counting problem are generally unfamiliar to physicists interested in the subject. For this reason we have developed the essay in such a fashion that, although primary interest is focused on the physical phenomena, we have also taken pains to carry out enough of the analysis so that the reader can follow the main details. We have chosen to present a consistently quantum mechanical version of the subject, in that we follow the Glauber theory throughout. (orig./WL)
International Nuclear Information System (INIS)
Béthermin, Matthieu; Daddi, Emanuele; Sargent, Mark T.; Elbaz, David; Mullaney, James; Pannella, Maurilio; Magdis, Georgios; Hezaveh, Yashar; Le Borgne, Damien; Buat, Véronique; Charmandaris, Vassilis; Lagache, Guilaine; Scott, Douglas
2012-01-01
We reproduce the mid-infrared to radio galaxy counts with a new empirical model based on our current understanding of the evolution of main-sequence (MS) and starburst (SB) galaxies. We rely on a simple spectral energy distribution (SED) library based on Herschel observations: a single SED for the MS and another one for SB, getting warmer with redshift. Our model is able to reproduce recent measurements of galaxy counts performed with Herschel, including counts per redshift slice. This agreement demonstrates the power of our 2-Star-Formation Modes (2SFM) decomposition in describing the statistical properties of infrared sources and their evolution with cosmic time. We discuss the relative contribution of MS and SB galaxies to the number counts at various wavelengths and flux densities. We also show that MS galaxies are responsible for a bump in the 1.4 GHz radio counts around 50 μJy. Material of the model (predictions, SED library, mock catalogs, etc.) is available online.
Statistical modelling of citation exchange between statistics journals.
Varin, Cristiano; Cattelan, Manuela; Firth, David
2016-01-01
Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.
Bastianello, Alvise; Piroli, Lorenzo; Calabrese, Pasquale
2018-05-01
We derive exact analytic expressions for the n -body local correlations in the one-dimensional Bose gas with contact repulsive interactions (Lieb-Liniger model) in the thermodynamic limit. Our results are valid for arbitrary states of the model, including ground and thermal states, stationary states after a quantum quench, and nonequilibrium steady states arising in transport settings. Calculations for these states are explicitly presented and physical consequences are critically discussed. We also show that the n -body local correlations are directly related to the full counting statistics for the particle-number fluctuations in a short interval, for which we provide an explicit analytic result.
Comparison of probabilistic models of the distribution of counts
International Nuclear Information System (INIS)
Salma, I.; Zemplen-Papp, E.
1992-01-01
The binominal, Poisson and modified Poisson models for describing the statistical nature of the distribution of counts are compared theoretically, and conclusions for application are proposed. The validity of the Poisson and the modified Poisson distribution for observing k events in a short time interval is investigated experimentally for various measuring times. The experiments to measure the influence of the significant radioactive decay were performed with 89m Y (T 1/2 =16.06 s), using a multichannel analyser (4096 channels) in the multiscaling mode. According to the results, Poisson distribution describes the counting experiment for short measuring times (up to T=0.5 T 1/2 ) and its application is recommended. However, the analysis of the data demonstrated that for long measurements (T≥1 T 1/2 ) Poisson distribution is not valid and the modified Poisson distribution is preferable. The practical implications in calculating uncertainties and in optimizing the measuring time are discussed. (author) 20 refs.; 7 figs.; 1 tab
Hidden Markov models for zero-inflated Poisson counts with an application to substance use.
DeSantis, Stacia M; Bandyopadhyay, Dipankar
2011-06-30
Paradigms for substance abuse cue-reactivity research involve pharmacological or stressful stimulation designed to elicit stress and craving responses in cocaine-dependent subjects. It is unclear as to whether stress induced from participation in such studies increases drug-seeking behavior. We propose a 2-state Hidden Markov model to model the number of cocaine abuses per week before and after participation in a stress-and cue-reactivity study. The hypothesized latent state corresponds to 'high' or 'low' use. To account for a preponderance of zeros, we assume a zero-inflated Poisson model for the count data. Transition probabilities depend on the prior week's state, fixed demographic variables, and time-varying covariates. We adopt a Bayesian approach to model fitting, and use the conditional predictive ordinate statistic to demonstrate that the zero-inflated Poisson hidden Markov model outperforms other models for longitudinal count data. Copyright © 2011 John Wiley & Sons, Ltd.
International Nuclear Information System (INIS)
Vincent, C.H.
1982-01-01
Bayes' principle is applied to the differential counting measurement of a positive quantity in which the statistical errors are not necessarily small in relation to the true value of the quantity. The methods of estimation derived are found to give consistent results and to avoid the anomalous negative estimates sometimes obtained by conventional methods. One of the methods given provides a simple means of deriving the required estimates from conventionally presented results and appears to have wide potential applications. Both methods provide the actual posterior probability distribution of the quantity to be measured. A particularly important potential application is the correction of counts on low radioacitvity samples for background. (orig.)
Amalia, Junita; Purhadi, Otok, Bambang Widjanarko
2017-11-01
Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.
International Nuclear Information System (INIS)
Valor, Alma; Alfonso, Lester; Caleyo, Francisco; Vidal, Julio; Perez-Baruch, Eloy; Hallen, José M.
2015-01-01
Highlights: • Observed external-corrosion defects in underground pipelines revealed a tendency to cluster. • The Poisson distribution is unable to fit extensive count data for these type of defects. • In contrast, the negative binomial distribution provides a suitable count model for them. • Two spatial stochastic processes lead to the negative binomial distribution for defect counts. • They are the Gamma-Poisson mixed process and the compound Poisson process. • A Rogeŕs process also arises as a plausible temporal stochastic process leading to corrosion defect clustering and to negative binomially distributed defect counts. - Abstract: The spatial distribution of external corrosion defects in buried pipelines is usually described as a Poisson process, which leads to corrosion defects being randomly distributed along the pipeline. However, in real operating conditions, the spatial distribution of defects considerably departs from Poisson statistics due to the aggregation of defects in groups or clusters. In this work, the statistical analysis of real corrosion data from underground pipelines operating in southern Mexico leads to conclude that the negative binomial distribution provides a better description for defect counts. The origin of this distribution from several processes is discussed. The analysed processes are: mixed Gamma-Poisson, compound Poisson and Roger’s processes. The physical reasons behind them are discussed for the specific case of soil corrosion.
What every radiochemist should know about statistics
International Nuclear Information System (INIS)
Nicholson, W.L.
1994-04-01
Radionuclide decay and measurement with appropriate counting instruments is one of the few physical processes for which exact mathematical/probabilistic models are available. This paper discusses statistical procedures associated with display and analysis of radionuclide counting data that derive from these exact models. For low count situations the attractiveness of fixed-count-random-time procedures is discussed
Farey Statistics in Time n^{2/3} and Counting Primitive Lattice Points in Polygons
Patrascu, Mihai
2007-01-01
We present algorithms for computing ranks and order statistics in the Farey sequence, taking time O (n^{2/3}). This improves on the recent algorithms of Pawlewicz [European Symp. Alg. 2007], running in time O (n^{3/4}). We also initiate the study of a more general algorithmic problem: counting primitive lattice points in planar shapes.
DEFF Research Database (Denmark)
Flindt, Christian; Novotny, Tomás; Braggio, Alessandro
2010-01-01
Recent experimental progress has made it possible to detect in real-time single electrons tunneling through Coulomb blockade nanostructures, thereby allowing for precise measurements of the statistical distribution of the number of transferred charges, the so-called full counting statistics...... interactions. Our recursive method can treat systems with many states as well as non-Markovian dynamics. We illustrate our approach with three examples of current experimental relevance: bunching transport through a two-level quantum dot, transport through a nanoelectromechanical system with dynamical Franck...
Gene coexpression measures in large heterogeneous samples using count statistics.
Wang, Y X Rachel; Waterman, Michael S; Huang, Haiyan
2014-11-18
With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.
Full counting statistics of level renormalization in electron transport through double quantum dots
International Nuclear Information System (INIS)
Luo Junyan; Shen Yu; Cen Gang; He Xiaoling; Wang Changrong; Jiao Hujun
2011-01-01
We examine the full counting statistics of electron transport through double quantum dots coupled in series, with particular attention being paid to the unique features originating from level renormalization. It is clearly illustrated that the energy renormalization gives rise to a dynamic charge blockade mechanism, which eventually results in super-Poissonian noise. Coupling of the double dots to an external heat bath leads to dephasing and relaxation mechanisms, which are demonstrated to suppress the noise in a unique way.
Poisson statistics application in modelling of neutron detection
International Nuclear Information System (INIS)
Avdic, S.; Marinkovic, P.
1996-01-01
The main purpose of this study is taking into account statistical analysis of the experimental data which were measured by 3 He neutron spectrometer. The unfolding method based on principle of maximum likelihood incorporates the Poisson approximation of counting statistics applied (aithor)
RCT: Module 2.03, Counting Errors and Statistics, Course 8768
Energy Technology Data Exchange (ETDEWEB)
Hillmer, Kurt T. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-01
Radiological sample analysis involves the observation of a random process that may or may not occur and an estimation of the amount of radioactive material present based on that observation. Across the country, radiological control personnel are using the activity measurements to make decisions that may affect the health and safety of workers at those facilities and their surrounding environments. This course will present an overview of measurement processes, a statistical evaluation of both measurements and equipment performance, and some actions to take to minimize the sources of error in count room operations. This course will prepare the student with the skills necessary for radiological control technician (RCT) qualification by passing quizzes, tests, and the RCT Comprehensive Phase 1, Unit 2 Examination (TEST 27566) and by providing in the field skills.
Energy Technology Data Exchange (ETDEWEB)
Cluchet, J.
1960-07-01
After a recall of some aspects regarding the Gauss law and the Gauss curve, this note addresses the case of performance of a large number of measurements of a source activity by means of a sensor (counter, scintillator, nuclear emulsion, etc.) at equal intervals, and with a number of events which is not rigorously constant. Thus, it addresses measurements, and more particularly counting operations in a random or statistical environment. It more particularly addresses the case of a counting rate due to a source greater (and then lower) than twenty times the Eigen movement. The validity of curves is discussed.
Poissonian and binomial models in radionuclide metrology by liquid scintillation counting
International Nuclear Information System (INIS)
Grau Malonda, A.
1990-01-01
Binomial and Poissonian models developed for calculating the counting efficiency from a free parameter is analysed in this paper. This model have been applied to liquid scintillator counting systems with two or three photomultipliers. It is mathematically demostrated that both models are equivalent and that the counting efficiencies calculated either from one or the other model are identical. (Author)
Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin
2018-01-01
Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.
On-line statistical processing of radiation detector pulse trains with time-varying count rates
International Nuclear Information System (INIS)
Apostolopoulos, G.
2008-01-01
Statistical analysis is of primary importance for the correct interpretation of nuclear measurements, due to the inherent random nature of radioactive decay processes. This paper discusses the application of statistical signal processing techniques to the random pulse trains generated by radiation detectors. The aims of the presented algorithms are: (i) continuous, on-line estimation of the underlying time-varying count rate θ(t) and its first-order derivative dθ/dt; (ii) detection of abrupt changes in both of these quantities and estimation of their new value after the change point. Maximum-likelihood techniques, based on the Poisson probability distribution, are employed for the on-line estimation of θ and dθ/dt. Detection of abrupt changes is achieved on the basis of the generalized likelihood ratio statistical test. The properties of the proposed algorithms are evaluated by extensive simulations and possible applications for on-line radiation monitoring are discussed
Evaluation of Deterministic and Stochastic Components of Traffic Counts
Directory of Open Access Journals (Sweden)
Ivan Bošnjak
2012-10-01
Full Text Available Traffic counts or statistical evidence of the traffic processare often a characteristic of time-series data. In this paper fundamentalproblem of estimating deterministic and stochasticcomponents of a traffic process are considered, in the context of"generalised traffic modelling". Different methods for identificationand/or elimination of the trend and seasonal componentsare applied for concrete traffic counts. Further investigationsand applications of ARIMA models, Hilbert space formulationsand state-space representations are suggested.
Barber, Catherine; Azrael, Deborah; Cohen, Amy; Miller, Matthew; Thymes, Deonza; Wang, David Enze; Hemenway, David
2016-05-01
To evaluate the National Violent Death Reporting System (NVDRS) as a surveillance system for homicides by law enforcement officers. We assessed sensitivity and positive predictive value of the NVDRS "type of death" variable against our study count of homicides by police, which we derived from NVDRS coded and narrative data for states participating in NVDRS 2005 to 2012. We compared state counts of police homicides from NVDRS, Vital Statistics, and Federal Bureau of Investigation Supplementary Homicide Reports. We identified 1552 police homicides in the 16 states. Positive predictive value and sensitivity of the NVDRS "type of death" variable for police homicides were high (98% and 90%, respectively). Counts from Vital Statistics and Supplementary Homicide Reports were 58% and 48%, respectively, of our study total; gaps varied widely by state. The annual rate of police homicide (0.24/100,000) varied 5-fold by state and 8-fold by race/ethnicity. NVDRS provides more complete data on police homicides than do existing systems. Expanding NVDRS to all 50 states and making 2 improvements we identify will be an efficient way to provide the nation with more accurate, detailed data on homicides by law enforcement.
Counts-in-Cylinders in the Sloan Digital Sky Survey with Comparisons to N-Body
Energy Technology Data Exchange (ETDEWEB)
Berrier, Heather D.; Barton, Elizabeth J.; /UC, Irvine; Berrier, Joel C.; /Arkansas U.; Bullock, James S.; /UC, Irvine; Zentner, Andrew R.; /Pittsburgh U.; Wechsler, Risa H. /KIPAC, Menlo Park /SLAC
2010-12-16
Environmental statistics provide a necessary means of comparing the properties of galaxies in different environments and a vital test of models of galaxy formation within the prevailing, hierarchical cosmological model. We explore counts-in-cylinders, a common statistic defined as the number of companions of a particular galaxy found within a given projected radius and redshift interval. Galaxy distributions with the same two-point correlation functions do not necessarily have the same companion count distributions. We use this statistic to examine the environments of galaxies in the Sloan Digital Sky Survey, Data Release 4. We also make preliminary comparisons to four models for the spatial distributions of galaxies, based on N-body simulations, and data from SDSS DR4 to study the utility of the counts-in-cylinders statistic. There is a very large scatter between the number of companions a galaxy has and the mass of its parent dark matter halo and the halo occupation, limiting the utility of this statistic for certain kinds of environmental studies. We also show that prevalent, empirical models of galaxy clustering that match observed two- and three-point clustering statistics well fail to reproduce some aspects of the observed distribution of counts-in-cylinders on 1, 3 and 6-h{sup -1}Mpc scales. All models that we explore underpredict the fraction of galaxies with few or no companions in 3 and 6-h{sup -1} Mpc cylinders. Roughly 7% of galaxies in the real universe are significantly more isolated within a 6 h{sup -1} Mpc cylinder than the galaxies in any of the models we use. Simple, phenomenological models that map galaxies to dark matter halos fail to reproduce high-order clustering statistics in low-density environments.
An accurate behavioral model for single-photon avalanche diode statistical performance simulation
Xu, Yue; Zhao, Tingchen; Li, Ding
2018-01-01
An accurate behavioral model is presented to simulate important statistical performance of single-photon avalanche diodes (SPADs), such as dark count and after-pulsing noise. The derived simulation model takes into account all important generation mechanisms of the two kinds of noise. For the first time, thermal agitation, trap-assisted tunneling and band-to-band tunneling mechanisms are simultaneously incorporated in the simulation model to evaluate dark count behavior of SPADs fabricated in deep sub-micron CMOS technology. Meanwhile, a complete carrier trapping and de-trapping process is considered in afterpulsing model and a simple analytical expression is derived to estimate after-pulsing probability. In particular, the key model parameters of avalanche triggering probability and electric field dependence of excess bias voltage are extracted from Geiger-mode TCAD simulation and this behavioral simulation model doesn't include any empirical parameters. The developed SPAD model is implemented in Verilog-A behavioral hardware description language and successfully operated on commercial Cadence Spectre simulator, showing good universality and compatibility. The model simulation results are in a good accordance with the test data, validating high simulation accuracy.
Directory of Open Access Journals (Sweden)
Himel eMallick
2016-03-01
Full Text Available Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP, Negative Binomial, and Zero-inflated Negative Binomial (ZINB. However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely
Intuitive introductory statistics
Wolfe, Douglas A
2017-01-01
This textbook is designed to give an engaging introduction to statistics and the art of data analysis. The unique scope includes, but also goes beyond, classical methodology associated with the normal distribution. What if the normal model is not valid for a particular data set? This cutting-edge approach provides the alternatives. It is an introduction to the world and possibilities of statistics that uses exercises, computer analyses, and simulations throughout the core lessons. These elementary statistical methods are intuitive. Counting and ranking features prominently in the text. Nonparametric methods, for instance, are often based on counts and ranks and are very easy to integrate into an introductory course. The ease of computation with advanced calculators and statistical software, both of which factor into this text, allows important techniques to be introduced earlier in the study of statistics. This book's novel scope also includes measuring symmetry with Walsh averages, finding a nonp...
Bayesian dynamic modeling of time series of dengue disease case counts.
Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander
2017-07-01
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful
Tutorial on Using Regression Models with Count Outcomes Using R
Directory of Open Access Journals (Sweden)
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
COUNTS-IN-CYLINDERS IN THE SLOAN DIGITAL SKY SURVEY WITH COMPARISONS TO N-BODY SIMULATIONS
International Nuclear Information System (INIS)
Berrier, Heather D.; Barton, Elizabeth J.; Bullock, James S.; Berrier, Joel C.; Zentner, Andrew R.; Wechsler, Risa H.
2011-01-01
Environmental statistics provide a necessary means of comparing the properties of galaxies in different environments, and a vital test of models of galaxy formation within the prevailing hierarchical cosmological model. We explore counts-in-cylinders, a common statistic defined as the number of companions of a particular galaxy found within a given projected radius and redshift interval. Galaxy distributions with the same two-point correlation functions do not necessarily have the same companion count distributions. We use this statistic to examine the environments of galaxies in the Sloan Digital Sky Survey Data Release 4 (SDSS DR4). We also make preliminary comparisons to four models for the spatial distributions of galaxies, based on N-body simulations and data from SDSS DR4, to study the utility of the counts-in-cylinders statistic. There is a very large scatter between the number of companions a galaxy has and the mass of its parent dark matter halo and the halo occupation, limiting the utility of this statistic for certain kinds of environmental studies. We also show that prevalent empirical models of galaxy clustering, that match observed two- and three-point clustering statistics well, fail to reproduce some aspects of the observed distribution of counts-in-cylinders on 1, 3, and 6 h -1 Mpc scales. All models that we explore underpredict the fraction of galaxies with few or no companions in 3 and 6 h -1 Mpc cylinders. Roughly 7% of galaxies in the real universe are significantly more isolated within a 6 h -1 Mpc cylinder than the galaxies in any of the models we use. Simple phenomenological models that map galaxies to dark matter halos fail to reproduce high-order clustering statistics in low-density environments.
Simulation on Poisson and negative binomial models of count road accident modeling
Sapuan, M. S.; Razali, A. M.; Zamzuri, Z. H.; Ibrahim, K.
2016-11-01
Accident count data have often been shown to have overdispersion. On the other hand, the data might contain zero count (excess zeros). The simulation study was conducted to create a scenarios which an accident happen in T-junction with the assumption the dependent variables of generated data follows certain distribution namely Poisson and negative binomial distribution with different sample size of n=30 to n=500. The study objective was accomplished by fitting Poisson regression, negative binomial regression and Hurdle negative binomial model to the simulated data. The model validation was compared and the simulation result shows for each different sample size, not all model fit the data nicely even though the data generated from its own distribution especially when the sample size is larger. Furthermore, the larger sample size indicates that more zeros accident count in the dataset.
Some Statistics for Measuring Large-Scale Structure
Brandenberger, Robert H.; Kaplan, David M.; A, Stephen; Ramsey
1993-01-01
Good statistics for measuring large-scale structure in the Universe must be able to distinguish between different models of structure formation. In this paper, two and three dimensional ``counts in cell" statistics and a new ``discrete genus statistic" are applied to toy versions of several popular theories of structure formation: random phase cold dark matter model, cosmic string models, and global texture scenario. All three statistics appear quite promising in terms of differentiating betw...
Regression analysis of mixed recurrent-event and panel-count data with additive rate models.
Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L
2015-03-01
Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. © 2014, The International Biometric Society.
Zheng, Han; Kimber, Alan; Goodwin, Victoria A; Pickering, Ruth M
2018-01-01
A common design for a falls prevention trial is to assess falling at baseline, randomize participants into an intervention or control group, and ask them to record the number of falls they experience during a follow-up period of time. This paper addresses how best to include the baseline count in the analysis of the follow-up count of falls in negative binomial (NB) regression. We examine the performance of various approaches in simulated datasets where both counts are generated from a mixed Poisson distribution with shared random subject effect. Including the baseline count after log-transformation as a regressor in NB regression (NB-logged) or as an offset (NB-offset) resulted in greater power than including the untransformed baseline count (NB-unlogged). Cook and Wei's conditional negative binomial (CNB) model replicates the underlying process generating the data. In our motivating dataset, a statistically significant intervention effect resulted from the NB-logged, NB-offset, and CNB models, but not from NB-unlogged, and large, outlying baseline counts were overly influential in NB-unlogged but not in NB-logged. We conclude that there is little to lose by including the log-transformed baseline count in standard NB regression compared to CNB for moderate to larger sized datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts
Directory of Open Access Journals (Sweden)
R. S. Sparks
2009-01-01
adaptive exponentially weighted moving average (EWMA plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forecasts of the next day's count. Departures of counts from their forecasts are monitored. The paper outlines an approach for improving early outbreak data signals by dynamically adjusting the exponential weights to be efficient at signalling local persistent high side changes. We emphasise outbreak signals in steady-state situations; that is, changes that occur after the EWMA statistic had run through several in-control counts.
Hess, Dale; van Lieshout, Marie-Colette; Payne, Bill; Stein, Alfred
This paper describes how spatial statistical techniques may be used to analyse weed occurrence in tropical fields. Quadrat counts of weed numbers are available over a series of years, as well as data on explanatory variables, and the aim is to smooth the data and assess spatial and temporal trends. We review a range of models for correlated count data. As an illustration, we consider data on striga infestation of a 60 × 24 m 2 millet field in Niger collected from 1985 until 1991, modelled by independent Poisson counts and a prior auto regression term enforcing spatial coherence. The smoothed fields show the presence of a seed bank, the estimated model parameters indicate a decay in the striga numbers over time, as well as a clear correlation with the amount of rainfall in 15 consecutive days following the sowing date. Such results could contribute to precision agriculture as a guide to more cost-effective striga control strategies.
An automated approach for annual layer counting in ice cores
Directory of Open Access Journals (Sweden)
M. Winstrup
2012-11-01
Full Text Available A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of hidden Markov models (HMMs, which originally was developed for use in machine speech recognition. The strength of the layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on statistical criteria for annual layer identification. The most likely positions of multiple layer boundaries in a section of ice core data are determined simultaneously, and a probabilistic uncertainty estimate of the resulting layer count is provided, ensuring an objective treatment of ambiguous layers in the data. Furthermore, multiple data series can be incorporated and used simultaneously. In this study, the automated layer counting algorithm has been applied to two ice core records from Greenland: one displaying a distinct annual signal and one which is more challenging. The algorithm shows high skill in reproducing the results from manual layer counts, and the resulting timescale compares well to absolute-dated volcanic marker horizons where these exist.
A scan statistic for continuous data based on the normal probability model
Directory of Open Access Journals (Sweden)
Huang Lan
2009-10-01
Full Text Available Abstract Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.
Applied categorical and count data analysis
Tang, Wan; Tu, Xin M
2012-01-01
Introduction Discrete Outcomes Data Source Outline of the BookReview of Key Statistical ResultsSoftwareContingency Tables Inference for One-Way Frequency TableInference for 2 x 2 TableInference for 2 x r TablesInference for s x r TableMeasures of AssociationSets of Contingency Tables Confounding Effects Sets of 2 x 2 TablesSets of s x r TablesRegression Models for Categorical Response Logistic Regression for Binary ResponseInference about Model ParametersGoodness of FitGeneralized Linear ModelsRegression Models for Polytomous ResponseRegression Models for Count Response Poisson Regression Mode
Full-counting statistics of energy transport of molecular junctions in the polaronic regime
International Nuclear Information System (INIS)
Tang, Gaomin; Yu, Zhizhou; Wang, Jian
2017-01-01
We investigate the full-counting statistics (FCS) of energy transport carried by electrons in molecular junctions for the Anderson–Holstein model in the polaronic regime. Using the two-time quantum measurement scheme, the generating function (GF) for the energy transport is derived and expressed as a Fredholm determinant in terms of Keldysh nonequilibrium Green’s function in the time domain. Dressed tunneling approximation is used in decoupling the phonon cloud operator in the polaronic regime. This formalism enables us to analyze the time evolution of energy transport dynamics after a sudden switch-on of the coupling between the dot and the leads towards the stationary state. The steady state energy current cumulant GF in the long time limit is obtained in the energy domain as well. Universal relations for steady state energy current FCS are derived under a finite temperature gradient with zero bias and this enabled us to express the equilibrium energy current cumulant by a linear combination of lower order cumulants. The behaviors of energy current cumulants in steady state under temperature gradient and external bias are numerically studied and explained. The transient dynamics of energy current cumulants is numerically calculated and analyzed. Universal scaling of normalized transient energy cumulants is found under both temperature gradient and external bias. (paper)
Zero-inflated count models for longitudinal measurements with heterogeneous random effects.
Zhu, Huirong; Luo, Sheng; DeSantis, Stacia M
2017-08-01
Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.
Peak-counts blood flow model-errors and limitations
International Nuclear Information System (INIS)
Mullani, N.A.; Marani, S.K.; Ekas, R.D.; Gould, K.L.
1984-01-01
The peak-counts model has several advantages, but its use may be limited due to the condition that the venous egress may not be negligible at the time of peak-counts. Consequently, blood flow measurements by the peak-counts model will depend on the bolus size, bolus duration, and the minimum transit time of the bolus through the region of interest. The effect of bolus size on the measurement of extraction fraction and blood flow was evaluated by injecting 1 to 30ml of rubidium chloride in the femoral vein of a dog and measuring the myocardial activity with a beta probe over the heart. Regional blood flow measurements were not found to vary with bolus sizes up to 30ml. The effect of bolus duration was studied by injecting a 10cc bolus of tracer at different speeds in the femoral vein of a dog. All intravenous injections undergo a broadening of the bolus duration due to the transit time of the tracer through the lungs and the heart. This transit time was found to range from 4-6 second FWHM and dominates the duration of the bolus to the myocardium for up to 3 second injections. A computer simulation has been carried out in which the different parameters of delay time, extraction fraction, and bolus duration can be changed to assess the errors in the peak-counts model. The results of the simulations show that the error will be greatest for short transit time delays and for low extraction fractions
Assessment of noise in a digital image using the join-count statistic and the Moran test
International Nuclear Information System (INIS)
Kehshih Chuang; Huang, H.K.
1992-01-01
It is assumed that data bits of a pixel in digital images can be divided into signal and noise bits. The signal bits occupy the most significant part of the pixel. The signal parts of each pixel are correlated while the noise parts are uncorrelated. Two statistical methods, the Moran test and the join-count statistic, are used to examine the noise parts. Images from computerized tomography, magnetic resonance and computed radiography are used for the evaluation of the noise bits. A residual image is formed by subtracting the original image from its smoothed version. The noise level in the residual image is then identical to that in the original image. Both statistical tests are then performed on the bit planes of the residual image. Results show that most digital images contain only 8-9 bits of correlated information. Both methods are easy to implement and fast to perform. (author)
Overdispersion in allelic counts and θ-correction in forensic genetics
DEFF Research Database (Denmark)
Tvedebrink, Torben
2009-01-01
A statistical model for incorporating the extra variability in allelic counts due to subpopulation structures is presented. In forensic genetics, this effect is modelled by the identical-by-decent-parameter, θ . It is shown, that θ may be defined as an overdispersion parameter capturing the extra...
Vandergoes, Marcus J.; Howarth, Jamie D.; Dunbar, Gavin B.; Turnbull, Jocelyn C.; Roop, Heidi A.; Levy, Richard H.; Li, Xun; Prior, Christine; Norris, Margaret; Keller, Liz D.; Baisden, W. Troy; Ditchburn, Robert; Fitzsimons, Sean J.; Bronk Ramsey, Christopher
2018-05-01
Annually resolved (varved) lake sequences are important palaeoenvironmental archives as they offer a direct incremental dating technique for high-frequency reconstruction of environmental and climate change. Despite the importance of these records, establishing a robust chronology and quantifying its precision and accuracy (estimations of error) remains an essential but challenging component of their development. We outline an approach for building reliable independent chronologies, testing the accuracy of layer counts and integrating all chronological uncertainties to provide quantitative age and error estimates for varved lake sequences. The approach incorporates (1) layer counts and estimates of counting precision; (2) radiometric and biostratigrapic dating techniques to derive independent chronology; and (3) the application of Bayesian age modelling to produce an integrated age model. This approach is applied to a case study of an annually resolved sediment record from Lake Ohau, New Zealand. The most robust age model provides an average error of 72 years across the whole depth range. This represents a fractional uncertainty of ∼5%, higher than the <3% quoted for most published varve records. However, the age model and reported uncertainty represent the best fit between layer counts and independent chronology and the uncertainties account for both layer counting precision and the chronological accuracy of the layer counts. This integrated approach provides a more representative estimate of age uncertainty and therefore represents a statistically more robust chronology.
Overdispersion in allelic counts and θ-correction in forensic genetics
DEFF Research Database (Denmark)
Tvedebrink, Torben
2010-01-01
We present a statistical model for incorporating the extra variability in allelic counts due to subpopulation structures. In forensic genetics, this effect is modelled by the identical-by-descent parameter θ, which measures the relationship between pairs of alleles within a population relative...... with computation of the profile log-likelihood, confidence intervals and hypothesis testing. In order to compare our method with existing methods, we reanalysed FBI data from Budowle and Moretti (1999) with allele counts in six US subpopulations. Furthermore, we investigate properties of our methodology from...
Statistical measurement of the gamma-ray source-count distribution as a function of energy
Zechlin, H.-S.; Cuoco, A.; Donato, F.; Fornengo, N.; Regis, M.
2017-01-01
Photon counts statistics have recently been proven to provide a sensitive observable for characterizing gamma-ray source populations and for measuring the composition of the gamma-ray sky. In this work, we generalize the use of the standard 1-point probability distribution function (1pPDF) to decompose the high-latitude gamma-ray emission observed with Fermi-LAT into: (i) point-source contributions, (ii) the Galactic foreground contribution, and (iii) a diffuse isotropic background contribution. We analyze gamma-ray data in five adjacent energy bands between 1 and 171 GeV. We measure the source-count distribution dN/dS as a function of energy, and demonstrate that our results extend current measurements from source catalogs to the regime of so far undetected sources. Our method improves the sensitivity for resolving point-source populations by about one order of magnitude in flux. The dN/dS distribution as a function of flux is found to be compatible with a broken power law. We derive upper limits on further possible breaks as well as the angular power of unresolved sources. We discuss the composition of the gamma-ray sky and capabilities of the 1pPDF method.
Statistical Models for Tornado Climatology: Long and Short-Term Views.
Elsner, James B; Jagger, Thomas H; Fricker, Tyler
2016-01-01
This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.
Identification of cotton properties to improve yarn count quality by using regression analysis
International Nuclear Information System (INIS)
Amin, M.; Ullah, M.; Akbar, A.
2014-01-01
Identification of raw material characteristics towards yarn count variation was studied by using statistical techniques. Regression analysis is used to meet the objective. Stepwise regression is used for mode) selection, and coefficient of determination and mean squared error (MSE) criteria are used to identify the contributing factors of cotton properties for yam count. Statistical assumptions of normality, autocorrelation and multicollinearity are evaluated by using probability plot, Durbin Watson test, variance inflation factor (VIF), and then model fitting is carried out. It is found that, invisible (INV), nepness (Nep), grayness (RD), cotton trash (TR) and uniformity index (VI) are the main contributing cotton properties for yarn count variation. The results are also verified by Pareto chart. (author)
Count Data On Cancer Death In Ohio A Bayesian Analysis
Directory of Open Access Journals (Sweden)
Walaa Hamdi
2015-08-01
Full Text Available This paper considers statistical modeling of count data on cancer death in Ohio State. We obtained count data on male and female from a website of the Centers for Disease Control and Prevention and used Bayesian analyses to find suitable models which help us to do inferences and predictions for next year. To assist us in selecting appropriate models we use criteria such as the DIC. In this paper we analyze the data to spatial longitudinal so we can capture possible correlations. Using our analyses we make predictions of the numbers of people who will die with cancer in a future year in Ohio State.
Physics colloquium: Single-electron counting in quantum metrology and in statistical mechanics
Geneva University
2011-01-01
GENEVA UNIVERSITY Ecole de physique Département de physique nucléaire et corspusculaire 24, quai Ernest-Ansermet 1211 Genève 4 Tél.: (022) 379 62 73 Fax: (022) 379 69 92olé Lundi 17 octobre 2011 17h00 - Ecole de Physique, Auditoire Stueckelberg PHYSICS COLLOQUIUM « Single-electron counting in quantum metrology and in statistical mechanics » Prof. Jukka Pekola Low Temperature Laboratory, Aalto University Helsinki, Finland First I discuss the basics of single-electron tunneling and its potential applications in metrology. My main focus is in developing an accurate source of single-electron current for the realization of the unit ampere. I discuss the principle and the present status of the so-called single- electron turnstile. Investigation of errors in transporting electrons one by one has revealed a wealth of observations on fundamental phenomena in mesoscopic superconductivity, including individual Andreev...
Modeling time-series count data: the unique challenges facing political communication studies.
Fogarty, Brian J; Monogan, James E
2014-05-01
This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al. (1997), Peake and Eshbaugh-Soha (2008), and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher. Copyright © 2013 Elsevier Inc. All rights reserved.
Taguchi, Katsuyuki; Polster, Christoph; Lee, Okkyun; Stierstorfer, Karl; Kappler, Steffen
2016-12-01
An x-ray photon interacts with photon counting detectors (PCDs) and generates an electron charge cloud or multiple clouds. The clouds (thus, the photon energy) may be split between two adjacent PCD pixels when the interaction occurs near pixel boundaries, producing a count at both of the pixels. This is called double-counting with charge sharing. (A photoelectric effect with K-shell fluorescence x-ray emission would result in double-counting as well). As a result, PCD data are spatially and energetically correlated, although the output of individual PCD pixels is Poisson distributed. Major problems include the lack of a detector noise model for the spatio-energetic cross talk and lack of a computationally efficient simulation tool for generating correlated Poisson data. A Monte Carlo (MC) simulation can accurately simulate these phenomena and produce noisy data; however, it is not computationally efficient. In this study, the authors developed a new detector model and implemented it in an efficient software simulator that uses a Poisson random number generator to produce correlated noisy integer counts. The detector model takes the following effects into account: (1) detection efficiency; (2) incomplete charge collection and ballistic effect; (3) interaction with PCDs via photoelectric effect (with or without K-shell fluorescence x-ray emission, which may escape from the PCDs or be reabsorbed); and (4) electronic noise. The correlation was modeled by using these two simplifying assumptions: energy conservation and mutual exclusiveness. The mutual exclusiveness is that no more than two pixels measure energy from one photon. The effect of model parameters has been studied and results were compared with MC simulations. The agreement, with respect to the spectrum, was evaluated using the reduced χ 2 statistics or a weighted sum of squared errors, χ red 2 (≥1), where χ red 2 =1 indicates a perfect fit. The model produced spectra with flat field irradiation that
Modeling patterns in count data using loglinear and related models
International Nuclear Information System (INIS)
Atwood, C.L.
1995-12-01
This report explains the use of loglinear and logit models, for analyzing Poisson and binomial counts in the presence of explanatory variables. The explanatory variables may be unordered categorical variables or numerical variables, or both. The report shows how to construct models to fit data, and how to test whether a model is too simple or too complex. The appropriateness of the methods with small data sets is discussed. Several example analyses, using the SAS computer package, illustrate the methods
Directory of Open Access Journals (Sweden)
Cheol-Eung Lee
2017-02-01
Full Text Available Several natural disasters occur because of torrential rainfalls. The change in global climate most likely increases the occurrences of such downpours. Hence, it is necessary to investigate the characteristics of the torrential rainfall events in order to introduce effective measures for mitigating disasters such as urban floods and landslides. However, one of the major problems is evaluating the number of torrential rainfall events from a statistical viewpoint. If the number of torrential rainfall occurrences during a month is considered as count data, their frequency distribution could be identified using a probability distribution. Generally, the number of torrential rainfall occurrences has been analyzed using the Poisson distribution (POI or the Generalized Poisson Distribution (GPD. However, it was reported that POI and GPD often overestimated or underestimated the observed count data when additional or fewer zeros were included. Hence, in this study, a zero-inflated model concept was applied to solve this problem existing in the conventional models. Zero-Inflated Poisson (ZIP model, Zero-Inflated Generalized Poisson (ZIGP model, and the Bayesian ZIGP model have often been applied to fit the count data having additional or fewer zeros. However, the applications of these models in water resource management have been very limited despite their efficiency and accuracy. The five models, namely, POI, GPD, ZIP, ZIGP, and Bayesian ZIGP, were applied to the torrential rainfall data having additional zeros obtained from two rain gauges in South Korea, and their applicability was examined in this study. In particular, the informative prior distributions evaluated via the empirical Bayes method using ten rain gauges were developed in the Bayesian ZIGP model. Finally, it was suggested to avoid using the POI and GPD models to fit the frequency of torrential rainfall data. In addition, it was concluded that the Bayesian ZIGP model used in this study
Modeling the frequency-dependent detective quantum efficiency of photon-counting x-ray detectors.
Stierstorfer, Karl
2018-01-01
To find a simple model for the frequency-dependent detective quantum efficiency (DQE) of photon-counting detectors in the low flux limit. Formula for the spatial cross-talk, the noise power spectrum and the DQE of a photon-counting detector working at a given threshold are derived. Parameters are probabilities for types of events like single counts in the central pixel, double counts in the central pixel and a neighboring pixel or single count in a neighboring pixel only. These probabilities can be derived in a simple model by extensive use of Monte Carlo techniques: The Monte Carlo x-ray propagation program MOCASSIM is used to simulate the energy deposition from the x-rays in the detector material. A simple charge cloud model using Gaussian clouds of fixed width is used for the propagation of the electric charge generated by the primary interactions. Both stages are combined in a Monte Carlo simulation randomizing the location of impact which finally produces the required probabilities. The parameters of the charge cloud model are fitted to the spectral response to a polychromatic spectrum measured with our prototype detector. Based on the Monte Carlo model, the DQE of photon-counting detectors as a function of spatial frequency is calculated for various pixel sizes, photon energies, and thresholds. The frequency-dependent DQE of a photon-counting detector in the low flux limit can be described with an equation containing only a small set of probabilities as input. Estimates for the probabilities can be derived from a simple model of the detector physics. © 2017 American Association of Physicists in Medicine.
Deep 3 GHz number counts from a P(D) fluctuation analysis
Vernstrom, T.; Scott, Douglas; Wall, J. V.; Condon, J. J.; Cotton, W. D.; Fomalont, E. B.; Kellermann, K. I.; Miller, N.; Perley, R. A.
2014-05-01
Radio source counts constrain galaxy populations and evolution, as well as the global star formation history. However, there is considerable disagreement among the published 1.4-GHz source counts below 100 μJy. Here, we present a statistical method for estimating the μJy and even sub-μJy source count using new deep wide-band 3-GHz data in the Lockman Hole from the Karl G. Jansky Very Large Array. We analysed the confusion amplitude distribution P(D), which provides a fresh approach in the form of a more robust model, with a comprehensive error analysis. We tested this method on a large-scale simulation, incorporating clustering and finite source sizes. We discuss in detail our statistical methods for fitting using Markov chain Monte Carlo, handling correlations, and systematic errors from the use of wide-band radio interferometric data. We demonstrated that the source count can be constrained down to 50 nJy, a factor of 20 below the rms confusion. We found the differential source count near 10 μJy to have a slope of -1.7, decreasing to about -1.4 at fainter flux densities. At 3 GHz, the rms confusion in an 8-arcsec full width at half-maximum beam is ˜ 1.2 μJy beam-1, and a radio background temperature ˜14 mK. Our counts are broadly consistent with published evolutionary models. With these results, we were also able to constrain the peak of the Euclidean normalized differential source count of any possible new radio populations that would contribute to the cosmic radio background down to 50 nJy.
A Realism-Based View on Counts in OMOP's Common Data Model.
Ceusters, Werner; Blaisure, Jonathan
2017-01-01
Correctly counting entities is a requirement for analytics tools to function appropriately. The Observational Medical Outcomes Partnership's (OMOP) Common Data Model (CDM) specifications were examined to assess the extent to which counting in OMOP CDM compatible data repositories would work as expected. To that end, constructs (tables, fields and attributes) defined in the OMOP CDM as well as cardinality constraints and other business rules found in its documentation and related literature were compared to the types of entities and axioms proposed in realism-based ontologies. It was found that not only the model itself, but also a proposed standard algorithm for computing condition eras may lead to erroneous counting of several sorts of entities.
Modelling a Java Ring based implementation of an N-Count payment system
Revill, J.D.; Hartel, Pieter H.
N-Count is a system for offline value transfer. A prototype of an N-Count payment system has been designed, and it has been implemented in Java. We have used the Java Ring with the Java Card API as a secure device. The system has also been modelled using the Spin model checker. The combined
Analysis of Parasite and Other Skewed Counts
Alexander, Neal
2012-01-01
Objective To review methods for the statistical analysis of parasite and other skewed count data. Methods Statistical methods for skewed count data are described and compared, with reference to those used over a ten year period of Tropical Medicine and International Health. Two parasitological datasets are used for illustration. Results Ninety papers were identified, 89 with descriptive and 60 with inferential analysis. A lack of clarity is noted in identifying measures of location, in particular the Williams and geometric mean. The different measures are compared, emphasizing the legitimacy of the arithmetic mean for skewed data. In the published papers, the t test and related methods were often used on untransformed data, which is likely to be invalid. Several approaches to inferential analysis are described, emphasizing 1) non-parametric methods, while noting that they are not simply comparisons of medians, and 2) generalized linear modelling, in particular with the negative binomial distribution. Additional methods, such as the bootstrap, with potential for greater use are described. Conclusions Clarity is recommended when describing transformations and measures of location. It is suggested that non-parametric methods and generalized linear models are likely to be sufficient for most analyses. PMID:22943299
Sampling, Probability Models and Statistical Reasoning Statistical
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Sampling, Probability Models and Statistical Reasoning Statistical Inference. Mohan Delampady V R Padmawar. General Article Volume 1 Issue 5 May 1996 pp 49-58 ...
Voet, van der H.; Goedhart, P.W.
2015-01-01
Publications on power analyses for field trial count data comparing transgenic and conventional crops have reported widely varying requirements for the replication needed to obtain statistical tests with adequate power. These studies are critically reviewed and complemented with a new simulation
Lukas, J M; Hawkins, D M; Kinsel, M L; Reneau, J K
2005-11-01
The objective of this study was to examine the relationship between monthly Dairy Herd Improvement (DHI) subclinical mastitis and new infection rate estimates and daily bulk tank somatic cell count (SCC) summarized by statistical process control tools. Dairy Herd Improvement Association test-day subclinical mastitis and new infection rate estimates along with daily or every other day bulk tank SCC data were collected for 12 mo of 2003 from 275 Upper Midwest dairy herds. Herds were divided into 5 herd production categories. A linear score [LNS = ln(BTSCC/100,000)/0.693147 + 3] was calculated for each individual bulk tank SCC. For both the raw SCC and the transformed data, the mean and sigma were calculated using the statistical quality control individual measurement and moving range chart procedure of Statistical Analysis System. One hundred eighty-three herds of the 275 herds from the study data set were then randomly selected and the raw (method 1) and transformed (method 2) bulk tank SCC mean and sigma were used to develop models for predicting subclinical mastitis and new infection rate estimates. Herd production category was also included in all models as 5 dummy variables. Models were validated by calculating estimates of subclinical mastitis and new infection rates for the remaining 92 herds and plotting them against observed values of each of the dependents. Only herd production category and bulk tank SCC mean were significant and remained in the final models. High R2 values (0.83 and 0.81 for methods 1 and 2, respectively) indicated a strong correlation between the bulk tank SCC and herd's subclinical mastitis prevalence. The standard errors of the estimate were 4.02 and 4.28% for methods 1 and 2, respectively, and decreased with increasing herd production. As a case study, Shewhart Individual Measurement Charts were plotted from the bulk tank SCC to identify shifts in mastitis incidence. Four of 5 charts examined signaled a change in bulk tank SCC before
An automated approach for annual layer counting in ice cores
DEFF Research Database (Denmark)
Winstrup, Mai; Svensson, A. M.; Rasmussen, S. O.
2012-01-01
A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of Hidden Markov Models (HMMs), which originally was developed for use in machine speech-recognition. The strength of the layer...
An automated approach for annual layer counting in ice cores
DEFF Research Database (Denmark)
Winstrup, Mai; Svensson, A. M.; Rasmussen, S. O.
2012-01-01
A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of hidden Markov models (HMMs), which originally was developed for use in machine speech recognition. The strength of the layer...
Evaluation of clustering statistics with N-body simulations
International Nuclear Information System (INIS)
Quinn, T.R.
1986-01-01
Two series of N-body simulations are used to determine the effectiveness of various clustering statistics in revealing initial conditions from evolved models. All the simulations contained 16384 particles and were integrated with the PPPM code. One series is a family of models with power at only one wavelength. The family contains five models with the wavelength of the power separated by factors of √2. The second series is a family of all equal power combinations of two wavelengths taken from the first series. The clustering statistics examined are the two point correlation function, the multiplicity function, the nearest neighbor distribution, the void probability distribution, the distribution of counts in cells, and the peculiar velocity distribution. It is found that the covariance function, the nearest neighbor distribution, and the void probability distribution are relatively insensitive to the initial conditions. The distribution of counts in cells show a little more sensitivity, but the multiplicity function is the best of the statistics considered for revealing the initial conditions
Poisson regression for modeling count and frequency outcomes in trauma research.
Gagnon, David R; Doron-LaMarca, Susan; Bell, Margret; O'Farrell, Timothy J; Taft, Casey T
2008-10-01
The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.
Statistical Model of Extreme Shear
DEFF Research Database (Denmark)
Larsen, Gunner Chr.; Hansen, Kurt Schaldemose
2004-01-01
In order to continue cost-optimisation of modern large wind turbines, it is important to continously increase the knowledge on wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describe the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of high-sampled full-scale time series measurements...... are consistent, given the inevitabel uncertainties associated with model as well as with the extreme value data analysis. Keywords: Statistical model, extreme wind conditions, statistical analysis, turbulence, wind loading, statistical analysis, turbulence, wind loading, wind shear, wind turbines....
Use of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials
Desmet, Lieven; Venet, David; Doffagne, Erik; Timmermans, Catherine; Legrand, Catherine; Burzykowski, Tomasz; Buyse, Marc
2017-01-01
As part of central statistical monitoring of multicenter clinical trial data, we propose a procedure based on the beta-binomial distribution for the detection of centers with atypical values for the probability of some event. The procedure makes no assumptions about the typical event proportion and uses the event counts from all centers to derive a reference model. The procedure is shown through simulations to have high sensitivity and high specificity if the contamination rate is small and t...
International Nuclear Information System (INIS)
Choi, Jiyoung; Kang, Dong-Goo; Kang, Sunghoon; Sung, Younghun; Ye, Jong Chul
2013-01-01
Purpose: Material decomposition using multienergy photon counting x-ray detectors (PCXD) has been an active research area over the past few years. Even with some success, the problem of optimal energy selection and three material decomposition including malignant tissue is still on going research topic, and more systematic studies are required. This paper aims to address this in a unified statistical framework in a mammographic environment.Methods: A unified statistical framework for energy level optimization and decomposition of three materials is proposed. In particular, an energy level optimization algorithm is derived using the theory of the minimum variance unbiased estimator, and an iterative algorithm is proposed for material composition as well as system parameter estimation under the unified statistical estimation framework. To verify the performance of the proposed algorithm, the authors performed simulation studies as well as real experiments using physical breast phantom and ex vivo breast specimen. Quantitative comparisons using various performance measures were conducted, and qualitative performance evaluations for ex vivo breast specimen were also performed by comparing the ground-truth malignant tissue areas identified by radiologists.Results: Both simulation and real experiments confirmed that the optimized energy bins by the proposed method allow better material decomposition quality. Moreover, for the specimen thickness estimation errors up to 2 mm, the proposed method provides good reconstruction results in both simulation and real ex vivo breast phantom experiments compared to existing methods.Conclusions: The proposed statistical framework of PCXD has been successfully applied for the energy optimization and decomposition of three material in a mammographic environment. Experimental results using the physical breast phantom and ex vivo specimen support the practicality of the proposed algorithm
Xiao, Yundan; Zhang, Xiongqing; Ji, Ping
2015-01-01
Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence.
Bilinear modulation models for seasonal tables of counts
B.D. Marx (Brian); P.H.C. Eilers (Paul); J. Gampe (Jutta); R. Rau (Roland)
2010-01-01
textabstractWe propose generalized linear models for time or age-time tables of seasonal counts, with the goal of better understanding seasonal patterns in the data. The linear predictor contains a smooth component for the trend and the product of a smooth component (the modulation) and a periodic
DEFF Research Database (Denmark)
Christensen, Nana Louise; Tolbod, Lars Poulsen
PET scans. 3) Static and dynamic images from a set of 7 patients (BSA: 1.6-2.2 m2) referred for 82Rb cardiac PET was analyzed using a range of beta factors. Results were compared to the institution’s standard clinical practice reconstruction protocol. All scans were performed on GE DMI Digital......Aim: Q.Clear reconstruction is expected to improve detection of perfusion defects in cardiac PET due to the high degree of image convergence and effective noise suppression. However, 82Rb (T½=76s) possess a special problem, since count statistics vary significantly not only between patients...... statistics using a cardiac PET phantom as well as a selection of clinical patients referred for 82Rb cardiac PET. Methods: The study consistent of 3 parts: 1) A thorax-cardiac phantom was scanned for 10 minutes after injection of 1110 MBq 82Rb. Frames at 3 different times after infusion were reconstructed...
Relativistic beaming and quasar statistics
International Nuclear Information System (INIS)
Orr, M.J.L.; Browne, I.W.A.
1982-01-01
The statistical predictions of a unified scheme for the radio emission from quasars are explored. This scheme attributes the observed differences between flat- and steep-spectrum quasars to projection and the effects of relativistic beaming of the emission from the nuclear components. We use a simple quasar model consisting of a compact relativistically beamed core with spectral index zero and unbeamed lobes, spectral index - 1, to predict the proportion of flat-spectrum sources in flux-limited samples selected at different frequencies. In our model this fraction depends on the core Lorentz factor, γ and we find that a value of approximately 5 gives satisfactory agreement with observation. In a similar way the model is used to construct the expected number/flux density counts for flat-spectrum quasars from the observed steep-spectrum counts. Again, good agreement with the observations is obtained if the average core Lorentz factor is about 5. Independent estimates of γ from observations of superluminal motion in quasars are of the same order of magnitude. We conclude that the statistical properties of quasars are entirely consistent with the predictions of simple relativistic-beam models. (author)
Modelling T4 cell count as a marker of HIV progression in the ...
African Journals Online (AJOL)
Modelling T4 cell count as a marker of HIV progression in the absence of any defense mechanism. VSM Yadavalli, MMO Labeodan, S Udayabaskaran, N Forche. Abstract. The T4 cell count, which is considered one of the markers of disease progression in an HIV infected individual, is modelled in this paper. The World ...
Statistical analysis of nematode counts from interlaboratory proficiency tests
Berg, van den W.; Hartsema, O.; Nijs, Den J.M.F.
2014-01-01
A series of proficiency tests on potato cyst nematode (PCN; n=29) and free-living stages of Meloidogyne and Pratylenchus (n=23) were investigated to determine the accuracy and precision of the nematode counts and to gain insights into possible trends and potential improvements. In each test, each
A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA
WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value
1993-01-01
In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing
Souto, R Seoane; Martín-Rodero, A; Yeyati, A Levy
2016-12-23
We analyze the quantum quench dynamics in the formation of a phase-biased superconducting nanojunction. We find that in the absence of an external relaxation mechanism and for very general conditions the system gets trapped in a metastable state, corresponding to a nonequilibrium population of the Andreev bound states. The use of the time-dependent full counting statistics analysis allows us to extract information on the asymptotic population of even and odd many-body states, demonstrating that a universal behavior, dependent only on the Andreev state energy, is reached in the quantum point contact limit. These results shed light on recent experimental observations on quasiparticle trapping in superconducting atomic contacts.
Exclusion statistics and integrable models
International Nuclear Information System (INIS)
Mashkevich, S.
1998-01-01
The definition of exclusion statistics that was given by Haldane admits a 'statistical interaction' between distinguishable particles (multispecies statistics). For such statistics, thermodynamic quantities can be evaluated exactly; explicit expressions are presented here for cluster coefficients. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models of the Calogero-Sutherland type. The interesting questions of generalizing this correspondence to the higher-dimensional and the multispecies cases remain essentially open; however, our results provide some hints as to searches for the models in question
Galaxy modelling. II. Multi-wavelength faint counts from a semi-analytic model of galaxy formation
Devriendt, J. E. G.; Guiderdoni, B.
2000-11-01
This paper predicts self-consistent faint galaxy counts from the UV to the submm wavelength range. The stardust spectral energy distributions described in Devriendt et al. \\citeparyear{DGS99} (Paper I) are embedded within the explicit cosmological framework of a simple semi-analytic model of galaxy formation and evolution. We begin with a description of the non-dissipative and dissipative collapses of primordial perturbations, and plug in standard recipes for star formation, stellar evolution and feedback. We also model the absorption of starlight by dust and its re-processing in the IR and submm. We then build a class of models which capture the luminosity budget of the universe through faint galaxy counts and redshift distributions in the whole wavelength range spanned by our spectra. In contrast with a rather stable behaviour in the optical and even in the far-IR, the submm counts are dramatically sensitive to variations in the cosmological parameters and changes in the star formation history. Faint submm counts are more easily accommodated within an open universe with a low value of Omega_0 , or a flat universe with a non-zero cosmological constant. We confirm the suggestion of Guiderdoni et al. \\citeparyear{GHBM98} that matching the current multi-wavelength data requires a population of heavily-extinguished, massive galaxies with large star formation rates ( ~ 500 M_sun yr-1) at intermediate and high redshift (z >= 1.5). Such a population of objects probably is the consequence of an increase of interaction and merging activity at high redshift, but a realistic quantitative description can only be obtained through more detailed modelling of such processes. This study illustrates the implementation of multi-wavelength spectra into a semi-analytic model. In spite of its simplicity, it already provides fair fits of the current data of faint counts, and a physically motivated way of interpolating and extrapolating these data to other wavelengths and fainter flux
Negative binomial mixed models for analyzing microbiome count data.
Zhang, Xinyan; Mallick, Himel; Tang, Zaixiang; Zhang, Lei; Cui, Xiangqin; Benson, Andrew K; Yi, Nengjun
2017-01-03
Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.
Using Count Data and Ordered Models in National Forest Recreation Demand Analysis
Simões, Paula; Barata, Eduardo; Cruz, Luis
2013-11-01
This research addresses the need to improve our knowledge on the demand for national forests for recreation and offers an in-depth data analysis supported by the complementary use of count data and ordered models. From a policy-making perspective, while count data models enable the estimation of monetary welfare measures, ordered models allow for the wider use of the database and provide a more flexible analysis of data. The main purpose of this article is to analyse the individual forest recreation demand and to derive a measure of its current use value. To allow a more complete analysis of the forest recreation demand structure the econometric approach supplements the use of count data models with ordered category models using data obtained by means of an on-site survey in the Bussaco National Forest (Portugal). Overall, both models reveal that travel cost and substitute prices are important explanatory variables, visits are a normal good and demographic variables seem to have no influence on demand. In particular, estimated price and income elasticities of demand are quite low. Accordingly, it is possible to argue that travel cost (price) in isolation may be expected to have a low impact on visitation levels.
A Scan Statistic for Continuous Data Based on the Normal Probability Model
Konty, Kevin; Kulldorff, Martin; Huang, Lan
2009-01-01
Abstract Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight...
Statistical modelling with quantile functions
Gilchrist, Warren
2000-01-01
Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of quantiles in their work. Until now, however, no one put all the ideas together to form what turns out to be a general approach to statistics.Statistical Modelling with Quantile Functions does just that. It systematically examines the entire process of statistical modelling, starting with using the quantile function to define continuous distributions. The author shows that by using this approach, it becomes possible to develop complex distributional models from simple components. A modelling kit can be developed that applies to the whole model - deterministic and stochastic components - and this kit operates by adding, multiplying, and transforming distributions rather than data.Statistical Modelling with Quantile Functions adds a new dimension to the practice of stati...
A statistical analysis of count normalization methods used in positron-emission tomography
International Nuclear Information System (INIS)
Holmes, T.J.; Ficke, D.C.; Snyder, D.L.
1984-01-01
As part of the Positron-Emission Tomography (PET) reconstruction process, annihilation counts are normalized for photon absorption, detector efficiency and detector-pair duty-cycle. Several normalization methods of time-of-flight and conventional systems are analyzed mathematically for count bias and variance. The results of the study have some implications on hardware and software complexity and on image noise and distortion
A Statistical Programme Assignment Model
DEFF Research Database (Denmark)
Rosholm, Michael; Staghøj, Jonas; Svarer, Michael
When treatment effects of active labour market programmes are heterogeneous in an observable way across the population, the allocation of the unemployed into different programmes becomes a particularly important issue. In this paper, we present a statistical model designed to improve the present...... duration of unemployment spells may result if a statistical programme assignment model is introduced. We discuss several issues regarding the plementation of such a system, especially the interplay between the statistical model and case workers....
voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.
Law, Charity W; Chen, Yunshun; Shi, Wei; Smyth, Gordon K
2014-02-03
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore
2014-04-01
Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.
Determining Gate Count Reliability in a Library Setting
Jeffrey Phillips
2016-01-01
Objective – Patron counts are a common form of measurement for library assessment. To develop accurate library statistics, it is necessary to determine any differences between various counting devices. A yearlong comparison between card reader turnstiles and laser gate counters in a university library sought to offer a standard percentage of variance and provide suggestions to increase the precision of counts. Methods – The collection of library exit counts identified the differences be...
Integrating count and detection–nondetection data to model population dynamics
Zipkin, Elise F.; Rossman, Sam; Yackulic, Charles B.; Wiens, David; Thorson, James T.; Davis, Raymond J.; Grant, Evan H. Campbell
2017-01-01
There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.
Lee, J-H; Han, G; Fulp, W J; Giuliano, A R
2012-06-01
The Poisson model can be applied to the count of events occurring within a specific time period. The main feature of the Poisson model is the assumption that the mean and variance of the count data are equal. However, this equal mean-variance relationship rarely occurs in observational data. In most cases, the observed variance is larger than the assumed variance, which is called overdispersion. Further, when the observed data involve excessive zero counts, the problem of overdispersion results in underestimating the variance of the estimated parameter, and thus produces a misleading conclusion. We illustrated the use of four models for overdispersed count data that may be attributed to excessive zeros. These are Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial models. The example data in this article deal with the number of incidents involving human papillomavirus infection. The four models resulted in differing statistical inferences. The Poisson model, which is widely used in epidemiology research, underestimated the standard errors and overstated the significance of some covariates.
The Rasch Poisson counts model for incomplete data : An application of the EM algorithm
Jansen, G.G.H.
Rasch's Poisson counts model is a latent trait model for the situation in which K tests are administered to N examinees and the test score is a count [e.g., the repeated occurrence of some event, such as the number of items completed or the number of items answered (in)correctly]. The Rasch Poisson
Nathaniel E. Seavy; Suhel Quader; John D. Alexander; C. John Ralph
2005-01-01
The success of avian monitoring programs to effectively guide management decisions requires that studies be efficiently designed and data be properly analyzed. A complicating factor is that point count surveys often generate data with non-normal distributional properties. In this paper we review methods of dealing with deviations from normal assumptions, and we focus...
Examining secular trend and seasonality in count data using dynamic generalized linear modelling
DEFF Research Database (Denmark)
Lundbye-Christensen, Søren; Dethlefsen, Claus; Gorst-Rasmussen, Anders
series regression model for Poisson counts. It differs in allowing the regression coefficients to vary gradually over time in a random fashion. Data In the period January 1980 to 1999, 17,989 incidents of acute myocardial infarction were recorded in the county of Northern Jutland, Denmark. Records were......Aims Time series of incidence counts often show secular trends and seasonal patterns. We present a model for incidence counts capable of handling a possible gradual change in growth rates and seasonal patterns, serial correlation and overdispersion. Methods The model resembles an ordinary time...... updated daily. Results The model with a seasonal pattern and an approximately linear trend was fitted to the data, and diagnostic plots indicate a good model fit. The analysis with the dynamic model revealed peaks coinciding with influenza epidemics. On average the peak-to-trough ratio is estimated...
An automated approach for annual layer counting in ice cores
Winstrup, M.; Svensson, A.; Rasmussen, S. O.; Winther, O.; Steig, E.; Axelrod, A.
2012-04-01
The temporal resolution of some ice cores is sufficient to preserve seasonal information in the ice core record. In such cases, annual layer counting represents one of the most accurate methods to produce a chronology for the core. Yet, manual layer counting is a tedious and sometimes ambiguous job. As reliable layer recognition becomes more difficult, a manual approach increasingly relies on human interpretation of the available data. Thus, much may be gained by an automated and therefore objective approach for annual layer identification in ice cores. We have developed a novel method for automated annual layer counting in ice cores, which relies on Bayesian statistics. It uses algorithms from the statistical framework of Hidden Markov Models (HMM), originally developed for use in machine speech recognition. The strength of this layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on purely objective criteria for annual layer identification. With this methodology, it is possible to determine the most likely position of multiple layer boundaries in an entire section of ice core data at once. It provides a probabilistic uncertainty estimate of the resulting layer count, hence ensuring a proper treatment of ambiguous layer boundaries in the data. Furthermore multiple data series can be incorporated to be used at once, hence allowing for a full multi-parameter annual layer counting method similar to a manual approach. In this study, the automated layer counting algorithm has been applied to data from the NGRIP ice core, Greenland. The NGRIP ice core has very high temporal resolution with depth, and hence the potential to be dated by annual layer counting far back in time. In previous studies [Andersen et al., 2006; Svensson et al., 2008], manual layer counting has been carried out back to 60 kyr BP. A comparison between the counted annual layers based on the two approaches will be presented
Wan, Wai-Yin; Chan, Jennifer S K
2009-08-01
For time series of count data, correlated measurements, clustering as well as excessive zeros occur simultaneously in biomedical applications. Ignoring such effects might contribute to misleading treatment outcomes. A generalized mixture Poisson geometric process (GMPGP) model and a zero-altered mixture Poisson geometric process (ZMPGP) model are developed from the geometric process model, which was originally developed for modelling positive continuous data and was extended to handle count data. These models are motivated by evaluating the trend development of new tumour counts for bladder cancer patients as well as by identifying useful covariates which affect the count level. The models are implemented using Bayesian method with Markov chain Monte Carlo (MCMC) algorithms and are assessed using deviance information criterion (DIC).
Modeling Repeated Count Data : Some Extensions of the Rasch Poisson Counts Model
van Duijn, M.A.J.; Jansen, Margo
1995-01-01
We consider data that can be summarized as an N X K table of counts-for example, test data obtained by administering K tests to N subjects. The cell entries y(ij) are assumed to be conditionally independent Poisson-distributed random variables, given the NK Poisson intensity parameters mu(ij). The
Optimization of counting time using count statistics on a diffraction beamline
Energy Technology Data Exchange (ETDEWEB)
Marais, D., E-mail: Deon.Marais@necsa.co.za [Research and Development Division, South African Nuclear Energy Corporation (Necsa) SOC Limited, PO Box 582, Pretoria 0001 (South Africa); School of Mechanical and Nuclear Engineering, North-West University, Potchefstroom 2520 (South Africa); Venter, A.M., E-mail: Andrew.Venter@necsa.co.za [Research and Development Division, South African Nuclear Energy Corporation (Necsa) SOC Limited, PO Box 582, Pretoria 0001 (South Africa); Faculty of Agriculture Science and Technology, North-West University, Mahikeng 2790 (South Africa); Markgraaff, J., E-mail: Johan.Markgraaff@nwu.ac.za [School of Mechanical and Nuclear Engineering, North-West University, Potchefstroom 2520 (South Africa)
2016-05-11
The feasibility of an alternative data acquisition strategy to improve the efficiency of beam time usage with neutron strain scanner instruments is demonstrated. By performing strain measurements against set statistical criteria, rather than time, not only leads to substantially reduced sample investigation time but also renders data of similar quality throughout.
Statistical aspects of autoregressive-moving average models in the assessment of radon mitigation
International Nuclear Information System (INIS)
Dunn, J.E.; Henschel, D.B.
1989-01-01
Radon values, as reflected by hourly scintillation counts, seem dominated by major, pseudo-periodic, random fluctuations. This methodological paper reports a moderate degree of success in modeling these data using relatively simple autoregressive-moving average models to assess the effectiveness of radon mitigation techniques in existing housing. While accounting for the natural correlation of successive observations, familiar summary statistics such as steady state estimates, standard errors, confidence limits, and tests of hypothesis are produced. The Box-Jenkins approach is used throughout. In particular, intervention analysis provides an objective means of assessing the effectiveness of an active mitigation measure, such as a fan off/on cycle. Occasionally, failure to declare a significant intervention has suggested a means of remedial action in the data collection procedure
Freund, D.A.; Kniesner, T.J.; LoSasso, A.T.
1996-01-01
We develop a synthetic difference-in-differences statistical design to apply to experimental data for adult women living in Hennepin County, Minnesota, to estimate the impact of Medicaid managed care on various modes of medical care use.Because the outcomes of interest are utilization counts with
Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.
Yasmin, Shamsunnahar; Eluru, Naveen
2016-10-01
The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation. Copyright © 2016 Elsevier Ltd. All rights reserved.
Multimode model for projective photon-counting measurements
International Nuclear Information System (INIS)
Tualle-Brouri, Rosa; Ourjoumtsev, Alexei; Dantan, Aurelien; Grangier, Philippe; Wubs, Martijn; Soerensen, Anders S.
2009-01-01
We present a general model to account for the multimode nature of the quantum electromagnetic field in projective photon-counting measurements. We focus on photon-subtraction experiments, where non-Gaussian states are produced conditionally. These are useful states for continuous-variable quantum-information processing. We present a general method called mode reduction that reduces the multimode model to an effective two-mode problem. We apply this method to a multimode model describing broadband parametric down-conversion, thereby improving the analysis of existing experimental results. The main improvement is that spatial and frequency filters before the photon detector are taken into account explicitly. We find excellent agreement with previously published experimental results, using fewer free parameters than before, and discuss the implications of our analysis for the optimized production of states with negative Wigner functions.
Diffeomorphic Statistical Deformation Models
DEFF Research Database (Denmark)
Hansen, Michael Sass; Hansen, Mads/Fogtman; Larsen, Rasmus
2007-01-01
In this paper we present a new method for constructing diffeomorphic statistical deformation models in arbitrary dimensional images with a nonlinear generative model and a linear parameter space. Our deformation model is a modified version of the diffeomorphic model introduced by Cootes et al....... The modifications ensure that no boundary restriction has to be enforced on the parameter space to prevent folds or tears in the deformation field. For straightforward statistical analysis, principal component analysis and sparse methods, we assume that the parameters for a class of deformations lie on a linear...... with ground truth in form of manual expert annotations, and compared to Cootes's model. We anticipate applications in unconstrained diffeomorphic synthesis of images, e.g. for tracking, segmentation, registration or classification purposes....
Energy Technology Data Exchange (ETDEWEB)
Lou, K [U.T M.D. Anderson Cancer Center, Houston, TX (United States); Rice University, Houston, TX (United States); Mirkovic, D; Sun, X; Zhu, X; Poenisch, F; Grosshans, D; Shao, Y [U.T M.D. Anderson Cancer Center, Houston, TX (United States); Clark, J [Rice University, Houston, TX (United States)
2014-06-01
Purpose: To study the feasibility of intra-fraction proton beam-range verification with PET imaging. Methods: Two phantoms homogeneous cylindrical PMMA phantoms (290 mm axial length, 38 mm and 200 mm diameter respectively) were studied using PET imaging: a small phantom using a mouse-sized PET (61 mm diameter field of view (FOV)) and a larger phantom using a human brain-sized PET (300 mm FOV). Monte Carlo (MC) simulations (MCNPX and GATE) were used to simulate 179.2 MeV proton pencil beams irradiating the two phantoms and be imaged by the two PET systems. A total of 50 simulations were conducted to generate 50 positron activity distributions and correspondingly 50 measured activity-ranges. The accuracy and precision of these activity-ranges were calculated under different conditions (including count statistics and other factors, such as crystal cross-section). Separate from the MC simulations, an activity distribution measured from a simulated PET image was modeled as a noiseless positron activity distribution corrupted by Poisson counting noise. The results from these two approaches were compared to assess the impact of count statistics on the accuracy and precision of activity-range calculations. Results: MC Simulations show that the accuracy and precision of an activity-range are dominated by the number (N) of coincidence events of the reconstructed image. They are improved in a manner that is inversely proportional to 1/sqrt(N), which can be understood from the statistical modeling. MC simulations also indicate that the coincidence events acquired within the first 60 seconds with 10{sup 9} protons (small phantom) and 10{sup 10} protons (large phantom) are sufficient to achieve both sub-millimeter accuracy and precision. Conclusion: Under the current MC simulation conditions, the initial study indicates that the accuracy and precision of beam-range verification are dominated by count statistics, and intra-fraction PET image-based beam-range verification is
Developments in statistical analysis in quantitative genetics
DEFF Research Database (Denmark)
Sorensen, Daniel
2009-01-01
of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...
Beach, Shaun E.; Semkow, Thomas M.; Remling, David J.; Bradt, Clayton J.
2017-07-01
We have developed accessible methods to demonstrate fundamental statistics in several phenomena, in the context of teaching electronic signal processing in a physics-based college-level curriculum. A relationship between the exponential time-interval distribution and Poisson counting distribution for a Markov process with constant rate is derived in a novel way and demonstrated using nuclear counting. Negative binomial statistics is demonstrated as a model for overdispersion and justified by the effect of electronic noise in nuclear counting. The statistics of digital packets on a computer network are shown to be compatible with the fractal-point stochastic process leading to a power-law as well as generalized inverse Gaussian density distributions of time intervals between packets.
Medicaid Drug Claims Statistics
U.S. Department of Health & Human Services — The Medicaid Drug Claims Statistics CD is a useful tool that conveniently breaks up Medicaid claim counts and separates them by quarter and includes an annual count.
Bayesian prediction of spatial count data using generalized linear mixed models
DEFF Research Database (Denmark)
Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge
2002-01-01
Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...
Statistical modeling for degradation data
Lio, Yuhlong; Ng, Hon; Tsai, Tzong-Ru
2017-01-01
This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products’ reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.
Exclusion statistics and integrable models
International Nuclear Information System (INIS)
Mashkevich, S.
1998-01-01
The definition of exclusion statistics, as given by Haldane, allows for a statistical interaction between distinguishable particles (multi-species statistics). The thermodynamic quantities for such statistics ca be evaluated exactly. The explicit expressions for the cluster coefficients are presented. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models. The interesting questions of generalizing this correspondence onto the higher-dimensional and the multi-species cases remain essentially open
A Statistical Model for Regional Tornado Climate Studies.
Jagger, Thomas H; Elsner, James B; Widen, Holly M
2015-01-01
Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.
Statistics and geometry of cosmic voids
International Nuclear Information System (INIS)
Gaite, José
2009-01-01
We introduce new statistical methods for the study of cosmic voids, focusing on the statistics of largest size voids. We distinguish three different types of distributions of voids, namely, Poisson-like, lognormal-like and Pareto-like distributions. The last two distributions are connected with two types of fractal geometry of the matter distribution. Scaling voids with Pareto distribution appear in fractal distributions with box-counting dimension smaller than three (its maximum value), whereas the lognormal void distribution corresponds to multifractals with box-counting dimension equal to three. Moreover, voids of the former type persist in the continuum limit, namely, as the number density of observable objects grows, giving rise to lacunar fractals, whereas voids of the latter type disappear in the continuum limit, giving rise to non-lacunar (multi)fractals. We propose both lacunar and non-lacunar multifractal models of the cosmic web structure of the Universe. A non-lacunar multifractal model is supported by current galaxy surveys as well as cosmological N-body simulations. This model suggests, in particular, that small dark matter halos and, arguably, faint galaxies are present in cosmic voids
Statistical precision of delayed-neutron nondestructive assay techniques
International Nuclear Information System (INIS)
Bayne, C.K.; McNeany, S.R.
1979-02-01
A theoretical analysis of the statistical precision of delayed-neutron nondestructive assay instruments is presented. Such instruments measure the fissile content of nuclear fuel samples by neutron irradiation and delayed-neutron detection. The precision of these techniques is limited by the statistical nature of the nuclear decay process, but the precision can be optimized by proper selection of system operating parameters. Our method is a three-part analysis. We first present differential--difference equations describing the fundamental physics of the measurements. We then derive and present complete analytical solutions to these equations. Final equations governing the expected number and variance of delayed-neutron counts were computer programmed to calculate the relative statistical precision of specific system operating parameters. Our results show that Poisson statistics do not govern the number of counts accumulated in multiple irradiation-count cycles and that, in general, maximum count precision does not correspond with maximum count as first expected. Covariance between the counts of individual cycles must be considered in determining the optimum number of irradiation-count cycles and the optimum irradiation-to-count time ratio. For the assay system in use at ORNL, covariance effects are small, but for systems with short irradiation-to-count transition times, covariance effects force the optimum number of irradiation-count cycles to be half those giving maximum count. We conclude that the equations governing the expected value and variance of delayed-neutron counts have been derived in closed form. These have been computerized and can be used to select optimum operating parameters for delayed-neutron assay devices
On statistical analysis of compound point process
Czech Academy of Sciences Publication Activity Database
Volf, Petr
2006-01-01
Roč. 35, 2-3 (2006), s. 389-396 ISSN 1026-597X R&D Projects: GA ČR(CZ) GA402/04/1294 Institutional research plan: CEZ:AV0Z10750506 Keywords : counting process * compound process * hazard function * Cox -model Subject RIV: BB - Applied Statistics, Operational Research
Relationship of milking rate to somatic cell count.
Brown, C A; Rischette, S J; Schultz, L H
1986-03-01
Information on milking rate, monthly bucket somatic cell counts, mastitis treatment, and milk production was obtained from 284 lactations of Holstein cows separated into three lactation groups. Significant correlations between somatic cell count (linear score) and other parameters included production in lactation 1 (-.185), production in lactation 2 (-.267), and percent 2-min milk in lactation 2 (.251). Somatic cell count tended to increase with maximum milking rate in all lactations, but correlations were not statistically significant. Twenty-nine percent of cows with milking rate measurements were treated for clinical mastitis. Treated cows in each lactation group produced less milk than untreated cows. In the second and third lactation groups, treated cows had a shorter total milking time and a higher percent 2-min milk than untreated cows, but differences were not statistically significant. Overall, the data support the concept that faster milking cows tend to have higher cell counts and more mastitis treatments, particularly beyond first lactation. However, the magnitude of the relationship was small.
Bayesian statistical evaluation of peak area measurements in gamma spectrometry
International Nuclear Information System (INIS)
Silva, L.; Turkman, A.; Paulino, C.D.
2010-01-01
We analyze results from determinations of peak areas for a radioactive source containing several radionuclides. The statistical analysis was performed using Bayesian methods based on the usual Poisson model for observed counts. This model does not appear to be a very good assumption for the counting system under investigation, even though it is not questioned as a whole by the inferential procedures adopted. We conclude that, in order to avoid incorrect inferences on relevant quantities, one must proceed to a further study that allows us to include missing influence parameters and to select a model explaining the observed data much better.
Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Halyo, Nesim; Choi, Sang H.
1987-01-01
Two count conversion algorithms and the associated dynamic sensor model for the M/WFOV nonscanner radiometers are defined. The sensor model provides and updates the constants necessary for the conversion algorithms, though the frequency with which these updates were needed was uncertain. This analysis therefore develops mathematical models for the conversion of irradiance at the sensor field of view (FOV) limiter into data counts, derives from this model two algorithms for the conversion of data counts to irradiance at the sensor FOV aperture and develops measurement models which account for a specific target source together with a sensor. The resulting algorithms are of the gain/offset and Kalman filter types. The gain/offset algorithm was chosen since it provided sufficient accuracy using simpler computations.
Hanford whole body counting manual
International Nuclear Information System (INIS)
Palmer, H.E.; Rieksts, G.A.; Lynch, T.P.
1990-06-01
This document describes the Hanford Whole Body Counting Program as it is administered by Pacific Northwest Laboratory (PNL) in support of the US Department of Energy--Richland Operations Office (DOE-RL) and its Hanford contractors. Program services include providing in vivo measurements of internally deposited radioactivity in Hanford employees (or visitors). Specific chapters of this manual deal with the following subjects: program operational charter, authority, administration, and practices, including interpreting applicable DOE Orders, regulations, and guidance into criteria for in vivo measurement frequency, etc., for the plant-wide whole body counting services; state-of-the-art facilities and equipment used to provide the best in vivo measurement results possible for the approximately 11,000 measurements made annually; procedures for performing the various in vivo measurements at the Whole Body Counter (WBC) and related facilities including whole body counts; operation and maintenance of counting equipment, quality assurance provisions of the program, WBC data processing functions, statistical aspects of in vivo measurements, and whole body counting records and associated guidance documents. 16 refs., 48 figs., 22 tabs
Do we need statistics when we have linguistics?
Directory of Open Access Journals (Sweden)
Cantos Gómez Pascual
2002-01-01
Full Text Available Statistics is known to be a quantitative approach to research. However, most of the research done in the fields of language and linguistics is of a different kind, namely qualitative. Succinctly, qualitative analysis differs from quantitative analysis is that in the former no attempt is made to assign frequencies, percentages and the like, to the linguistic features found or identified in the data. In quantitative research, linguistic features are classified and counted, and even more complex statistical models are constructed in order to explain these observed facts. In qualitative research, however, we use the data only for identifying and describing features of language usage and for providing real occurrences/examples of particular phenomena. In this paper, we shall try to show how quantitative methods and statistical techniques can supplement qualitative analyses of language. We shall attempt to present some mathematical and statistical properties of natural languages, and introduce some of the quantitative methods which are of the most value in working empirically with texts and corpora, illustrating the various issues with numerous examples and moving from the most basic descriptive techniques (frequency counts and percentages to decision-taking techniques (chi-square and z-score and to more sophisticated statistical language models (Type-Token/Lemma-Token/Lemma-Type formulae, cluster analysis and discriminant function analysis.
Time Evolving Fission Chain Theory and Fast Neutron and Gamma-Ray Counting Distributions
International Nuclear Information System (INIS)
Kim, K. S.; Nakae, L. F.; Prasad, M. K.; Snyderman, N. J.; Verbeke, J. M.
2015-01-01
Here, we solve a simple theoretical model of time evolving fission chains due to Feynman that generalizes and asymptotically approaches the point model theory. The point model theory has been used to analyze thermal neutron counting data. This extension of the theory underlies fast counting data for both neutrons and gamma rays from metal systems. Fast neutron and gamma-ray counting is now possible using liquid scintillator arrays with nanosecond time resolution. For individual fission chains, the differential equations describing three correlated probability distributions are solved: the time-dependent internal neutron population, accumulation of fissions in time, and accumulation of leaked neutrons in time. Explicit analytic formulas are given for correlated moments of the time evolving chain populations. The equations for random time gate fast neutron and gamma-ray counting distributions, due to randomly initiated chains, are presented. Correlated moment equations are given for both random time gate and triggered time gate counting. There are explicit formulas for all correlated moments are given up to triple order, for all combinations of correlated fast neutrons and gamma rays. The nonlinear differential equations for probabilities for time dependent fission chain populations have a remarkably simple Monte Carlo realization. A Monte Carlo code was developed for this theory and is shown to statistically realize the solutions to the fission chain theory probability distributions. Combined with random initiation of chains and detection of external quanta, the Monte Carlo code generates time tagged data for neutron and gamma-ray counting and from these data the counting distributions.
Use of Statistics for Data Evaluation in Environmental Radioactivity Measurements
International Nuclear Information System (INIS)
Sutarman
2001-01-01
Counting statistics will give a correction on environmental radioactivity measurement result. Statistics provides formulas to determine standard deviation (S B ) and minimum detectable concentration (MDC) according to the Poisson distribution. Both formulas depend on the background count rate, counting time, counting efficiency, gamma intensity, and sample size. A long time background counting results in relatively low S B and MDC that can present relatively accurate measurement results. (author)
Determining Gate Count Reliability in a Library Setting
Directory of Open Access Journals (Sweden)
Jeffrey Phillips
2016-09-01
Full Text Available Objective – Patron counts are a common form of measurement for library assessment. To develop accurate library statistics, it is necessary to determine any differences between various counting devices. A yearlong comparison between card reader turnstiles and laser gate counters in a university library sought to offer a standard percentage of variance and provide suggestions to increase the precision of counts. Methods – The collection of library exit counts identified the differences between turnstile and laser gate counter data. Statistical software helped to eliminate any inaccuracies in the collection of turnstile data, allowing this data set to be the base for comparison. Collection intervals were randomly determined and demonstrated periods of slow, average, and heavy traffic. Results – After analyzing 1,039,766 patron visits throughout a year, the final totals only showed a difference of .43% (.0043 between the two devices. The majority of collection periods did not exceed a difference of 3% between the counting instruments. Conclusion – Turnstiles card readers and laser gate counters provide similar levels of reliability when measuring patron activity. Each system has potential counting inaccuracies, but several methods exist to create more precise totals. Turnstile card readers are capable of offering greater detail involving patron identity, but their high cost makes them inaccessible for libraries with lower budgets. This makes laser gate counters an affordable alternative for reliable patron counting in an academic library.
A Statistical Model for Regional Tornado Climate Studies.
Directory of Open Access Journals (Sweden)
Thomas H Jagger
Full Text Available Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA. A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.
VSRR Provisional Drug Overdose Death Counts
U.S. Department of Health & Human Services — This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. National...
Classical model of intermediate statistics
International Nuclear Information System (INIS)
Kaniadakis, G.
1994-01-01
In this work we present a classical kinetic model of intermediate statistics. In the case of Brownian particles we show that the Fermi-Dirac (FD) and Bose-Einstein (BE) distributions can be obtained, just as the Maxwell-Boltzmann (MD) distribution, as steady states of a classical kinetic equation that intrinsically takes into account an exclusion-inclusion principle. In our model the intermediate statistics are obtained as steady states of a system of coupled nonlinear kinetic equations, where the coupling constants are the transmutational potentials η κκ' . We show that, besides the FD-BE intermediate statistics extensively studied from the quantum point of view, we can also study the MB-FD and MB-BE ones. Moreover, our model allows us to treat the three-state mixing FD-MB-BE intermediate statistics. For boson and fermion mixing in a D-dimensional space, we obtain a family of FD-BE intermediate statistics by varying the transmutational potential η BF . This family contains, as a particular case when η BF =0, the quantum statistics recently proposed by L. Wu, Z. Wu, and J. Sun [Phys. Lett. A 170, 280 (1992)]. When we consider the two-dimensional FD-BE statistics, we derive an analytic expression of the fraction of fermions. When the temperature T→∞, the system is composed by an equal number of bosons and fermions, regardless of the value of η BF . On the contrary, when T=0, η BF becomes important and, according to its value, the system can be completely bosonic or fermionic, or composed both by bosons and fermions
Energy Technology Data Exchange (ETDEWEB)
Cheng, Z; Zheng, X; Deen, J; Peng, H [McMaster University, Hamilton, ON (Canada); Xing, L [Stanford University School of Medicine, Stanford, CA (United States)
2016-06-15
Purpose: Silicon photomultiplier (SiPM) has recently emerged as a promising photodetector for biomedical imaging applications. Due to its high multiplication gain (comparable to PMT), fast timing, low cost and compactness, it is considered a good candidate for photon counting CT. Dark noise is a limiting factor which impacts both energy resolution and detection dynamic range. Our goal is to develop a comprehensive model for noise sources for SiPM sensors. Methods: The physical parameters used in this work were based upon a test SPAD fabricated in 130nm CMOS process. The SPAD uses an n+/p-well junction, which is isolated from the p-substrate by a deep n-well junction. Inter-avalanche time measurement was used to record the time interval between two adjacent avalanche pulses. After collecting 1×106 counts, the histogram was obtained and multiple exponential fitting process was used to extract the lifetime associated with the traps within the bandgap. Results: At room temperature, the breakdown voltage of the SPAD is ∼11.4V and shows a temperature coefficient of 7.7mV/°C. The dark noise of SPAD increases with both the excess biasing voltage and temperature. The primary dark counts from the model were validated against the measurement results. A maximum relative error of 8.7% is observed at 20 °C with an excess voltage of 0.5V. The probabilities of after-pulsing are found to be dependent of both temperature and excess voltage. With 0.5V excess voltage, the after-pulsing probability is 63.5% at - 30 °C and drops to ∼6.6% at 40 °C. Conclusion: A comprehensive noise model for SPAD sensor was proposed. The model takes into account of static, dynamic and statistical behavior of SPADs. We believe that this is the first SPAD circuit simulation model that includes the band-to-band tunneling dark noise contribution and temporal dependence of the after-pulsing probability.
Probing NWP model deficiencies by statistical postprocessing
DEFF Research Database (Denmark)
Rosgaard, Martin Haubjerg; Nielsen, Henrik Aalborg; Nielsen, Torben S.
2016-01-01
The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational....... Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites....
Statistical Model of Extreme Shear
DEFF Research Database (Denmark)
Hansen, Kurt Schaldemose; Larsen, Gunner Chr.
2005-01-01
In order to continue cost-optimisation of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate...
Equivalence of truncated count mixture distributions and mixtures of truncated count distributions.
Böhning, Dankmar; Kuhnert, Ronny
2006-12-01
This article is about modeling count data with zero truncation. A parametric count density family is considered. The truncated mixture of densities from this family is different from the mixture of truncated densities from the same family. Whereas the former model is more natural to formulate and to interpret, the latter model is theoretically easier to treat. It is shown that for any mixing distribution leading to a truncated mixture, a (usually different) mixing distribution can be found so that the associated mixture of truncated densities equals the truncated mixture, and vice versa. This implies that the likelihood surfaces for both situations agree, and in this sense both models are equivalent. Zero-truncated count data models are used frequently in the capture-recapture setting to estimate population size, and it can be shown that the two Horvitz-Thompson estimators, associated with the two models, agree. In particular, it is possible to achieve strong results for mixtures of truncated Poisson densities, including reliable, global construction of the unique NPMLE (nonparametric maximum likelihood estimator) of the mixing distribution, implying a unique estimator for the population size. The benefit of these results lies in the fact that it is valid to work with the mixture of truncated count densities, which is less appealing for the practitioner but theoretically easier. Mixtures of truncated count densities form a convex linear model, for which a developed theory exists, including global maximum likelihood theory as well as algorithmic approaches. Once the problem has been solved in this class, it might readily be transformed back to the original problem by means of an explicitly given mapping. Applications of these ideas are given, particularly in the case of the truncated Poisson family.
Aspects of statistical model for multifragmentation
International Nuclear Information System (INIS)
Bhattacharyya, P.; Das Gupta, S.; Mekjian, A. Z.
1999-01-01
We deal with two different aspects of an exactly soluble statistical model of fragmentation. First we show, using zero range force and finite temperature Thomas-Fermi theory, that a common link can be found between finite temperature mean field theory and the statistical fragmentation model. We show the latter naturally arises in the spinodal region. Next we show that although the exact statistical model is a canonical model and uses temperature, microcanonical results which use constant energy rather than constant temperature can also be obtained from the canonical model using saddle-point approximation. The methodology is extremely simple to implement and at least in all the examples studied in this work is very accurate. (c) 1999 The American Physical Society
Statistical Compression for Climate Model Output
Hammerling, D.; Guinness, J.; Soh, Y. J.
2017-12-01
Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus is it important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset-one year of daily mean temperature data-particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.
Automated statistical modeling of analytical measurement systems
International Nuclear Information System (INIS)
Jacobson, J.J.
1992-01-01
The statistical modeling of analytical measurement systems at the Idaho Chemical Processing Plant (ICPP) has been completely automated through computer software. The statistical modeling of analytical measurement systems is one part of a complete quality control program used by the Remote Analytical Laboratory (RAL) at the ICPP. The quality control program is an integration of automated data input, measurement system calibration, database management, and statistical process control. The quality control program and statistical modeling program meet the guidelines set forth by the American Society for Testing Materials and American National Standards Institute. A statistical model is a set of mathematical equations describing any systematic bias inherent in a measurement system and the precision of a measurement system. A statistical model is developed from data generated from the analysis of control standards. Control standards are samples which are made up at precise known levels by an independent laboratory and submitted to the RAL. The RAL analysts who process control standards do not know the values of those control standards. The object behind statistical modeling is to describe real process samples in terms of their bias and precision and, to verify that a measurement system is operating satisfactorily. The processing of control standards gives us this ability
High Reproducibility of ELISPOT Counts from Nine Different Laboratories
Directory of Open Access Journals (Sweden)
Srividya Sundararaman
2015-01-01
Full Text Available The primary goal of immune monitoring with ELISPOT is to measure the number of T cells, specific for any antigen, accurately and reproducibly between different laboratories. In ELISPOT assays, antigen-specific T cells secrete cytokines, forming spots of different sizes on a membrane with variable background intensities. Due to the subjective nature of judging maximal and minimal spot sizes, different investigators come up with different numbers. This study aims to determine whether statistics-based, automated size-gating can harmonize the number of spot counts calculated between different laboratories. We plated PBMC at four different concentrations, 24 replicates each, in an IFN-γ ELISPOT assay with HCMV pp65 antigen. The ELISPOT plate, and an image file of the plate was counted in nine different laboratories using ImmunoSpot® Analyzers by (A Basic Count™ relying on subjective counting parameters set by the respective investigators and (B SmartCount™, an automated counting protocol by the ImmunoSpot® Software that uses statistics-based spot size auto-gating with spot intensity auto-thresholding. The average coefficient of variation (CV for the mean values between independent laboratories was 26.7% when counting with Basic Count™, and 6.7% when counting with SmartCount™. Our data indicates that SmartCount™ allows harmonization of counting ELISPOT results between different laboratories and investigators.
Comment on: 'A Poisson resampling method for simulating reduced counts in nuclear medicine images'.
de Nijs, Robin
2015-07-21
In order to be able to calculate half-count images from already acquired data, White and Lawson published their method based on Poisson resampling. They verified their method experimentally by measurements with a Co-57 flood source. In this comment their results are reproduced and confirmed by a direct numerical simulation in Matlab. Not only Poisson resampling, but also two direct redrawing methods were investigated. Redrawing methods were based on a Poisson and a Gaussian distribution. Mean, standard deviation, skewness and excess kurtosis half-count/full-count ratios were determined for all methods, and compared to the theoretical values for a Poisson distribution. Statistical parameters showed the same behavior as in the original note and showed the superiority of the Poisson resampling method. Rounding off before saving of the half count image had a severe impact on counting statistics for counts below 100. Only Poisson resampling was not affected by this, while Gaussian redrawing was less affected by it than Poisson redrawing. Poisson resampling is the method of choice, when simulating half-count (or less) images from full-count images. It simulates correctly the statistical properties, also in the case of rounding off of the images.
Statistical modelling for ship propulsion efficiency
DEFF Research Database (Denmark)
Petersen, Jóan Petur; Jacobsen, Daniel J.; Winther, Ole
2012-01-01
This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks...
Statistical distribution for generalized ideal gas of fractional-statistics particles
International Nuclear Information System (INIS)
Wu, Y.
1994-01-01
We derive the occupation-number distribution in a generalized ideal gas of particles obeying fractional statistics, including mutual statistics, by adopting a state-counting definition. When there is no mutual statistics, the statistical distribution interpolates between bosons and fermions, and respects a fractional exclusion principle (except for bosons). Anyons in a strong magnetic field at low temperatures constitute such a physical system. Applications to the thermodynamic properties of quasiparticle excitations in the Laughlin quantum Hall fluid are discussed
Method of inspecting Raschig rings by neutron absorption counting
International Nuclear Information System (INIS)
Morris, R.N.; Murri, R.L.; Hume, M.W.
1979-01-01
A neutron counting method for inspecting borosilicate glass Raschig rings and an apparatus designed specifically for this method are discussed. The neutron count ratios for rings of a given thickness show a linear correlation to the boron oxide content of the rings. The count ratio also has a linear relationship to the thickness of rings of a given boron oxide content. Consequently, the experimentally-determined count ratio and physically-measured thickness of Raschig rings can be used to statistically predict their boron oxide content and determine whether or not they meet quality control acceptance criteria
Statistical inference for a class of multivariate negative binomial distributions
DEFF Research Database (Denmark)
Rubak, Ege Holger; Møller, Jesper; McCullagh, Peter
This paper considers statistical inference procedures for a class of models for positively correlated count variables called α-permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Title V Permitting Statistics Inventory
U.S. Environmental Protection Agency — The Title V Permitting Statistics Inventory contains measured and estimated nationwide statistical data, consisting of counts of permitted sources, types of permits...
Sensometrics: Thurstonian and Statistical Models
DEFF Research Database (Denmark)
Christensen, Rune Haubo Bojesen
. sensR is a package for sensory discrimination testing with Thurstonian models and ordinal supports analysis of ordinal data with cumulative link (mixed) models. While sensR is closely connected to the sensometrics field, the ordinal package has developed into a generic statistical package applicable......This thesis is concerned with the development and bridging of Thurstonian and statistical models for sensory discrimination testing as applied in the scientific discipline of sensometrics. In sensory discrimination testing sensory differences between products are detected and quantified by the use...... and sensory discrimination testing in particular in a series of papers by advancing Thurstonian models for a range of sensory discrimination protocols in addition to facilitating their application by providing software for fitting these models. The main focus is on identifying Thurstonian models...
Statistical modelling for social researchers principles and practice
Tarling, Roger
2008-01-01
This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-l...
A count rate model for PET and its application to an LSO HR PLUS scanner
International Nuclear Information System (INIS)
Moisan, C.; Rogers, J.G.; Douglas, J.L.
1996-10-01
We present a count rate model for PET. Considering a standard 20 x 20 cm phantom in the field-of-view of a cylindrical septaless tomograph, the model computes the acceptance to prompt and random events from simple geometric considerations. Dead time factors at all stages of a typical event acquisition architecture are calculated from specified processing clock cycles. Validations of the model's predictions against the measured performances of the ECAT-953B and the EXACT HR PLUS are presented. The model is then used to investigate the benefit of using detectors made of LSO in the EXACT HR PLUS scanner geometry. The results indicate that in replacing BGO by the faster LSO, one can count on an increase of the peak noise-equivalent-count rate by a factor 2.2. This gain will be achieved by using a 5 nsec coincidence window, buckets operating on 128 nsec clock cycle, and front-end data acquisition that can sustain a total rate of 2.9 MHz. (authors)
Topology for statistical modeling of petascale data.
Energy Technology Data Exchange (ETDEWEB)
Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)
2011-07-01
This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.
Bayesian models: A statistical primer for ecologists
Hobbs, N. Thompson; Hooten, Mevin B.
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models
Liu, Lian; Zhang, Shao-Wu; Huang, Yufei; Meng, Jia
2017-08-31
As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m 1 A-Seq, Par-CLIP, RIP-Seq, etc.
Statistical Model-Based Face Pose Estimation
Institute of Scientific and Technical Information of China (English)
GE Xinliang; YANG Jie; LI Feng; WANG Huahua
2007-01-01
A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.
Kassahun, Wondwosen; Neyens, Thomas; Molenberghs, Geert; Faes, Christel; Verbeke, Geert
2014-11-10
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, and public health. Such data are often characterized by the following three features. First, correlation due to the repeated measures is usually accounted for using subject-specific random effects, which are assumed to be normally distributed. Second, the sample variance may exceed the mean, and hence, the theoretical mean-variance relationship is violated, leading to overdispersion. This is usually allowed for based on a hierarchical approach, combining a Poisson model with gamma distributed random effects. Third, an excess of zeros beyond what standard count distributions can predict is often handled by either the hurdle or the zero-inflated model. A zero-inflated model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated-at-zero count distribution is used for the non-zero state. In practice, however, all these three features can appear simultaneously. Hence, a modeling framework that incorporates all three is necessary, and this presents challenges for the data analysis. Such models, when conditionally specified, will naturally have a subject-specific interpretation. However, adopting their purposefully modified marginalized versions leads to a direct marginal or population-averaged interpretation for parameter estimates of covariate effects, which is the primary interest in many applications. In this paper, we present a marginalized hurdle model and a marginalized zero-inflated model for correlated and overdispersed count data with excess zero observations and then illustrate these further with two case studies. The first dataset focuses on the Anopheles mosquito density around a hydroelectric dam, while adolescents' involvement in work, to earn money and support their families or themselves, is
Simple statistical model for branched aggregates
DEFF Research Database (Denmark)
Lemarchand, Claire; Hansen, Jesper Schmidt
2015-01-01
, given that it already has bonds with others. The model is applied here to asphaltene nanoaggregates observed in molecular dynamics simulations of Cooee bitumen. The variation with temperature of the probabilities deduced from this model is discussed in terms of statistical mechanics arguments....... The relevance of the statistical model in the case of asphaltene nanoaggregates is checked by comparing the predicted value of the probability for one molecule to have exactly i bonds with the same probability directly measured in the molecular dynamics simulations. The agreement is satisfactory......We propose a statistical model that can reproduce the size distribution of any branched aggregate, including amylopectin, dendrimers, molecular clusters of monoalcohols, and asphaltene nanoaggregates. It is based on the conditional probability for one molecule to form a new bond with a molecule...
Sempa, Joseph B; Ujeneza, Eva L; Nieuwoudt, Martin
2017-01-01
In Sub-Saharan African (SSA) resource limited settings, Cluster of Differentiation 4 (CD4) counts continue to be used for clinical decision making in antiretroviral therapy (ART). Here, HIV-infected people often remain with CD4 counts immunological monitoring is necessary. Due to varying statistical modeling methods comparing immune response to ART across different cohorts is difficult. We systematically review such models and detail the similarities, differences and problems. 'Preferred Reporting Items for Systematic Review and Meta-Analyses' guidelines were used. Only studies of immune-response after ART initiation from SSA in adults were included. Data was extracted from each study and tabulated. Outcomes were categorized into 3 groups: 'slope', 'survival', and 'asymptote' models. Wordclouds were drawn wherein the frequency of variables occurring in the reviewed models is indicated by their size and color. 69 covariates were identified in the final models of 35 studies. Effect sizes of covariates were not directly quantitatively comparable in view of the combination of differing variables and scale transformation methods across models. Wordclouds enabled the identification of qualitative and semi-quantitative covariate sets for each outcome category. Comparison across categories identified sex, baseline age, baseline log viral load, baseline CD4, ART initiation regimen and ART duration as a minimal consensus set. Most models were different with respect to covariates included, variable transformations and scales, model assumptions, modelling strategies and reporting methods, even for the same outcomes. To enable comparison across cohorts, statistical models would benefit from the application of more uniform modelling techniques. Historic efforts have produced results that are anecdotal to individual cohorts only. This study was able to define 'prior' knowledge in the Bayesian sense. Such information has value for prospective modelling efforts.
Accuracy in activation analysis: count rate effects
International Nuclear Information System (INIS)
Lindstrom, R.M.; Fleming, R.F.
1980-01-01
The accuracy inherent in activation analysis is ultimately limited by the uncertainty of counting statistics. When careful attention is paid to detail, several workers have shown that all systematic errors can be reduced to an insignificant fraction of the total uncertainty, even when the statistical limit is well below one percent. A matter of particular importance is the reduction of errors due to high counting rate. The loss of counts due to random coincidence (pulse pileup) in the amplifier and to digitization time in the ADC may be treated as a series combination of extending and non-extending dead times, respectively. The two effects are experimentally distinct. Live timer circuits in commercial multi-channel analyzers compensate properly for ADC dead time for long-lived sources, but not for pileup. Several satisfactory solutions are available, including pileup rejection and dead time correction circuits, loss-free ADCs, and computed corrections in a calibrated system. These methods are sufficiently reliable and well understood that a decaying source can be measured routinely with acceptably small errors at a dead time as high as 20 percent
1981-10-01
Two statistical procedures have been developed to estimate hourly or daily aircraft counts. These counts can then be transformed into estimates of instantaneous air counts. The first procedure estimates the stable (deterministic) mean level of hourly...
Matrix Tricks for Linear Statistical Models
Puntanen, Simo; Styan, George PH
2011-01-01
In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and
Statistical Model Checking of Rich Models and Properties
DEFF Research Database (Denmark)
Poulsen, Danny Bøgsted
in undecidability issues for the traditional model checking approaches. Statistical model checking has proven itself a valuable supplement to model checking and this thesis is concerned with extending this software validation technique to stochastic hybrid systems. The thesis consists of two parts: the first part...... motivates why existing model checking technology should be supplemented by new techniques. It also contains a brief introduction to probability theory and concepts covered by the six papers making up the second part. The first two papers are concerned with developing online monitoring techniques...... systems. The fifth paper shows how stochastic hybrid automata are useful for modelling biological systems and the final paper is concerned with showing how statistical model checking is efficiently distributed. In parallel with developing the theory contained in the papers, a substantial part of this work...
Statistical Modelling of Wind Proles - Data Analysis and Modelling
DEFF Research Database (Denmark)
Jónsson, Tryggvi; Pinson, Pierre
The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....
Interpretation of galaxy counts
International Nuclear Information System (INIS)
Tinsely, B.M.
1980-01-01
New models are presented for the interpretation of recent counts of galaxies to 24th magnitude, and predictions are shown to 28th magnitude for future comparison with data from the Space Telescope. The results supersede earlier, more schematic models by the author. Tyson and Jarvis found in their counts a ''local'' density enhancement at 17th magnitude, on comparison with the earlier models; the excess is no longer significant when a more realistic mixture of galaxy colors is used. Bruzual and Kron's conclusion that Kron's counts show evidence for evolution at faint magnitudes is confirmed, and it is predicted that some 23d magnitude galaxies have redshifts greater than unity. These may include spheroidal systems, elliptical galaxies, and the bulges of early-type spirals and S0's, seen during their primeval rapid star formation
A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.
Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep
2017-01-01
The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.
Statistical physics of pairwise probability models
DEFF Research Database (Denmark)
Roudi, Yasser; Aurell, Erik; Hertz, John
2009-01-01
(dansk abstrakt findes ikke) Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data......: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying...
Full counting statistics in a serially coupled double quantum dot system with spin-orbit coupling
Wang, Qiang; Xue, Hai-Bin; Xie, Hai-Qing
2018-04-01
We study the full counting statistics of electron transport through a serially coupled double quantum dot (QD) system with spin-orbit coupling (SOC) weakly coupled to two electrodes. We demonstrate that the spin polarizations of the source and drain electrodes determine whether the shot noise maintains super-Poissonian distribution, and whether the sign transitions of the skewness from positive to negative values and of the kurtosis from negative to positive values take place. In particular, the interplay between the spin polarizations of the source and drain electrodes and the magnitude of the external magnetic field, can give rise to a gate-voltage-tunable strong negative differential conductance (NDC) and the shot noise in this NDC region is significantly enhanced. Importantly, for a given SOC parameter, the obvious variation of the high-order current cumulants as a function of the energy-level detuning in a certain range, especially the dip position of the Fano factor of the skewness can be used to qualitatively extract the information about the magnitude of the SOC.
Change-Point Methods for Overdispersed Count Data
National Research Council Canada - National Science Library
Wilken, Brian A
2007-01-01
.... Although the Poisson model is often used to model count data, the two-parameter gamma-Poisson mixture parameterization of the negative binomial distribution is often a more adequate model for overdispersed count data...
Energy Technology Data Exchange (ETDEWEB)
Xue Haibin, E-mail: xhb98326110@163.co [Institute of Theoretical Physics, Shanxi University, Taiyuan, Shanxi 030006 (China); Nie, Y.-H., E-mail: nieyh@sxu.edu.c [Institute of Theoretical Physics, Shanxi University, Taiyuan, Shanxi 030006 (China); Li, Z.-J.; Liang, J.-Q. [Institute of Theoretical Physics, Shanxi University, Taiyuan, Shanxi 030006 (China)
2011-01-17
We study the full counting statistics (FCS) in a single-molecule magnet (SMM) with finite Coulomb interaction U. For finite U the FCS, differing from U{yields}{infinity}, shows a symmetric gate-voltage-dependence when the coupling strengths with two electrodes are interchanged, which can be observed experimentally just by reversing the bias-voltage. Moreover, we find that the effect of finite U on shot noise depends on the internal level structure of the SMM and the coupling asymmetry of the SMM with two electrodes as well. When the coupling of the SMM with the incident-electrode is stronger than that with the outgoing-electrode, the super-Poissonian shot noise in the sequential tunneling regime appears under relatively small gate-voltage and relatively large finite U, and dose not for U{yields}{infinity}; while it occurs at relatively large gate-voltage for the opposite coupling case. The formation mechanism of super-Poissonian shot noise can be qualitatively attributed to the competition between fast and slow transport channels.
International Nuclear Information System (INIS)
Xue Haibin; Nie, Y.-H.; Li, Z.-J.; Liang, J.-Q.
2011-01-01
We study the full counting statistics (FCS) in a single-molecule magnet (SMM) with finite Coulomb interaction U. For finite U the FCS, differing from U→∞, shows a symmetric gate-voltage-dependence when the coupling strengths with two electrodes are interchanged, which can be observed experimentally just by reversing the bias-voltage. Moreover, we find that the effect of finite U on shot noise depends on the internal level structure of the SMM and the coupling asymmetry of the SMM with two electrodes as well. When the coupling of the SMM with the incident-electrode is stronger than that with the outgoing-electrode, the super-Poissonian shot noise in the sequential tunneling regime appears under relatively small gate-voltage and relatively large finite U, and dose not for U→∞; while it occurs at relatively large gate-voltage for the opposite coupling case. The formation mechanism of super-Poissonian shot noise can be qualitatively attributed to the competition between fast and slow transport channels.
Stamm, John W.; Long, D. Leann; Kincade, Megan E.
2012-01-01
Over the past five to ten years, zero-inflated count regression models have been increasingly applied to the analysis of dental caries indices (e.g., DMFT, dfms, etc). The main reason for that is linked to the broad decline in children’s caries experience, such that dmf and DMF indices more frequently generate low or even zero counts. This article specifically reviews the application of zero-inflated Poisson and zero-inflated negative binomial regression models to dental caries, with emphasis on the description of the models and the interpretation of fitted model results given the study goals. The review finds that interpretations provided in the published caries research are often imprecise or inadvertently misleading, particularly with respect to failing to discriminate between inference for the class of susceptible persons defined by such models and inference for the sampled population in terms of overall exposure effects. Recommendations are provided to enhance the use as well as the interpretation and reporting of results of count regression models when applied to epidemiological studies of dental caries. PMID:22710271
Precise method for correcting count-rate losses in scintillation cameras
International Nuclear Information System (INIS)
Madsen, M.T.; Nickles, R.J.
1986-01-01
Quantitative studies performed with scintillation detectors often require corrections for lost data because of the finite resolving time of the detector. Methods that monitor losses by means of a reference source or pulser have unacceptably large statistical fluctuations associated with their correction factors. Analytic methods that model the detector as a paralyzable system require an accurate estimate of the system resolving time. Because the apparent resolving time depends on many variables, including the window setting, source distribution, and the amount of scattering material, significant errors can be introduced by relying on a resolving time obtained from phantom measurements. These problems can be overcome by curve-fitting the data from a reference source to a paralyzable model in which the true total count rate in the selected window is estimated from the observed total rate. The resolving time becomes a free parameter in this method which is optimized to provide the best fit to the observed reference data. The fitted curve has the inherent accuracy of the reference source method with the precision associated with the observed total image count rate. Correction factors can be simply calculated from the ratio of the true reference source rate and the fitted curve. As a result, the statistical uncertainty of the data corrected by this method is not significantly increased
Testa, Francesco; Marano, Giuseppe; Ambrogi, Federico; Boracchi, Patrizia; Casula, Antonio; Biganzoli, Elia; Moroni, Paolo
2017-10-01
Elevated bulk tank milk somatic cell count (BMSCC) has a negative impact on milk production, milk quality, and animal health. Seasonal increases in herd level somatic cell count (SCC) are commonly associated with elevated environmental temperature and humidity. The Temperature Humidity Index (THI) has been developed to measure general environmental stress in dairy cattle; however, additional work is needed to determine a specific effect of the heat stress index on herd-level SCC. Generalized Additive Model methods were used for a flexible exploration of the relationships between daily temperature, relative humidity, and bulk milk somatic cell count. The data consist of BMSCC and meteorological recordings collected between March 2009 and October 2011 of 10 dairy farms. The results indicate that, an average increase of 0.16% of BMSCC is expected for an increase of 1°C degree of temperature. A complex relationship was found for relative humidity. For example, increase of 0.099%, 0.037% and 0.020% are expected in correspondence to an increase of relative humidity from 50% to 51%, 80% to 81%; and 90% to 91%, respectively. Using this model, it will be possible to provide evidence-based advice to dairy farmers for the use of THI control charts created on the basis of our statistical model. Copyright © 2017 Elsevier Ltd. All rights reserved.
Baró, Jordi; Davidsen, Jörn
2018-03-01
The hypothesis of critical failure relates the presence of an ultimate stability point in the structural constitutive equation of materials to a divergence of characteristic scales in the microscopic dynamics responsible for deformation. Avalanche models involving critical failure have determined common universality classes for stick-slip processes and fracture. However, not all empirical failure processes exhibit the trademarks of criticality. The rheological properties of materials introduce dissipation, usually reproduced in conceptual models as a hardening of the coarse grained elements of the system. Here, we investigate the effects of transient hardening on (i) the activity rate and (ii) the statistical properties of avalanches. We find the explicit representation of transient hardening in the presence of generalized viscoelasticity and solve the corresponding mean-field model of fracture. In the quasistatic limit, the accelerated energy release is invariant with respect to rheology and the avalanche propagation can be reinterpreted in terms of a stochastic counting process. A single universality class can be defined from such analogy, and all statistical properties depend only on the distance to criticality. We also prove that interevent correlations emerge due to the hardening—even in the quasistatic limit—that can be interpreted as "aftershocks" and "foreshocks."
Accounting for Zero Inflation of Mussel Parasite Counts Using Discrete Regression Models
Directory of Open Access Journals (Sweden)
Emel Çankaya
2017-06-01
Full Text Available In many ecological applications, the absences of species are inevitable due to either detection faults in samples or uninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice for modelling such data is regression modelling of log(abundance+1 and it is well know that resulting model is inadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflated regression (ZIP and ZINB, Hurdle-Poisson (HP and Hurdle-Negative Binomial (HNB amongst others are widely preferred to the classical regression models. Due to the fact that mussels are one of the economically most important aquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four models in determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasite harming the existence of Mediterranean mussels (Mytilus galloprovincialis L.. The data collected from the three coastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued and model comparisons were based on information criterion. The results showed that the probability of the occurrence of this parasite is here best formulated by ZINB or HNB models and influential factors of models were found to be correspondent with ecological differences of the regions.
A generalized model for coincidence counting
International Nuclear Information System (INIS)
Lu, Ming-Shih; Teichmann, T.
1992-01-01
The aim of this paper is to provide a description of the multiplicative processes associated with coincidence counting techniques, for example in the NDA of plutonium bearing materials. The model elucidates both the physical processes and the underlying mathematical formalism in a relatively simple but comprehensive way. In particular, it includes the effect of absorption by impurities or poisons, as well as that of neutron leakage on a parallel basis to the treatment of induced fission itself. The work thus parallels and generalizes the methods of Boehnel of Hage and Cifarelli, and more recently of Yanjushkin. This paper introduces the concept of a dual probability generating function to account for both the basic physical multiplication phenomena, as well as the detection phenomena. The underlying approach extends the idea of a simple probability generating function, due to De Moivre. The basic mathematical background may be found, for example, in Feller 1966
A new model for the simplification of particle counting data
Directory of Open Access Journals (Sweden)
M. F. Fadal
2012-06-01
Full Text Available This paper proposes a three-parameter mathematical model to describe the particle size distribution in a water sample. The proposed model offers some conceptual advantages over two other models reported on previously, and also provides a better fit to the particle counting data obtained from 321 water samples taken over three years at a large South African drinking water supplier. Using the data from raw water samples taken from a moderately turbid, large surface impoundment, as well as samples from the same water after treatment, typical ranges of the model parameters are presented for both raw and treated water. Once calibrated, the model allows the calculation and comparison of total particle number and volumes over any randomly selected size interval of interest.
Moghimbeigi, Abbas
2015-05-07
Poisson regression models provide a standard framework for quantitative trait locus (QTL) mapping of count traits. In practice, however, count traits are often over-dispersed relative to the Poisson distribution. In these situations, the zero-inflated Poisson (ZIP), zero-inflated generalized Poisson (ZIGP) and zero-inflated negative binomial (ZINB) regression may be useful for QTL mapping of count traits. Added genetic variables to the negative binomial part equation, may also affect extra zero data. In this study, to overcome these challenges, I apply two-part ZINB model. The EM algorithm with Newton-Raphson method in the M-step uses for estimating parameters. An application of the two-part ZINB model for QTL mapping is considered to detect associations between the formation of gallstone and the genotype of markers. Copyright © 2015 Elsevier Ltd. All rights reserved.
Uncertainty the soul of modeling, probability & statistics
Briggs, William
2016-01-01
This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...
Statistical Models for Social Networks
Snijders, Tom A. B.; Cook, KS; Massey, DS
2011-01-01
Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For
An Adaptive Smoother for Counting Measurements
International Nuclear Information System (INIS)
Kondrasovs Vladimir; Coulon Romain; Normand Stephane
2013-06-01
Counting measurements associated with nuclear instruments are tricky to carry out due to the stochastic process of the radioactivity. Indeed events counting have to be processed and filtered in order to display a stable count rate value and to allow variations monitoring in the measured activity. Smoothers (as the moving average) are adjusted by a time constant defined as a compromise between stability and response time. A new approach has been developed and consists in improving the response time while maintaining count rate stability. It uses the combination of a smoother together with a detection filter. A memory of counting data is processed to calculate several count rate estimates using several integration times. These estimates are then sorted into the memory from short to long integration times. A measurement position, in terms of integration time, is then chosen into this memory after a detection test. An inhomogeneity into the Poisson counting process is detected by comparison between current position estimate and the other estimates contained into the memory in respect with the associated statistical variance calculated with homogeneous assumption. The measurement position (historical time) and the ability to forget an obsolete data or to keep in memory a useful data are managed using the detection test result. The proposed smoother is then an adaptive and a learning algorithm allowing an optimization of the response time while maintaining measurement counting stability and converging efficiently to the best counting estimate after an effective change in activity. This algorithm has also the specificity to be low recursive and thus easily embedded into DSP electronics based on FPGA or micro-controllers meeting 'real life' time requirements. (authors)
Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O
2016-06-01
Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.
Graph theory applied to noise and vibration control in statistical energy analysis models.
Guasch, Oriol; Cortés, Lluís
2009-06-01
A fundamental aspect of noise and vibration control in statistical energy analysis (SEA) models consists in first identifying and then reducing the energy flow paths between subsystems. In this work, it is proposed to make use of some results from graph theory to address both issues. On the one hand, linear and path algebras applied to adjacency matrices of SEA graphs are used to determine the existence of any order paths between subsystems, counting and labeling them, finding extremal paths, or determining the power flow contributions from groups of paths. On the other hand, a strategy is presented that makes use of graph cut algorithms to reduce the energy flow from a source subsystem to a receiver one, modifying as few internal and coupling loss factors as possible.
Collecting operational event data for statistical analysis
International Nuclear Information System (INIS)
Atwood, C.L.
1994-09-01
This report gives guidance for collecting operational data to be used for statistical analysis, especially analysis of event counts. It discusses how to define the purpose of the study, the unit (system, component, etc.) to be studied, events to be counted, and demand or exposure time. Examples are given of classification systems for events in the data sources. A checklist summarizes the essential steps in data collection for statistical analysis
Functional summary statistics for the Johnson-Mehl model
DEFF Research Database (Denmark)
Møller, Jesper; Ghorbani, Mohammad
The Johnson-Mehl germination-growth model is a spatio-temporal point process model which among other things have been used for the description of neurotransmitters datasets. However, for such datasets parametric Johnson-Mehl models fitted by maximum likelihood have yet not been evaluated by means...... of functional summary statistics. This paper therefore invents four functional summary statistics adapted to the Johnson-Mehl model, with two of them based on the second-order properties and the other two on the nuclei-boundary distances for the associated Johnson-Mehl tessellation. The functional summary...... statistics theoretical properties are investigated, non-parametric estimators are suggested, and their usefulness for model checking is examined in a simulation study. The functional summary statistics are also used for checking fitted parametric Johnson-Mehl models for a neurotransmitters dataset....
Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P
1999-01-01
Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149
Statistical Engine Knock Control
DEFF Research Database (Denmark)
Stotsky, Alexander A.
2008-01-01
A new statistical concept of the knock control of a spark ignition automotive engine is proposed . The control aim is associated with the statistical hy pothesis test which compares the threshold value to the average value of the max imal amplitud e of the knock sensor signal at a given freq uency....... C ontrol algorithm which is used for minimization of the regulation error realizes a simple count-up-count-d own logic. A new ad aptation algorithm for the knock d etection threshold is also d eveloped . C onfi d ence interval method is used as the b asis for ad aptation. A simple statistical mod el...... which includ es generation of the amplitud e signals, a threshold value d etermination and a knock sound mod el is d eveloped for evaluation of the control concept....
Assessing the value of museums with a combined discrete choice/ count data model
Rouwendal, J.; Boter, J.
2009-01-01
This article assesses the value of Dutch museums using information about destination choice as well as about the number of trips undertaken by an actor. Destination choice is analysed by means of a mixed logit model, and a count data model is used to explain trip generation. We use a
Distributions with given marginals and statistical modelling
Fortiana, Josep; Rodriguez-Lallena, José
2002-01-01
This book contains a selection of the papers presented at the meeting `Distributions with given marginals and statistical modelling', held in Barcelona (Spain), July 17-20, 2000. In 24 chapters, this book covers topics such as the theory of copulas and quasi-copulas, the theory and compatibility of distributions, models for survival distributions and other well-known distributions, time series, categorical models, definition and estimation of measures of dependence, monotonicity and stochastic ordering, shape and separability of distributions, hidden truncation models, diagonal families, orthogonal expansions, tests of independence, and goodness of fit assessment. These topics share the use and properties of distributions with given marginals, this being the fourth specialised text on this theme. The innovative aspect of the book is the inclusion of statistical aspects such as modelling, Bayesian statistics, estimation, and tests.
Actuarial statistics with generalized linear mixed models
Antonio, K.; Beirlant, J.
2007-01-01
Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics
Reference analysis of the signal + background model in counting experiments
Casadei, D.
2012-01-01
The model representing two independent Poisson processes, labelled as ``signal'' and ``background'' and both contributing additively to the total number of counted events, is considered from a Bayesian point of view. This is a widely used model for the searches of rare or exotic events in presence of a background source, as for example in the searches performed by high-energy physics experiments. In the assumption of prior knowledge about the background yield, a reference prior is obtained for the signal alone and its properties are studied. Finally, the properties of the full solution, the marginal reference posterior, are illustrated with few examples.
Structured statistical models of inductive reasoning.
Kemp, Charles; Tenenbaum, Joshua B
2009-01-01
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.
Weichenthal, Scott; Dufresne, André; Infante-Rivard, Claire; Joseph, Lawrence
2008-03-01
School classrooms are potentially important micro-environments for childhood exposures owing to the large amount of time children spend in these locations. While a number of airborne contaminants may be present in schools, to date few studies have examined ultrafine particle (0.02-1 microm) (UFP) levels in classrooms. In this study, our objective was to characterize UFP counts (cm(-3)) in classrooms during the winter months and to develop a model to predict such exposures based on ambient weather conditions and outdoor UFPs, as well as classroom characteristics such as size, temperature, relative humidity, and carbon dioxide levels. In total, UFP count data were collected on 60 occasions in 37 occupied classrooms at one elementary school and one secondary school in Pembroke, Ontario. On average, outdoor UFP levels exceeded indoor measures by 8989 cm(-3) (95% confidence interval (CI): 6382, 11596), and classroom UFP counts were similar at both schools with a combined average of 5017 cm(-3) (95% CI: 4300, 5734). Of the variables examined only wind speed and outdoor UFPs were important determinants of classrooms UFP levels. Specifically, each 10 km/h increase in wind speed corresponded to an 1873 cm(-3) (95% CI: 825, 2920) decrease in classroom UFP counts, and each 10000 cm(-3) increase in outdoor UFPs corresponded to a 1550 cm(-3) (95% CI: 930, 2171) increase in classroom UFP levels. However, high correlations between these two predictors meant that the independent effects of wind speed and outdoor UFPs could not be separated in multivariable models, and only outdoor UFP counts were included in the final predictive model. To evaluate model performance, classroom UFP counts were collected for 8 days at two new schools and compared to predicted values based on outdoor UFP measures. A moderate correlation was observed between measured and predicted classroom UFP counts (r=0.63) for both schools combined, but this relationship was not valid on days in which a strong
International Nuclear Information System (INIS)
Weichenthal, Scott; Dufresne, Andre; Infante-Rivard, Claire; Joseph, Lawrence
2008-01-01
School classrooms are potentially important micro-environments for childhood exposures owing to the large amount of time children spend in these locations. While a number of airborne contaminants may be present in schools, to date few studies have examined ultrafine particle (0.02-1 μm) (UFP) levels in classrooms. In this study, our objective was to characterize UFP counts (cm -3 ) in classrooms during the winter months and to develop a model to predict such exposures based on ambient weather conditions and outdoor UFPs, as well as classroom characteristics such as size, temperature, relative humidity, and carbon dioxide levels. In total, UFP count data were collected on 60 occasions in 37 occupied classrooms at one elementary school and one secondary school in Pembroke, Ontario. On average, outdoor UFP levels exceeded indoor measures by 8989 cm -3 (95% confidence interval (CI): 6382, 11 596), and classroom UFP counts were similar at both schools with a combined average of 5017 cm -3 (95% CI: 4300, 5734). Of the variables examined only wind speed and outdoor UFPs were important determinants of classrooms UFP levels. Specifically, each 10 km/h increase in wind speed corresponded to an 1873 cm -3 (95% CI: 825, 2920) decrease in classroom UFP counts, and each 10 000 cm -3 increase in outdoor UFPs corresponded to a 1550 cm -3 (95% CI: 930, 2171) increase in classroom UFP levels. However, high correlations between these two predictors meant that the independent effects of wind speed and outdoor UFPs could not be separated in multivariable models, and only outdoor UFP counts were included in the final predictive model. To evaluate model performance, classroom UFP counts were collected for 8 days at two new schools and compared to predicted values based on outdoor UFP measures. A moderate correlation was observed between measured and predicted classroom UFP counts (r=0.63) for both schools combined, but this relationship was not valid on days in which a strong indoor UFP
Quantum Biometrics with Retinal Photon Counting
Loulakis, M.; Blatsios, G.; Vrettou, C. S.; Kominis, I. K.
2017-10-01
It is known that the eye's scotopic photodetectors, rhodopsin molecules, and their associated phototransduction mechanism leading to light perception, are efficient single-photon counters. We here use the photon-counting principles of human rod vision to propose a secure quantum biometric identification based on the quantum-statistical properties of retinal photon detection. The photon path along the human eye until its detection by rod cells is modeled as a filter having a specific transmission coefficient. Precisely determining its value from the photodetection statistics registered by the conscious observer is a quantum parameter estimation problem that leads to a quantum secure identification method. The probabilities for false-positive and false-negative identification of this biometric technique can readily approach 10-10 and 10-4, respectively. The security of the biometric method can be further quantified by the physics of quantum measurements. An impostor must be able to perform quantum thermometry and quantum magnetometry with energy resolution better than 10-9ℏ , in order to foil the device by noninvasively monitoring the biometric activity of a user.
The Fractal Characteristics of the Landslides by Box-Counting and P-A Model
Wang, Zhiwang; Zhou, Fangfang; Cao, Hao
2018-01-01
The landslide is a kind of complicated phenomenon with nonlinear inter-reaction. The traditional theories and methods are difficult to study the uncertainty characteristics of dynamic evolution of the landslides. This paper applies box-counting and P-A model to study the fractal characteristics of geometric shape and spatial distribution of the landslide hazards in the study area from Badong county to Zigui county in TGP reservoir region. The data obtained from the study area shows power-law distributions of geometric shape and spatial distribution of the landslides, and thus reveals some fractal or self-similarity properties. The fractral dimensions DAP of the spatial distribution of landslides by P-A model shows that DAP of the western landslides in the study area are smaller than those of the east, which shows that the geometry of the eastern landslide is more irregular and complicated than the western ones. The results show box-counting model and P-A model can be used to characterize the fractal characteristics of geometric shape and spatial distribution of the landslides.
Statistical modelling in biostatistics and bioinformatics selected papers
Peng, Defen
2014-01-01
This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...
Pixel-Cluster Counting Luminosity Measurement in ATLAS
McCormack, William Patrick; The ATLAS collaboration
2016-01-01
A precision measurement of the delivered luminosity is a key component of the ATLAS physics program at the Large Hadron Collider (LHC). A fundamental ingredient of the strategy to control the systematic uncertainties affecting the absolute luminosity has been to compare the measurements of several luminometers, most of which use more than one counting technique. The level of consistency across the various methods provides valuable cross-checks as well as an estimate of the detector-related systematic uncertainties. This poster describes the development of a luminosity algorithm based on pixel-cluster counting in the recently installed ATLAS inner b-layer (IBL), using data recorded during the 2015 pp run at the LHC. The noise and background contamination of the luminosity-associated cluster count is minimized by a multi-component fit to the measured cluster-size distribution in the forward pixel modules of the IBL. The linearity, long-term stability and statistical precision of the cluster-counting method are ...
Pixel-Cluster Counting Luminosity Measurement In ATLAS
AUTHOR|(SzGeCERN)782710; The ATLAS collaboration
2017-01-01
A precision measurement of the delivered luminosity is a key component of the ATLAS physics program at the Large Hadron Collider (LHC). A fundamental ingredient of the strategy to control the systematic uncertainties affecting the absolute luminosity has been to compare the measure- ments of several luminometers, most of which use more than one counting technique. The level of consistency across the various methods provides valuable cross-checks as well as an estimate of the detector-related systematic uncertainties. This poster describes the development of a luminosity algorithm based on pixel-cluster counting in the recently installed ATLAS inner b-layer (IBL), using data recorded during the 2015 pp run at the LHC. The noise and background contamination of the luminosity-associated cluster count is minimized by a multi-component fit to the measured cluster-size distribution in the forward pixel modules of the IBL. The linearity, long-term stability and statistical precision of the cluster- counting method a...
Comment on: 'A Poisson resampling method for simulating reduced counts in nuclear medicine images'
DEFF Research Database (Denmark)
de Nijs, Robin
2015-01-01
In order to be able to calculate half-count images from already acquired data, White and Lawson published their method based on Poisson resampling. They verified their method experimentally by measurements with a Co-57 flood source. In this comment their results are reproduced and confirmed...... by a direct numerical simulation in Matlab. Not only Poisson resampling, but also two direct redrawing methods were investigated. Redrawing methods were based on a Poisson and a Gaussian distribution. Mean, standard deviation, skewness and excess kurtosis half-count/full-count ratios were determined for all...... methods, and compared to the theoretical values for a Poisson distribution. Statistical parameters showed the same behavior as in the original note and showed the superiority of the Poisson resampling method. Rounding off before saving of the half count image had a severe impact on counting statistics...
Hornbrook, Mark C; Goshen, Ran; Choman, Eran; O'Keeffe-Rosetti, Maureen; Kinar, Yaron; Liles, Elizabeth G; Rust, Kristal C
2017-10-01
Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a "calendar year" based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. ColonFlag ® identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.
Problems and precision of the alpha scintillation radon counting system
International Nuclear Information System (INIS)
Lucas, H.F.; Markuu, F.
1985-01-01
Variations in efficiency as large as 3% have been found for radon scintillation counting systems in which the photomultiplier tubes are sensitive to the thermoluminescent photons emitted by the scintillator after exposure to light or for which the resolution has deteriorated. The additional standard deviation caused by counting a radon chamber on multiple counting systems has been evaluated and the effect, if present, did not exceed about 0.1%. The chambers have been calibrated for the measurement of radon in air, and the standard deviation was equal to statistical counting error combined with a systematic error of 1.1%. 3 references, 2 figures, 2 tables
Study of mast cell count in skin tags
Directory of Open Access Journals (Sweden)
Zaher Hesham
2007-01-01
Full Text Available Background: Skin tags or acrochordons are common tumors of middle-aged and elderly subjects. They consist of loose fibrous tissue and occur mainly on the neck and major flexures as small, soft, pedunculated protrusions. Objectives: The aim was to compare the mast cells count in skin tags to adjacent normal skin in diabetic and nondiabetic participants in an attempt to elucidate the possible role of mast cells in the pathogenesis of skin tags. Participants and Methods: Thirty participants with skin tags were divided into group I (15 nondiabetic participants and group II (15 diabetic participants. Three biopsies were obtained from each participant: a large skin tag, a small skin tag and adjacent normal skin. Mast cell count from all the obtained sections was carried out, and the mast cell density was expressed as the average mast cell count/high power field (HPF. Results: A statistically significant increase in mast cells count in skin tags in comparison to normal skin was detected in group I and group II. There was no statistically significant difference between mast cell counts in skin tags of both the groups. Conclusion: Both the mast cell mediators and hyperinsulinemia are capable of inducing fibroblast proliferation and epidermal hyperplasia that are the main pathologic abnormalities seen in all types of skin tags. However, the presence of mast cells in all examined skin tags regardless of diabetes and obesity may point to the possible crucial role of mast cells in the etiogenesis of skin tags through its interaction with fibroblasts and keratinocytes.
The distribution of controlled drugs on banknotes via counting machines.
Carter, James F; Sleeman, Richard; Parry, Joanna
2003-03-27
Bundles of paper, similar to sterling banknotes, were counted in banks in England and Wales. Subsequent analysis showed that the counting process, both by machine and by hand, transferred nanogram amounts of cocaine to the paper. Crystalline material, similar to cocaine hydrochloride, could be observed on the surface of the paper following counting. The geographical distribution of contamination broadly followed Government statistics for cocaine usage within the UK. Diacetylmorphine, Delta(9)-tetrahydrocannabinol (THC) and 3,4-methylenedioxymethylamphetamine (MDMA) were not detected during this study.
Statistical Inference for a Class of Multivariate Negative Binomial Distributions
DEFF Research Database (Denmark)
Rubak, Ege H.; Møller, Jesper; McCullagh, Peter
This paper considers statistical inference procedures for a class of models for positively correlated count variables called -permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...... studied in the literature, while this is the first statistical paper on -permanental random fields. The focus is on maximum likelihood estimation, maximum quasi-likelihood estimation and on maximum composite likelihood estimation based on uni- and bivariate distributions. Furthermore, new results...
Generalized interpolative quantum statistics
International Nuclear Information System (INIS)
Ramanathan, R.
1992-01-01
A generalized interpolative quantum statistics is presented by conjecturing a certain reordering of phase space due to the presence of possible exotic objects other than bosons and fermions. Such an interpolation achieved through a Bose-counting strategy predicts the existence of an infinite quantum Boltzmann-Gibbs statistics akin to the one discovered by Greenberg recently
Lagarias, Jeffrey C.; Rains, Eric; Vanderbei, Robert J.
2001-01-01
The Kruskal Count is a card trick invented by Martin J. Kruskal in which a magician "guesses" a card selected by a subject according to a certain counting procedure. With high probability the magician can correctly "guess" the card. The success of the trick is based on a mathematical principle related to coupling methods for Markov chains. This paper analyzes in detail two simplified variants of the trick and estimates the probability of success. The model predictions are compared with simula...
DEFF Research Database (Denmark)
Østergaard, Søren; Ettema, Jehan Frans; Hjortø, Line
Multiple regression and model building with mediator variables was addressed to avoid double counting when economic values are estimated from data simulated with herd simulation modeling (using the SimHerd model). The simulated incidence of metritis was analyzed statistically as the independent v...... in multiparous cows. The merit of using this approach was demonstrated since the economic value of metritis was estimated to be 81% higher when no mediator variables were included in the multiple regression analysis......Multiple regression and model building with mediator variables was addressed to avoid double counting when economic values are estimated from data simulated with herd simulation modeling (using the SimHerd model). The simulated incidence of metritis was analyzed statistically as the independent...... variable, while using the traits representing the direct effects of metritis on yield, fertility and occurrence of other diseases as mediator variables. The economic value of metritis was estimated to be €78 per 100 cow-years for each 1% increase of metritis in the period of 1-100 days in milk...
Directory of Open Access Journals (Sweden)
Xavier A. Harrison
2014-10-01
Full Text Available Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated data, or an excess frequency of zeroes (zero-inflation. Accounting for overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE, where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with overdispersion are scarce. Here I use simulations to show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of overdispersion. There was a positive relationship between the magnitude of overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for overdispersion in mixed models can erroneously inflate measures of explained variance (r2, which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of overdispersion and must be applied judiciously.
Alabama Kids Count 2002 Data Book.
Curtis, Apreill; Bogie, Don
This Kids Count data book examines statewide trends in well-being of Alabamas children. The statistical portrait is based on 18 indicators in the areas of child health, education, safety, and security: (1) infant mortality rate; (2) low weight births; (3) child health index; (4) births to unmarried teens; (5) first grade retention; (6) school…
A Stochastic Fractional Dynamics Model of Rainfall Statistics
Kundu, Prasun; Travis, James
2013-04-01
Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is designed to faithfully reflect the scale dependence and is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. The main restriction is the assumption that the statistics of the precipitation field is spatially homogeneous and isotropic and stationary in time. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of the radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment. Some data sets containing periods of non-stationary behavior that involves occasional anomalously correlated rain events, present a challenge for the model.
A Model for Positively Correlated Count Variables
DEFF Research Database (Denmark)
Møller, Jesper; Rubak, Ege Holger
2010-01-01
An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields...... and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α-permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work....
Statistical Models and Methods for Lifetime Data
Lawless, Jerald F
2011-01-01
Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis problems."-Biometrics"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."-Statistics in MedicineThe statistical analysis of lifetime or response time data is a key tool in engineering,
Playing at Statistical Mechanics
Clark, Paul M.; And Others
1974-01-01
Discussed are the applications of counting techniques of a sorting game to distributions and concepts in statistical mechanics. Included are the following distributions: Fermi-Dirac, Bose-Einstein, and most probable. (RH)
Topology for Statistical Modeling of Petascale Data
Energy Technology Data Exchange (ETDEWEB)
Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Bremer, P. -T. [Univ. of Utah, Salt Lake City, UT (United States)
2013-10-31
Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.
Statistical models and methods for reliability and survival analysis
Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo
2013-01-01
Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical
Is it safe to use Poisson statistics in nuclear spectrometry?
International Nuclear Information System (INIS)
Pomme, S.; Robouch, P.; Arana, G.; Eguskiza, M.; Maguregui, M.I.
2000-01-01
The boundary conditions in which Poisson statistics can be applied in nuclear spectrometry are investigated. Improved formulas for the uncertainty of nuclear counting with deadtime and pulse pileup are presented. A comparison is made between the expected statistical uncertainty for loss-free counting, fixed live-time and fixed real-time measurements. (author)
Analysis of counting data: Development of the SATLAS Python package
Gins, W.; de Groote, R. P.; Bissell, M. L.; Granados Buitrago, C.; Ferrer, R.; Lynch, K. M.; Neyens, G.; Sels, S.
2018-01-01
For the analysis of low-statistics counting experiments, a traditional nonlinear least squares minimization routine may not always provide correct parameter and uncertainty estimates due to the assumptions inherent in the algorithm(s). In response to this, a user-friendly Python package (SATLAS) was written to provide an easy interface between the data and a variety of minimization algorithms which are suited for analyzinglow, as well as high, statistics data. The advantage of this package is that it allows the user to define their own model function and then compare different minimization routines to determine the optimal parameter values and their respective (correlated) errors. Experimental validation of the different approaches in the package is done through analysis of hyperfine structure data of 203Fr gathered by the CRIS experiment at ISOLDE, CERN.
Statistical properties of superimposed stationary spike trains.
Deger, Moritz; Helias, Moritz; Boucsein, Clemens; Rotter, Stefan
2012-06-01
The Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models. The constructed superimposed spike trains reveal strong deviations from the Poisson model. We find that superpositions of model spike trains that take the effective refractoriness of the neurons into account yield a much better description. A minimal model of this kind is the Poisson process with dead-time (PPD). For this process, and for superpositions thereof, we obtain analytical expressions for some second-order statistical quantities-like the count variability, inter-spike interval (ISI) variability and ISI correlations-and demonstrate the match with the in vivo data. We conclude that effective refractoriness is the key property that shapes the statistical properties of the superposition spike trains. We present new, efficient algorithms to generate superpositions of PPDs and of gamma processes that can be used to provide more realistic background input in simulations of networks of spiking neurons. Using these generators, we show in simulations that neurons which receive superimposed spike trains as input are highly sensitive for the statistical effects induced by neuronal refractoriness.
A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model
Energy Technology Data Exchange (ETDEWEB)
Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-05-11
This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.
Leveraging multiple datasets for deep leaf counting
Dobrescu, Andrei; Giuffrida, Mario Valerio; Tsaftaris, Sotirios A
2017-01-01
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method tre...
Atmospheric mold spore counts in relation to meteorological parameters
Katial, R. K.; Zhang, Yiming; Jones, Richard H.; Dyer, Philip D.
Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (Pmodel was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.
Pattern statistics on Markov chains and sensitivity to parameter estimation
Directory of Open Access Journals (Sweden)
Nuel Grégory
2006-10-01
Full Text Available Abstract Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,.... Results: In the particular case where pattern statistics (overlap counting only computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.
Model-generated air quality statistics for application in vegetation response models in Alberta
International Nuclear Information System (INIS)
McVehil, G.E.; Nosal, M.
1990-01-01
To test and apply vegetation response models in Alberta, air pollution statistics representative of various parts of the Province are required. At this time, air quality monitoring data of the requisite accuracy and time resolution are not available for most parts of Alberta. Therefore, there exists a need to develop appropriate air quality statistics. The objectives of the work reported here were to determine the applicability of model generated air quality statistics and to develop by modelling, realistic and representative time series of hourly SO 2 concentrations that could be used to generate the statistics demanded by vegetation response models
Alabama Kids Count 2001 Data Book.
Curtis, Apreill; Bogie, Don
This Kids Count data book examines statewide trends in well-being for Alabama's children. The statistical portrait is based on 17 indicators in the areas of health, education, safety, and security. The indicators are: (1) infant mortality rate; (2) low weight births; (3) child health index; (4) births to unmarried teens; (5) first grade retention;…
Nevada Kids Count Data Book, 1997.
We Can, Inc., Las Vegas, NV.
This Kids Count data book is the first to examine statewide indicators of the well being of Nevada's children. The statistical portrait is based on 15 indicators of child well being: (1) percent low birth-weight babies; (2) infant mortality rate; (3) percent of children in poverty; (4) percent of children in single-parent families; (5) percent of…
Performance modeling, loss networks, and statistical multiplexing
Mazumdar, Ravi
2009-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I
Chrom, Pawel; Stec, Rafal; Bodnar, Lubomir; Szczylik, Cezary
2018-01-01
The study investigated whether a replacement of neutrophil count and platelet count by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model would improve its prognostic accuracy. This retrospective analysis included consecutive patients with metastatic renal cell carcinoma treated with first-line tyrosine kinase inhibitors. The IMDC and modified-IMDC models were compared using: concordance index (CI), bias-corrected concordance index (BCCI), calibration plots, the Grønnesby and Borgan test, Bayesian Information Criterion (BIC), generalized R 2 , Integrated Discrimination Improvement (IDI), and continuous Net Reclassification Index (cNRI) for individual risk factors and the three risk groups. Three hundred and twenty-one patients were eligible for analyses. The modified-IMDC model with NLR value of 3.6 and PLR value of 157 was selected for comparison with the IMDC model. Both models were well calibrated. All other measures favoured the modified-IMDC model over the IMDC model (CI, 0.706 vs. 0.677; BCCI, 0.699 vs. 0.671; BIC, 2,176.2 vs. 2,190.7; generalized R 2 , 0.238 vs. 0.202; IDI, 0.044; cNRI, 0.279 for individual risk factors; and CI, 0.669 vs. 0.641; BCCI, 0.669 vs. 0.641; BIC, 2,183.2 vs. 2,198.1; generalized R 2 , 0.163 vs. 0.123; IDI, 0.045; cNRI, 0.165 for the three risk groups). Incorporation of NLR and PLR in place of neutrophil count and platelet count improved prognostic accuracy of the IMDC model. These findings require external validation before introducing into clinical practice.
Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.
2017-12-01
Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.
Directory of Open Access Journals (Sweden)
N. Mielenz
2015-01-01
Full Text Available Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM. In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.
Statistical Models of Adaptive Immune populations
Sethna, Zachary; Callan, Curtis; Walczak, Aleksandra; Mora, Thierry
The availability of large (104-106 sequences) datasets of B or T cell populations from a single individual allows reliable fitting of complex statistical models for naïve generation, somatic selection, and hypermutation. It is crucial to utilize a probabilistic/informational approach when modeling these populations. The inferred probability distributions allow for population characterization, calculation of probability distributions of various hidden variables (e.g. number of insertions), as well as statistical properties of the distribution itself (e.g. entropy). In particular, the differences between the T cell populations of embryonic and mature mice will be examined as a case study. Comparing these populations, as well as proposed mixed populations, provides a concrete exercise in model creation, comparison, choice, and validation.
Tropical geometry of statistical models.
Pachter, Lior; Sturmfels, Bernd
2004-11-16
This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.
Range walk error correction and modeling on Pseudo-random photon counting system
Shen, Shanshan; Chen, Qian; He, Weiji
2017-08-01
Signal to noise ratio and depth accuracy are modeled for the pseudo-random ranging system with two random processes. The theoretical results, developed herein, capture the effects of code length and signal energy fluctuation are shown to agree with Monte Carlo simulation measurements. First, the SNR is developed as a function of the code length. Using Geiger-mode avalanche photodiodes (GMAPDs), longer code length is proven to reduce the noise effect and improve SNR. Second, the Cramer-Rao lower bound on range accuracy is derived to justify that longer code length can bring better range accuracy. Combined with the SNR model and CRLB model, it is manifested that the range accuracy can be improved by increasing the code length to reduce the noise-induced error. Third, the Cramer-Rao lower bound on range accuracy is shown to converge to the previously published theories and introduce the Gauss range walk model to range accuracy. Experimental tests also converge to the presented boundary model in this paper. It has been proven that depth error caused by the fluctuation of the number of detected photon counts in the laser echo pulse leads to the depth drift of Time Point Spread Function (TPSF). Finally, numerical fitting function is used to determine the relationship between the depth error and the photon counting ratio. Depth error due to different echo energy is calibrated so that the corrected depth accuracy is improved to 1cm.
Modified Exponential (MOE) Models: statistical Models for Risk Estimation of Low dose Rate Radiation
International Nuclear Information System (INIS)
Ogata, H.; Furukawa, C.; Kawakami, Y.; Magae, J.
2004-01-01
Simultaneous inclusion of dose and dose-rate is required to evaluate the risk of long term irradiation at low dose-rates, since biological responses to radiation are complex processes that depend both on irradiation time and total dose. Consequently, it is necessary to consider a model including cumulative dose,dose-rate and irradiation time to estimate quantitative dose-response relationship on the biological response to radiation. In this study, we measured micronucleus formation and (3H) thymidine uptake in U2OS, human osteosarcoma cell line, as indicators of biological response to gamma radiation. Cells were exposed to gamma ray in irradiation room bearing 50,000 Ci 60Co. After irradiation, they were cultured for 24h in the presence of cytochalasin B to block cytokinesis, and cytoplasm and nucleus were stained with DAPI and propidium iodide. The number of binuclear cells bearing a micronucleus was counted under a florescence microscope. For proliferation inhibition, cells were cultured for 48 h after the irradiation and (3h) thymidine was pulsed for 4h before harvesting. We statistically analyzed the data for quantitative evaluation of radiation risk at low dose/dose-rate. (Author)
12th Workshop on Stochastic Models, Statistics and Their Applications
Rafajłowicz, Ewaryst; Szajowski, Krzysztof
2015-01-01
This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.
Energy Technology Data Exchange (ETDEWEB)
Bard, D.; Chang, C.; Kahn, S. M.; Gilmore, K.; Marshall, S. [KIPAC, Stanford University, 452 Lomita Mall, Stanford, CA 94309 (United States); Kratochvil, J. M.; Huffenberger, K. M. [Department of Physics, University of Miami, Coral Gables, FL 33124 (United States); May, M. [Physics Department, Brookhaven National Laboratory, Upton, NY 11973 (United States); AlSayyad, Y.; Connolly, A.; Gibson, R. R.; Jones, L.; Krughoff, S. [Department of Astronomy, University of Washington, Seattle, WA 98195 (United States); Ahmad, Z.; Bankert, J.; Grace, E.; Hannel, M.; Lorenz, S. [Department of Physics, Purdue University, West Lafayette, IN 47907 (United States); Haiman, Z.; Jernigan, J. G., E-mail: djbard@slac.stanford.edu [Department of Astronomy and Astrophysics, Columbia University, New York, NY 10027 (United States); and others
2013-09-01
We study the effect of galaxy shape measurement errors on predicted cosmological constraints from the statistics of shear peak counts with the Large Synoptic Survey Telescope (LSST). We use the LSST Image Simulator in combination with cosmological N-body simulations to model realistic shear maps for different cosmological models. We include both galaxy shape noise and, for the first time, measurement errors on galaxy shapes. We find that the measurement errors considered have relatively little impact on the constraining power of shear peak counts for LSST.
Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I
2018-01-01
Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.
Statistical Validation of Engineering and Scientific Models: Background
International Nuclear Information System (INIS)
Hills, Richard G.; Trucano, Timothy G.
1999-01-01
A tutorial is presented discussing the basic issues associated with propagation of uncertainty analysis and statistical validation of engineering and scientific models. The propagation of uncertainty tutorial illustrates the use of the sensitivity method and the Monte Carlo method to evaluate the uncertainty in predictions for linear and nonlinear models. Four example applications are presented; a linear model, a model for the behavior of a damped spring-mass system, a transient thermal conduction model, and a nonlinear transient convective-diffusive model based on Burger's equation. Correlated and uncorrelated model input parameters are considered. The model validation tutorial builds on the material presented in the propagation of uncertainty tutoriaI and uses the damp spring-mass system as the example application. The validation tutorial illustrates several concepts associated with the application of statistical inference to test model predictions against experimental observations. Several validation methods are presented including error band based, multivariate, sum of squares of residuals, and optimization methods. After completion of the tutorial, a survey of statistical model validation literature is presented and recommendations for future work are made
Four shells atomic model to computer the counting efficiency of electron-capture nuclides
International Nuclear Information System (INIS)
Grau Malonda, A.; Fernandez Martinez, A.
1985-01-01
The present paper develops a four-shells atomic model in order to obtain the efficiency of detection in liquid scintillation courting, Mathematical expressions are given to calculate the probabilities of the 229 different atomic rearrangements so as the corresponding effective energies. This new model will permit the study of the influence of the different parameters upon the counting efficiency for nuclides of high atomic number. (Author) 7 refs
Multiple commodities in statistical microeconomics: Model and market
Baaquie, Belal E.; Yu, Miao; Du, Xin
2016-11-01
A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.
Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin
2015-03-01
Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.
Statistical models for optimizing mineral exploration
International Nuclear Information System (INIS)
Wignall, T.K.; DeGeoffroy, J.
1987-01-01
The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)
Emuchay, Charles Iheanyichi; Okeniyi, Shemaiah Olufemi; Okeniyi, Joshua Olusegun
2014-04-01
The expensive and technology limited setting of CD4 count testing is a major setback to the initiation of HAART in a resource limited country like Nigeria. Simple and inexpensive tools such as Hemoglobin (Hb) measurement and Total Lymphocyte Count (TLC) are recommended as substitute marker. In order to assess the correlations of these parameters with CD4 count, 100 "apparently healthy" male volunteers tested HIV positive aged ≥ 20 years but ≤ 40 years were recruited and from whom Hb, Hct, TLC and CD4 count were obtained. The correlation coefficients, R, the Nash-Sutcliffe Coefficient of Efficiency (CoE) and the p-values of the ANOVA model of Hb, Hct and TLC with CD4 count were assessed. The assessments show that there is no significant relationship of any of these parameters with CD4 count and the correlation coefficients are very weak. This study shows that Hb, Hct and TLC cannot be substitute for CD4 count as this might lead to certain individuals' deprivation of required treatment.
Statistical physics of pairwise probability models
Directory of Open Access Journals (Sweden)
Yasser Roudi
2009-11-01
Full Text Available Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.
Directory of Open Access Journals (Sweden)
Tara L Crewe
Full Text Available The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling to reduce the probability that individuals will
Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.
2013-01-01
Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.
DEFF Research Database (Denmark)
Andersen, J.S.; Bedaux, J.J.M.; Kooijman, S.A.L.M.
2000-01-01
This paper describes the influence of design characteristics on the statistical inference for an ecotoxicological hazard-based model using simulated survival data. The design characteristics of interest are the number and spacing of observations (counts) in time, the number and spacing of exposure...... concentrations (within c(min) and c(max)), and the initial number of individuals at time 0 in each concentration. A comparison of the coverage probabilities for confidence limits arising from the profile-likelihood approach and the Wald-based approach is carried out. The Wald-based approach is very sensitive...
Principles of correlation counting
International Nuclear Information System (INIS)
Mueller, J.W.
1975-01-01
A review is given of the various applications which have been made of correlation techniques in the field of nuclear physics, in particular for absolute counting. Whereas in most cases the usual coincidence method will be preferable for its simplicity, correlation counting may be the only possible approach in such cases where the two radiations of the cascade cannot be well separated or when there is a longliving intermediate state. The measurement of half-lives and of count rates of spurious pulses is also briefly discussed. The various experimental situations lead to different ways the correlation method is best applied (covariance technique with one or with two detectors, application of correlation functions, etc.). Formulae are given for some simple model cases, neglecting dead-time corrections
Zhang, Yun; Baheti, Saurabh; Sun, Zhifu
2018-05-01
High-throughput bisulfite methylation sequencing such as reduced representation bisulfite sequencing (RRBS), Agilent SureSelect Human Methyl-Seq (Methyl-seq) or whole-genome bisulfite sequencing is commonly used for base resolution methylome research. These data are represented either by the ratio of methylated cytosine versus total coverage at a CpG site or numbers of methylated and unmethylated cytosines. Multiple statistical methods can be used to detect differentially methylated CpGs (DMCs) between conditions, and these methods are often the base for the next step of differentially methylated region identification. The ratio data have a flexibility of fitting to many linear models, but the raw count data take consideration of coverage information. There is an array of options in each datatype for DMC detection; however, it is not clear which is an optimal statistical method. In this study, we systematically evaluated four statistic methods on methylation ratio data and four methods on count-based data and compared their performances with regard to type I error control, sensitivity and specificity of DMC detection and computational resource demands using real RRBS data along with simulation. Our results show that the ratio-based tests are generally more conservative (less sensitive) than the count-based tests. However, some count-based methods have high false-positive rates and should be avoided. The beta-binomial model gives a good balance between sensitivity and specificity and is preferred method. Selection of methods in different settings, signal versus noise and sample size estimation are also discussed.
Statistical transmutation in doped quantum dimer models.
Lamas, C A; Ralko, A; Cabra, D C; Poilblanc, D; Pujol, P
2012-07-06
We prove a "statistical transmutation" symmetry of doped quantum dimer models on the square, triangular, and kagome lattices: the energy spectrum is invariant under a simultaneous change of statistics (i.e., bosonic into fermionic or vice versa) of the holes and of the signs of all the dimer resonance loops. This exact transformation enables us to define the duality equivalence between doped quantum dimer Hamiltonians and provides the analytic framework to analyze dynamical statistical transmutations. We investigate numerically the doping of the triangular quantum dimer model with special focus on the topological Z(2) dimer liquid. Doping leads to four (instead of two for the square lattice) inequivalent families of Hamiltonians. Competition between phase separation, superfluidity, supersolidity, and fermionic phases is investigated in the four families.
Metal ion levels and lymphocyte counts
DEFF Research Database (Denmark)
Penny, Jeannette Ø; Varmarken, Jens-Erik; Ovesen, Ole
2013-01-01
BACKGROUND AND PURPOSE: Wear particles from metal-on-metal arthroplasties are under suspicion for adverse effects both locally and systemically, and the DePuy ASR Hip Resurfacing System (RHA) has above-average failure rates. We compared lymphocyte counts in RHA and total hip arthroplasty (THA) an....../ppb. INTERPRETATION: Circulating T-lymphocyte levels may decline after surgery, regardless of implant type. Metal ions-particularly cobalt-may have a general depressive effect on T- and B-lymphocyte levels. Registered with ClinicalTrials.gov under # NCT01113762.......BACKGROUND AND PURPOSE: Wear particles from metal-on-metal arthroplasties are under suspicion for adverse effects both locally and systemically, and the DePuy ASR Hip Resurfacing System (RHA) has above-average failure rates. We compared lymphocyte counts in RHA and total hip arthroplasty (THA....... RESULTS: The T-lymphocyte counts for both implant types declined over the 2-year period. This decline was statistically significant for CD3(+)CD8(+) in the THA group, with a regression coefficient of -0.04 × 10(9)cells/year (95% CI: -0.08 to -0.01). Regression analysis indicated a depressive effect...
Textual information access statistical models
Gaussier, Eric
2013-01-01
This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access:- information extraction and retrieval;- text classification and clustering;- opinion mining;- comprehension aids (automatic summarization, machine translation, visualization).In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications
Model for neural signaling leap statistics
International Nuclear Information System (INIS)
Chevrollier, Martine; Oria, Marcos
2011-01-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.
Model for neural signaling leap statistics
Chevrollier, Martine; Oriá, Marcos
2011-03-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.
WE-A-201-02: Modern Statistical Modeling
Energy Technology Data Exchange (ETDEWEB)
Niemierko, A.
2016-06-15
Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear
WE-A-201-02: Modern Statistical Modeling
International Nuclear Information System (INIS)
Niemierko, A.
2016-01-01
Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear
Maximum entropy deconvolution of low count nuclear medicine images
International Nuclear Information System (INIS)
McGrath, D.M.
1998-12-01
Maximum entropy is applied to the problem of deconvolving nuclear medicine images, with special consideration for very low count data. The physics of the formation of scintigraphic images is described, illustrating the phenomena which degrade planar estimates of the tracer distribution. Various techniques which are used to restore these images are reviewed, outlining the relative merits of each. The development and theoretical justification of maximum entropy as an image processing technique is discussed. Maximum entropy is then applied to the problem of planar deconvolution, highlighting the question of the choice of error parameters for low count data. A novel iterative version of the algorithm is suggested which allows the errors to be estimated from the predicted Poisson mean values. This method is shown to produce the exact results predicted by combining Poisson statistics and a Bayesian interpretation of the maximum entropy approach. A facility for total count preservation has also been incorporated, leading to improved quantification. In order to evaluate this iterative maximum entropy technique, two comparable methods, Wiener filtering and a novel Bayesian maximum likelihood expectation maximisation technique, were implemented. The comparison of results obtained indicated that this maximum entropy approach may produce equivalent or better measures of image quality than the compared methods, depending upon the accuracy of the system model used. The novel Bayesian maximum likelihood expectation maximisation technique was shown to be preferable over many existing maximum a posteriori methods due to its simplicity of implementation. A single parameter is required to define the Bayesian prior, which suppresses noise in the solution and may reduce the processing time substantially. Finally, maximum entropy deconvolution was applied as a pre-processing step in single photon emission computed tomography reconstruction of low count data. Higher contrast results were
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
Studies on the Pulse Rate, Pedometer Count and Satisfactoin Degree at Various Exercise
小原, 史朗
2004-01-01
This investigation examined whether free exercise of students became good stimulation of breathing circulation function from relation of pulse rate and pedometer count. And, I examined it on satisfaction degree after exercise. Object person was 432 man students (total of 1391) and 94 woman students (total of 472). As for relation of pulse rate and pedometer count, statistical meaning was recognized by man and women. The exercise that a pulse rate and pedometer count were high together seemed ...
Milewski, Emil G
2012-01-01
REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Statistics II discusses sampling theory, statistical inference, independent and dependent variables, correlation theory, experimental design, count data, chi-square test, and time se
Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data
Directory of Open Access Journals (Sweden)
Jorge Alberto Achcar
2011-12-01
Full Text Available INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.
Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data.
Achcar, Jorge Alberto; Martinez, Edson Zangiacomi; Souza, Aparecida Doniseti Pires de; Tachibana, Vilma Mayumi; Flores, Edilson Ferreira
2011-01-01
Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using bayesian spatiotemporal methods. We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the bayesian paradigm is a good strategy for modeling malaria counts.
An Improved Statistical Point-source Foreground Model for the Epoch of Reionization
Energy Technology Data Exchange (ETDEWEB)
Murray, S. G.; Trott, C. M.; Jordan, C. H. [ARC Centre of Excellence for All-sky Astrophysics (CAASTRO) (Australia)
2017-08-10
We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions for the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.
An Improved Statistical Point-source Foreground Model for the Epoch of Reionization
Murray, S. G.; Trott, C. M.; Jordan, C. H.
2017-08-01
We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions for the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.
The use of plant models in deep learning: an application to leaf counting in rosette plants.
Ubbens, Jordan; Cieslak, Mikolaj; Prusinkiewicz, Przemyslaw; Stavness, Ian
2018-01-01
Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.
Equilibrium statistical mechanics of lattice models
Lavis, David A
2015-01-01
Most interesting and difficult problems in equilibrium statistical mechanics concern models which exhibit phase transitions. For graduate students and more experienced researchers this book provides an invaluable reference source of approximate and exact solutions for a comprehensive range of such models. Part I contains background material on classical thermodynamics and statistical mechanics, together with a classification and survey of lattice models. The geometry of phase transitions is described and scaling theory is used to introduce critical exponents and scaling laws. An introduction is given to finite-size scaling, conformal invariance and Schramm—Loewner evolution. Part II contains accounts of classical mean-field methods. The parallels between Landau expansions and catastrophe theory are discussed and Ginzburg—Landau theory is introduced. The extension of mean-field theory to higher-orders is explored using the Kikuchi—Hijmans—De Boer hierarchy of approximations. In Part III the use of alge...
Computational and Statistical Models: A Comparison for Policy Modeling of Childhood Obesity
Mabry, Patricia L.; Hammond, Ross; Ip, Edward Hak-Sing; Huang, Terry T.-K.
As systems science methodologies have begun to emerge as a set of innovative approaches to address complex problems in behavioral, social science, and public health research, some apparent conflicts with traditional statistical methodologies for public health have arisen. Computational modeling is an approach set in context that integrates diverse sources of data to test the plausibility of working hypotheses and to elicit novel ones. Statistical models are reductionist approaches geared towards proving the null hypothesis. While these two approaches may seem contrary to each other, we propose that they are in fact complementary and can be used jointly to advance solutions to complex problems. Outputs from statistical models can be fed into computational models, and outputs from computational models can lead to further empirical data collection and statistical models. Together, this presents an iterative process that refines the models and contributes to a greater understanding of the problem and its potential solutions. The purpose of this panel is to foster communication and understanding between statistical and computational modelers. Our goal is to shed light on the differences between the approaches and convey what kinds of research inquiries each one is best for addressing and how they can serve complementary (and synergistic) roles in the research process, to mutual benefit. For each approach the panel will cover the relevant "assumptions" and how the differences in what is assumed can foster misunderstandings. The interpretations of the results from each approach will be compared and contrasted and the limitations for each approach will be delineated. We will use illustrative examples from CompMod, the Comparative Modeling Network for Childhood Obesity Policy. The panel will also incorporate interactive discussions with the audience on the issues raised here.
The demand of car rentals: a microeconometric approach with count models and survey data
Czech Academy of Sciences Publication Activity Database
Menezes, A. G.; Uzagalieva, Ainura
2013-01-01
Roč. 5, č. 1 (2013), s. 25-41 ISSN 1973-3909 Institutional support: RVO:67985998 Keywords : count data models * tourism * tax rates Subject RIV: AH - Economics http://www.rofea.org/index.php?journal=journal&page=article&op=view&path%5B%5D=106
Spherical Process Models for Global Spatial Statistics
Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.
2017-01-01
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture
Model for neural signaling leap statistics
Energy Technology Data Exchange (ETDEWEB)
Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)
2011-03-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.
Overdispersion in nuclear statistics
International Nuclear Information System (INIS)
Semkow, Thomas M.
1999-01-01
The modern statistical distribution theory is applied to the development of the overdispersion theory in ionizing-radiation statistics for the first time. The physical nuclear system is treated as a sequence of binomial processes, each depending on a characteristic probability, such as probability of decay, detection, etc. The probabilities fluctuate in the course of a measurement, and the physical reasons for that are discussed. If the average values of the probabilities change from measurement to measurement, which originates from the random Lexis binomial sampling scheme, then the resulting distribution is overdispersed. The generating functions and probability distribution functions are derived, followed by a moment analysis. The Poisson and Gaussian limits are also given. The distribution functions belong to a family of generalized hypergeometric factorial moment distributions by Kemp and Kemp, and can serve as likelihood functions for the statistical estimations. An application to radioactive decay with detection is described and working formulae are given, including a procedure for testing the counting data for overdispersion. More complex experiments in nuclear physics (such as solar neutrino) can be handled by this model, as well as distinguishing between the source and background
Analysis and Evaluation of Statistical Models for Integrated Circuits Design
Directory of Open Access Journals (Sweden)
Sáenz-Noval J.J.
2011-10-01
Full Text Available Statistical models for integrated circuits (IC allow us to estimate the percentage of acceptable devices in the batch before fabrication. Actually, Pelgrom is the statistical model most accepted in the industry; however it was derived from a micrometer technology, which does not guarantee reliability in nanometric manufacturing processes. This work considers three of the most relevant statistical models in the industry and evaluates their limitations and advantages in analog design, so that the designer has a better criterion to make a choice. Moreover, it shows how several statistical models can be used for each one of the stages and design purposes.
The issue of statistical power for overall model fit in evaluating structural equation models
Directory of Open Access Journals (Sweden)
Richard HERMIDA
2015-06-01
Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.
Understanding and forecasting polar stratospheric variability with statistical models
Directory of Open Access Journals (Sweden)
C. Blume
2012-07-01
Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.
Improved model for statistical alignment
Energy Technology Data Exchange (ETDEWEB)
Miklos, I.; Toroczkai, Z. (Zoltan)
2001-01-01
The statistical approach to molecular sequence evolution involves the stochastic modeling of the substitution, insertion and deletion processes. Substitution has been modeled in a reliable way for more than three decades by using finite Markov-processes. Insertion and deletion, however, seem to be more difficult to model, and thc recent approaches cannot acceptably deal with multiple insertions and deletions. A new method based on a generating function approach is introduced to describe the multiple insertion process. The presented algorithm computes the approximate joint probability of two sequences in 0(13) running time where 1 is the geometric mean of the sequence lengths.
SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.
Chenghong, Wang; Jiang, Yichen; Mohammed, Noman; Chen, Feng; Jiang, Xiaoqian; Al Aziz, Md Momin; Sadat, Md Nazmus; Wang, Shuang
2017-01-01
As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project.
Daily precipitation statistics in regional climate models
DEFF Research Database (Denmark)
Frei, Christoph; Christensen, Jens Hesselbjerg; Déqué, Michel
2003-01-01
An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km...
Infinite Random Graphs as Statistical Mechanical Models
DEFF Research Database (Denmark)
Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria
2011-01-01
We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe a ...
Performance in population models for count data, part II: a new SAEM algorithm
Savic, Radojka; Lavielle, Marc
2009-01-01
Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (1). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13 % for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7 % for all explored scenarios. The longest CPU time was 95s for parameter estimation and 56s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009). PMID:19680795
Effects of quantum coherence on work statistics
Xu, Bao-Ming; Zou, Jian; Guo, Li-Sha; Kong, Xiang-Mu
2018-05-01
In the conventional two-point measurement scheme of quantum thermodynamics, quantum coherence is destroyed by the first measurement. But as we know the coherence really plays an important role in the quantum thermodynamics process, and how to describe the work statistics for a quantum coherent process is still an open question. In this paper, we use the full counting statistics method to investigate the effects of quantum coherence on work statistics. First, we give a general discussion and show that for a quantum coherent process, work statistics is very different from that of the two-point measurement scheme, specifically the average work is increased or decreased and the work fluctuation can be decreased by quantum coherence, which strongly depends on the relative phase, the energy level structure, and the external protocol. Then, we concretely consider a quenched one-dimensional transverse Ising model and show that quantum coherence has a more significant influence on work statistics in the ferromagnetism regime compared with that in the paramagnetism regime, so that due to the presence of quantum coherence the work statistics can exhibit the critical phenomenon even at high temperature.
Haematological changes in HIV infection with correlation to CD4 cell count
Directory of Open Access Journals (Sweden)
SS Parinitha
2012-03-01
Full Text Available BackgroundHIV infection is associated with a wide range of haematological abnormalities.Methods and ObjectivesThe objectives in this study were to study haematological changes in HIV patients and to correlate them with CD4 cell counts. Two hundred and fifty HIV positive patients referred to the haematology laboratory section for complete haemogram in whom CD4 count was done were included in the study. Haematologic parameters and CD4 counts were studied in each of these patients.Descriptive statistics were applied. Association between two attributes was calculated by chi-square test and p value less than 0.05 was considered statistically significant.ResultsAmong 250 patients, anaemia was seen in 210 (84% cases. The most common type was normocytic normochromic (40.4%. Lymphopenia was seen in 163 (65.2% cases and thrombocytopenia in 45 (18% cases. The majority of cases (70% had CD4 cell counts below 200 cells/mm3. Fifty-four cases (21.6% had CD4 counts between 200 to 499 cells/mm3 and 21 (8.4% cases had CD4 counts more than 500 cells/ mm3.In patients with CD4 counts less than 200 cells/mm3, anaemia was seen in 91.4% cases, leucopenia in 26.8%cases, lymphopenia in 80% cases and thrombocytopenia in 21.7% cases.ConclusionHaematologic manifestations of HIV infection are common and more frequent with progression of disease. The present study revealed a significant increase in the number of cases of anaemia, and lymphopenia, with decreasing CD4 cell counts. Thrombocytopenia is also seen but does not show significant increase with disease progression. The study also highlights the importance of simultaneously treating HIV patients for haematologic manifestations to reduce morbidity.
Using Christmas Bird Count data in analysis of population change
Sauer, J.R.; Link, W.A.
2002-01-01
The scientific credibility of Christmas Bird Count (CBC) results depend on the development and implementation of appropriate methods of statistical analysis. The key to any successful analysis of CBC data is to begin with a careful review of how the limitations of the data are likely to influence the results of the analysis, then to choose methods of analysis that accommodate as much as possible the limitations of the survey. For our analyses of CBC data, we develop a flexible model for effort adjustment and use information from the data to guide the selection of the best model. We include geographic structuring to accommodate the regional variation in number of samples, use a model that allows for overdispersed poisson data appropriate for counts, and employ empirical Bayes procedures to accommodate differences in quality of information in regional summaries. This generalized linear model approach is very flexible, and can be applied to a variety of studies focused on factors influencing wintering bird populations. In particular, the model can be easily modified to contain covariates, allowing for assessment of associations between CBC counts and winter weather, disturbance, and a variety of other environmental factors. These new survey analysis methods have added value in that they provide insights into changes in survey design that can enhance the value of the information. The CBC has been extremely successful as a tool for increasing public interest in birding and bird conservation. Use of the information for bird conservation creates new demands on quality of information, and it is important to maintain a dialogue between users of the information, information needs for the analyses, and survey coordinators and participants. Our work as survey analysts emphasizes the value and limitations of existing data, and provides some indications of what features of the survey could be modified to make the survey a more reliable source of bird population data. Surveys
An R2 statistic for fixed effects in the linear mixed model.
Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver
2008-12-20
Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.
Design Study of an Incinerator Ash Conveyor Counting System - 13323
International Nuclear Information System (INIS)
Jaederstroem, Henrik; Bronson, Frazier
2013-01-01
A design study has been performed for a system that should measure the Cs-137 activity in ash from an incinerator. Radioactive ash, expected to consist of both Cs-134 and Cs-137, will be transported on a conveyor belt at 0.1 m/s. The objective of the counting system is to determine the Cs-137 activity and direct the ash to the correct stream after a diverter. The decision levels are ranging from 8000 to 400000 Bq/kg and the decision error should be as low as possible. The decision error depends on the total measurement uncertainty which depends on the counting statistics and the uncertainty in the efficiency of the geometry. For the low activity decision it is necessary to know the efficiency to be able to determine if the signal from the Cs-137 is above the minimum detectable activity and that it generates enough counts to reach the desired precision. For the higher activity decision the uncertainty of the efficiency needs to be understood to minimize decision errors. The total efficiency of the detector is needed to be able to determine if the detector will be able operate at the count rate at the highest expected activity. The design study that is presented in this paper describes how the objectives of the monitoring systems were obtained, the choice of detector was made and how ISOCS (In Situ Object Counting System) mathematical modeling was used to calculate the efficiency. The ISOCS uncertainty estimator (IUE) was used to determine which parameters of the ash was important to know accurately in order to minimize the uncertainty of the efficiency. The examined parameters include the height of the ash on the conveyor belt, the matrix composition and density and relative efficiency of the detector. (authors)
Design Study of an Incinerator Ash Conveyor Counting System - 13323
Energy Technology Data Exchange (ETDEWEB)
Jaederstroem, Henrik; Bronson, Frazier [Canberra Industries Inc., 800 Research Parkway Meriden CT 06450 (United States)
2013-07-01
A design study has been performed for a system that should measure the Cs-137 activity in ash from an incinerator. Radioactive ash, expected to consist of both Cs-134 and Cs-137, will be transported on a conveyor belt at 0.1 m/s. The objective of the counting system is to determine the Cs-137 activity and direct the ash to the correct stream after a diverter. The decision levels are ranging from 8000 to 400000 Bq/kg and the decision error should be as low as possible. The decision error depends on the total measurement uncertainty which depends on the counting statistics and the uncertainty in the efficiency of the geometry. For the low activity decision it is necessary to know the efficiency to be able to determine if the signal from the Cs-137 is above the minimum detectable activity and that it generates enough counts to reach the desired precision. For the higher activity decision the uncertainty of the efficiency needs to be understood to minimize decision errors. The total efficiency of the detector is needed to be able to determine if the detector will be able operate at the count rate at the highest expected activity. The design study that is presented in this paper describes how the objectives of the monitoring systems were obtained, the choice of detector was made and how ISOCS (In Situ Object Counting System) mathematical modeling was used to calculate the efficiency. The ISOCS uncertainty estimator (IUE) was used to determine which parameters of the ash was important to know accurately in order to minimize the uncertainty of the efficiency. The examined parameters include the height of the ash on the conveyor belt, the matrix composition and density and relative efficiency of the detector. (authors)
Directory of Open Access Journals (Sweden)
Christopher J Paciorek
Full Text Available We present a gridded 8 km-resolution data product of the estimated composition of tree taxa at the time of Euro-American settlement of the northeastern United States and the statistical methodology used to produce the product from trees recorded by land surveyors. Composition is defined as the proportion of stems larger than approximately 20 cm diameter at breast height for 22 tree taxa, generally at the genus level. The data come from settlement-era public survey records that are transcribed and then aggregated spatially, giving count data. The domain is divided into two regions, eastern (Maine to Ohio and midwestern (Indiana to Minnesota. Public Land Survey point data in the midwestern region (ca. 0.8-km resolution are aggregated to a regular 8 km grid, while data in the eastern region, from Town Proprietor Surveys, are aggregated at the township level in irregularly-shaped local administrative units. The product is based on a Bayesian statistical model fit to the count data that estimates composition on the 8 km grid across the entire domain. The statistical model is designed to handle data from both the regular grid and the irregularly-shaped townships and allows us to estimate composition at locations with no data and to smooth over noise caused by limited counts in locations with data. Critically, the model also allows us to quantify uncertainty in our composition estimates, making the product suitable for applications employing data assimilation. We expect this data product to be useful for understanding the state of vegetation in the northeastern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can also serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data product is being made available at the NIS data portal as version 1.0.
Mixed deterministic statistical modelling of regional ozone air pollution
Kalenderski, Stoitchko
2011-03-17
We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..
Adaptive Maneuvering Frequency Method of Current Statistical Model
Institute of Scientific and Technical Information of China (English)
Wei Sun; Yongjian Yang
2017-01-01
Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.
Speech emotion recognition based on statistical pitch model
Institute of Scientific and Technical Information of China (English)
WANG Zhiping; ZHAO Li; ZOU Cairong
2006-01-01
A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.
Chrom, Pawel; Stec, Rafal; Bodnar, Lubomir; Szczylik, Cezary
2017-01-01
Purpose The study investigated whether a replacement of neutrophil count and platelet count by neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) within the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model would improve its prognostic accuracy. Materials and Methods This retrospective analysis included consecutive patients with metastatic renal cell carcinoma treated with first-line tyrosine kinase inhibitors. The IMDC and modified-IMDC m...
TasselNet: counting maize tassels in the wild via local counts regression network.
Lu, Hao; Cao, Zhiguo; Xiao, Yang; Zhuang, Bohan; Shen, Chunhua
2017-01-01
Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting ( MTC ) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting
TasselNet: counting maize tassels in the wild via local counts regression network
Directory of Open Access Journals (Sweden)
Hao Lu
2017-11-01
Full Text Available Abstract Background Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. Results This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large
Statistical validation of normal tissue complication probability models.
Xu, Cheng-Jian; van der Schaaf, Arjen; Van't Veld, Aart A; Langendijk, Johannes A; Schilstra, Cornelis
2012-09-01
To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use. Copyright © 2012 Elsevier Inc. All rights reserved.
Statistical Validation of Normal Tissue Complication Probability Models
Energy Technology Data Exchange (ETDEWEB)
Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schilstra, Cornelis [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Radiotherapy Institute Friesland, Leeuwarden (Netherlands)
2012-09-01
Purpose: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. Methods and Materials: A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Results: Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Conclusion: Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.
Shell model in large spaces and statistical spectroscopy
International Nuclear Information System (INIS)
Kota, V.K.B.
1996-01-01
For many nuclear structure problems of current interest it is essential to deal with shell model in large spaces. For this, three different approaches are now in use and two of them are: (i) the conventional shell model diagonalization approach but taking into account new advances in computer technology; (ii) the shell model Monte Carlo method. A brief overview of these two methods is given. Large space shell model studies raise fundamental questions regarding the information content of the shell model spectrum of complex nuclei. This led to the third approach- the statistical spectroscopy methods. The principles of statistical spectroscopy have their basis in nuclear quantum chaos and they are described (which are substantiated by large scale shell model calculations) in some detail. (author)
Advances in statistical models for data analysis
Minerva, Tommaso; Vichi, Maurizio
2015-01-01
This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.
A heuristic statistical stopping rule for iterative reconstruction in emission tomography
International Nuclear Information System (INIS)
Ben Bouallegue, F.; Mariano-Goulart, D.; Crouzet, J.F.
2013-01-01
We propose a statistical stopping criterion for iterative reconstruction in emission tomography based on a heuristic statistical description of the reconstruction process. The method was assessed for maximum likelihood expectation maximization (MLEM) reconstruction. Based on Monte-Carlo numerical simulations and using a perfectly modeled system matrix, our method was compared with classical iterative reconstruction followed by low-pass filtering in terms of Euclidian distance to the exact object, noise, and resolution. The stopping criterion was then evaluated with realistic PET data of a Hoffman brain phantom produced using the Geant4 application in emission tomography (GATE) platform for different count levels. The numerical experiments showed that compared with the classical method, our technique yielded significant improvement of the noise-resolution tradeoff for a wide range of counting statistics compatible with routine clinical settings. When working with realistic data, the stopping rule allowed a qualitatively and quantitatively efficient determination of the optimal image. Our method appears to give a reliable estimation of the optimal stopping point for iterative reconstruction. It should thus be of practical interest as it produces images with similar or better quality than classical post-filtered iterative reconstruction with a mastered computation time. (author)
Computationally efficient statistical differential equation modeling using homogenization
Hooten, Mevin B.; Garlick, Martha J.; Powell, James A.
2013-01-01
Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.
Statistical analyses to support guidelines for marine avian sampling. Final report
Kinlan, Brian P.; Zipkin, Elise; O'Connell, Allan F.; Caldow, Chris
2012-01-01
Interest in development of offshore renewable energy facilities has led to a need for high-quality, statistically robust information on marine wildlife distributions. A practical approach is described to estimate the amount of sampling effort required to have sufficient statistical power to identify species-specific “hotspots” and “coldspots” of marine bird abundance and occurrence in an offshore environment divided into discrete spatial units (e.g., lease blocks), where “hotspots” and “coldspots” are defined relative to a reference (e.g., regional) mean abundance and/or occurrence probability for each species of interest. For example, a location with average abundance or occurrence that is three times larger the mean (3x effect size) could be defined as a “hotspot,” and a location that is three times smaller than the mean (1/3x effect size) as a “coldspot.” The choice of the effect size used to define hot and coldspots will generally depend on a combination of ecological and regulatory considerations. A method is also developed for testing the statistical significance of possible hotspots and coldspots. Both methods are illustrated with historical seabird survey data from the USGS Avian Compendium Database. Our approach consists of five main components: 1. A review of the primary scientific literature on statistical modeling of animal group size and avian count data to develop a candidate set of statistical distributions that have been used or may be useful to model seabird counts. 2. Statistical power curves for one-sample, one-tailed Monte Carlo significance tests of differences of observed small-sample means from a specified reference distribution. These curves show the power to detect "hotspots" or "coldspots" of occurrence and abundance at a range of effect sizes, given assumptions which we discuss. 3. A model selection procedure, based on maximum likelihood fits of models in the candidate set, to determine an appropriate statistical
Models for probability and statistical inference theory and applications
Stapleton, James H
2007-01-01
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...
Fluctuations and correlations in statistical models of hadron production
International Nuclear Information System (INIS)
Gorenstein, M. I.
2012-01-01
An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution are introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.
Growth curve models and statistical diagnostics
Pan, Jian-Xin
2002-01-01
Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.
Compton suppression gamma-counting: The effect of count rate
Millard, H.T.
1984-01-01
Past research has shown that anti-coincidence shielded Ge(Li) spectrometers enhanced the signal-to-background ratios for gamma-photopeaks, which are situated on high Compton backgrounds. Ordinarily, an anti- or non-coincidence spectrum (A) and a coincidence spectrum (C) are collected simultaneously with these systems. To be useful in neutron activation analysis (NAA), the fractions of the photopeak counts routed to the two spectra must be constant from sample to sample to variations must be corrected quantitatively. Most Compton suppression counting has been done at low count rate, but in NAA applications, count rates may be much higher. To operate over the wider dynamic range, the effect of count rate on the ratio of the photopeak counts in the two spectra (A/C) was studied. It was found that as the count rate increases, A/C decreases for gammas not coincident with other gammas from the same decay. For gammas coincident with other gammas, A/C increases to a maximum and then decreases. These results suggest that calibration curves are required to correct photopeak areas so quantitative data can be obtained at higher count rates. ?? 1984.
International Nuclear Information System (INIS)
Parthasarathy, R.; Saisubalakshmi, D.; Mishra, G.K.; Srinivas, K.C.; Venkatasubramani, C.R.
2004-01-01
The paper describes a comprehensive nuclear pulse counting system that can cater to up to seven nuclear detector set-ups located in different places in the laboratory. Each detector set up has an interfacing module that conditions the amplifier pulses and transmits them to a common counting system. The microcontroller-based system receives these pulses through a multiplexer and counts the pulses for a user specified preset time. The system has a routine to determine detector plateau characteristics and fix the detector operating voltage. In this mode, the system collects the EHT-versus- counts data in a EHT programmed sequence and plots the profile. The system conducts the counting routine for a stipulated number of times and does all necessary statistical tests to ensure the proper functioning of the detector under test. The system also includes a test routine that checks the performance of the counting system by connecting it to a local pulse generator. The microcontroller based system interacts with a PC through RS232 communication for user interaction and reporting. (author)
Advanced data analysis in neuroscience integrating statistical and computational models
Durstewitz, Daniel
2017-01-01
This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerfu...
International Nuclear Information System (INIS)
Quadri, Andrea
2006-01-01
We elucidate the geometry of the polynomial formulation of the non-Abelian Stueckelberg mechanism. We show that a natural off-shell nilpotent Becchi-Rouet-Stora-Tyutin (BRST) differential exists allowing to implement the constraint on the σ field by means of BRST techniques. This is achieved by extending the ghost sector by an additional U(1) factor (Abelian embedding). An important consequence is that a further BRST-invariant but not gauge-invariant mass term can be written for the non-Abelian gauge fields. As all versions of the Stueckelberg theory, also the Abelian embedding formulation yields a nonpower-counting renormalizable theory in D=4. We then derive its natural power-counting renormalizable extension and show that the physical spectrum contains a physical massive scalar particle. Physical unitarity is also established. This model implements the spontaneous symmetry breaking in the Abelian embedding formalism
Directory of Open Access Journals (Sweden)
Phillip P. Allen
2014-05-01
Full Text Available Techniques that analyze biological remains from sediment sequences for environmental reconstructions are well established and widely used. Yet, identifying, counting, and recording biological evidence such as pollen grains remain a highly skilled, demanding, and time-consuming task. Standard procedure requires the classification and recording of between 300 and 500 pollen grains from each representative sample. Recording the data from a pollen count requires significant effort and focused resources from the palynologist. However, when an adaptation to the recording procedure is utilized, efficiency and time economy improve. We describe EcoCount, which represents a development in environmental data recording procedure. EcoCount is a voice activated fully customizable digital count sheet that allows the investigator to continuously interact with a field of view during the data recording. Continuous viewing allows the palynologist the opportunity to remain engaged with the essential task, identification, for longer, making pollen counting more efficient and economical. EcoCount is a versatile software package that can be used to record a variety of environmental evidence and can be installed onto different computer platforms, making the adoption by users and laboratories simple and inexpensive. The user-friendly format of EcoCount allows any novice to be competent and functional in a very short time.
Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science
Statistical analysis of the count and profitability of air conditioners.
Rady, El Houssainy A; Mohamed, Salah M; Abd Elmegaly, Alaa A
2018-08-01
This article presents the statistical analysis of the number and profitability of air conditioners in an Egyptian company. Checking the same distribution for each categorical variable has been made using Kruskal-Wallis test.
... by kidney disease) RBC destruction ( hemolysis ) due to transfusion, blood vessel injury, or other cause Leukemia Malnutrition Bone ... slight risk any time the skin is broken) Alternative Names Erythrocyte count; Red blood cell count; Anemia - RBC count Images Blood test ...
Cellular automata and statistical mechanical models
International Nuclear Information System (INIS)
Rujan, P.
1987-01-01
The authors elaborate on the analogy between the transfer matrix of usual lattice models and the master equation describing the time development of cellular automata. Transient and stationary properties of probabilistic automata are linked to surface and bulk properties, respectively, of restricted statistical mechanical systems. It is demonstrated that methods of statistical physics can be successfully used to describe the dynamic and the stationary behavior of such automata. Some exact results are derived, including duality transformations, exact mappings, disorder, and linear solutions. Many examples are worked out in detail to demonstrate how to use statistical physics in order to construct cellular automata with desired properties. This approach is considered to be a first step toward the design of fully parallel, probabilistic systems whose computational abilities rely on the cooperative behavior of their components
Growth Curve Models and Applications : Indian Statistical Institute
2017-01-01
Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...
Improving statistical reasoning theoretical models and practical implications
Sedlmeier, Peter
1999-01-01
This book focuses on how statistical reasoning works and on training programs that can exploit people''s natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.
Solar radiation data - statistical analysis and simulation models
Energy Technology Data Exchange (ETDEWEB)
Mustacchi, C; Cena, V; Rocchi, M; Haghigat, F
1984-01-01
The activities consisted in collecting meteorological data on magnetic tape for ten european locations (with latitudes ranging from 42/sup 0/ to 56/sup 0/ N), analysing the multi-year sequences, developing mathematical models to generate synthetic sequences having the same statistical properties of the original data sets, and producing one or more Short Reference Years (SRY's) for each location. The meteorological parameters examinated were (for all the locations) global + diffuse radiation on horizontal surface, dry bulb temperature, sunshine duration. For some of the locations additional parameters were available, namely, global, beam and diffuse radiation on surfaces other than horizontal, wet bulb temperature, wind velocity, cloud type, cloud cover. The statistical properties investigated were mean, variance, autocorrelation, crosscorrelation with selected parameters, probability density function. For all the meteorological parameters, various mathematical models were built: linear regression, stochastic models of the AR and the DAR type. In each case, the model with the best statistical behaviour was selected for the production of a SRY for the relevant parameter/location.
International Nuclear Information System (INIS)
Smiriga, N.G.
1976-01-01
This report compares two models for converting beta backscatter count readings into thickness measurements. The necessary formulas to be used in an unweighted and weighted regression analysis are listed. The question of whether one should perform a regression analysis using the five available standard thicknesses or whether one should, in addition to these standard thicknesses, use zero as a standard thickness is decided. A weighted regression analysis is compared with an unweighted one for each model. The ''best'' model is selected, and the conclusions of the analysis are presented
Statistical Model Checking for Biological Systems
DEFF Research Database (Denmark)
David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel
2014-01-01
Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...
Right-sizing statistical models for longitudinal data.
Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M
2015-12-01
Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).
Statistical models based on conditional probability distributions
International Nuclear Information System (INIS)
Narayanan, R.S.
1991-10-01
We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)
A statistical model for mapping morphological shape
Directory of Open Access Journals (Sweden)
Li Jiahan
2010-07-01
Full Text Available Abstract Background Living things come in all shapes and sizes, from bacteria, plants, and animals to humans. Knowledge about the genetic mechanisms for biological shape has far-reaching implications for a range spectrum of scientific disciplines including anthropology, agriculture, developmental biology, evolution and biomedicine. Results We derived a statistical model for mapping specific genes or quantitative trait loci (QTLs that control morphological shape. The model was formulated within the mixture framework, in which different types of shape are thought to result from genotypic discrepancies at a QTL. The EM algorithm was implemented to estimate QTL genotype-specific shapes based on a shape correspondence analysis. Computer simulation was used to investigate the statistical property of the model. Conclusion By identifying specific QTLs for morphological shape, the model developed will help to ask, disseminate and address many major integrative biological and genetic questions and challenges in the genetic control of biological shape and function.
International Nuclear Information System (INIS)
Povoski, Stephen P; Chapman, Gregg J; Murrey, Douglas A; Lee, Robert; Martin, Edward W; Hall, Nathan C
2013-01-01
Intraoperative detection of 18 F-FDG-avid tissue sites during 18 F-FDG-directed surgery can be very challenging when utilizing gamma detection probes that rely on a fixed target-to-background (T/B) ratio (ratiometric threshold) for determination of probe positivity. The purpose of our study was to evaluate the counting efficiency and the success rate of in situ intraoperative detection of 18 F-FDG-avid tissue sites (using the three-sigma statistical threshold criteria method and the ratiometric threshold criteria method) for three different gamma detection probe systems. Of 58 patients undergoing 18 F-FDG-directed surgery for known or suspected malignancy using gamma detection probes, we identified nine 18 F-FDG-avid tissue sites (from amongst seven patients) that were seen on same-day preoperative diagnostic PET/CT imaging, and for which each 18 F-FDG-avid tissue site underwent attempted in situ intraoperative detection concurrently using three gamma detection probe systems (K-alpha probe, and two commercially-available PET-probe systems), and then were subsequently surgical excised. The mean relative probe counting efficiency ratio was 6.9 (± 4.4, range 2.2–15.4) for the K-alpha probe, as compared to 1.5 (± 0.3, range 1.0–2.1) and 1.0 (± 0, range 1.0–1.0), respectively, for two commercially-available PET-probe systems (P < 0.001). Successful in situ intraoperative detection of 18 F-FDG-avid tissue sites was more frequently accomplished with each of the three gamma detection probes tested by using the three-sigma statistical threshold criteria method than by using the ratiometric threshold criteria method, specifically with the three-sigma statistical threshold criteria method being significantly better than the ratiometric threshold criteria method for determining probe positivity for the K-alpha probe (P = 0.05). Our results suggest that the improved probe counting efficiency of the K-alpha probe design used in conjunction with the three
Statistical model selection with “Big Data”
Directory of Open Access Journals (Sweden)
Jurgen A. Doornik
2015-12-01
Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.
Statistical shape and appearance models of bones.
Sarkalkan, Nazli; Weinans, Harrie; Zadpoor, Amir A
2014-03-01
When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone. Copyright © 2013 Elsevier Inc. All rights reserved.
Proposals of counting method for bubble detectors and their intercomparisons
International Nuclear Information System (INIS)
Ramalho, Eduardo; Silva, Ademir X.; Bellido, Luis F.; Facure, Alessandro; Pereira, Mario
2009-01-01
The study of neutron's spectrometry and dosimetry has become significantly easier due to relatively new devices called bubble detectors. Insensitive to gamma rays and composed by superheated emulsions, they still are subjects of many researches in Radiation Physics and Nuclear Engineering. In bubble detectors, either exposed to more intense neutron fields or for a long time, when more bubbles are produced, the statistical uncertainty during the dosimetric and spectrometric processes is reduced. A proposal of this nature is set up in this work, which presents ways to perform counting processes for bubble detectors and an updated proceeding to get the irradiated detectors' images in order to make the manual counting easier. Twelve BDS detectors were irradiated by RDS111 cyclotron from IEN's (Instituto de Engenharia Nuclear) and photographed using an assembly specially designed for this experiment. Counting was proceeded manually in a first moment; simultaneously, ImagePro was used in order to perform counting automatically. The bubble counting values, either manual or automatic, were compared and the time to get them and their difficult levels as well. After the bubble counting, the detectors' standardizes responses were calculated in both cases, according to BDS's manual and they were also compared. Among the results, the counting on these devices really becomes very hard at a large number of bubbles, besides higher variations in counting of many bubbles. Because of the good agreement between manual counting and the custom program, the last one revealed a good alternative in practical and economical levels. Despite the good results, the custom program needs of more adjustments in order to achieve more accuracy on higher counting on bubble detectors for neutron measurement applications. (author)
Maximum Likelihood Time-of-Arrival Estimation of Optical Pulses via Photon-Counting Photodetectors
Erkmen, Baris I.; Moision, Bruce E.
2010-01-01
Many optical imaging, ranging, and communications systems rely on the estimation of the arrival time of an optical pulse. Recently, such systems have been increasingly employing photon-counting photodetector technology, which changes the statistics of the observed photocurrent. This requires time-of-arrival estimators to be developed and their performances characterized. The statistics of the output of an ideal photodetector, which are well modeled as a Poisson point process, were considered. An analytical model was developed for the mean-square error of the maximum likelihood (ML) estimator, demonstrating two phenomena that cause deviations from the minimum achievable error at low signal power. An approximation was derived to the threshold at which the ML estimator essentially fails to provide better than a random guess of the pulse arrival time. Comparing the analytic model performance predictions to those obtained via simulations, it was verified that the model accurately predicts the ML performance over all regimes considered. There is little prior art that attempts to understand the fundamental limitations to time-of-arrival estimation from Poisson statistics. This work establishes both a simple mathematical description of the error behavior, and the associated physical processes that yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior.
Statistical models and NMR analysis of polymer microstructure
Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...
Modelling T4 cell count as a marker of HIV progression in the absence of any defence mechanism
Directory of Open Access Journals (Sweden)
VSS Yadavalli
2010-12-01
Full Text Available The T4 cell count, which is considered one of the markers of disease progression in an HIV infected individual, is modelled in this paper. The World Health Organisation has recently advocated that countries encourage HIV infected individuals to commence antiretroviral treatments once their T4 cell count drops below 350 cells per ml of blood (this threshold was formerly 200 cells per ml of blood. This recommendation is made because when the T4 cell count is low, the T4 cells are unable to mount an effective immune response against antigens and any such foreign matters in the body, and consequently the individual becomes susceptible to opportunistic infections and lymphomas. A stochastic catastrophe model is developed in this paper to obtain the mean, variance and covariance of the uninfected, infected and lysed T4 cells. The amount of toxin produced in an HIV infected person from the time of infection to a later time may also be obtained from the model. Numerical illustrations of the correlation structures between uninfected and infected T4 cells, and between the infected and lysed T4 cells are also presented.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
Workshop on Model Uncertainty and its Statistical Implications
1988-01-01
In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.
Kolmogorov complexity, pseudorandom generators and statistical models testing
Czech Academy of Sciences Publication Activity Database
Šindelář, Jan; Boček, Pavel
2002-01-01
Roč. 38, č. 6 (2002), s. 747-759 ISSN 0023-5954 R&D Projects: GA ČR GA102/99/1564 Institutional research plan: CEZ:AV0Z1075907 Keywords : Kolmogorov complexity * pseudorandom generators * statistical models testing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.341, year: 2002
International Nuclear Information System (INIS)
Starkov, V. N.; Semenov, A. A.; Gomonay, H. V.
2009-01-01
We demonstrate a practical possibility of loss compensation in measured photocounting statistics in the presence of dark counts and background radiation noise. It is shown that satisfactory results are obtained even in the case of low detection efficiency and large experimental errors.
Visualization of the variability of 3D statistical shape models by animation.
Lamecker, Hans; Seebass, Martin; Lange, Thomas; Hege, Hans-Christian; Deuflhard, Peter
2004-01-01
Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.
Applied systems ecology: models, data, and statistical methods
Energy Technology Data Exchange (ETDEWEB)
Eberhardt, L L
1976-01-01
In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.
Generalized estimators of avian abundance from count survey data
Directory of Open Access Journals (Sweden)
Royle, J. A.
2004-01-01
Full Text Available I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture-recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.
The use of statistical models in heavy-ion reactions studies
International Nuclear Information System (INIS)
Stokstad, R.G.
1984-01-01
This chapter reviews the use of statistical models to describe nuclear level densities and the decay of equilibrated nuclei. The statistical models of nuclear structure and nuclear reactions presented here have wide application in the analysis of heavy-ion reaction data. Applications are illustrated with examples of gamma-ray decay, the emission of light particles and heavier clusters of nucleons, and fission. In addition to the compound nucleus, the treatment of equilibrated fragments formed in binary reactions is discussed. The statistical model is shown to be an important tool for the identification of products from nonequilibrium decay
Multivariate statistical modelling based on generalized linear models
Fahrmeir, Ludwig
1994-01-01
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...
Linear mixed models a practical guide using statistical software
West, Brady T; Galecki, Andrzej T
2006-01-01
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo
Active Learning with Statistical Models.
1995-01-01
Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with
Statistical analysis of angular correlation measurements
International Nuclear Information System (INIS)
Oliveira, R.A.A.M. de.
1986-01-01
Obtaining the multipole mixing ratio, δ, of γ transitions in angular correlation measurements is a statistical problem characterized by the small number of angles in which the observation is made and by the limited statistic of counting, α. The inexistence of a sufficient statistics for the estimator of δ, is shown. Three different estimators for δ were constructed and their properties of consistency, bias and efficiency were tested. Tests were also performed in experimental results obtained in γ-γ directional correlation measurements. (Author) [pt
Immature germ cells in semen - correlation with total sperm count and sperm motility
Directory of Open Access Journals (Sweden)
Priya S Patil
2013-01-01
Conclusions: Round cells in semen can be differentiated into immature germ cells and leucocytes using simple staining methods. The differential counts mentioned in a semen report give valuable and clinically relevant information. In this study, we observed a negative correlation between total count and immature germ cells, as well as sperm motility and shedding of immature germ cells. The latter was statistically significant with a P value 0.000.
Parametric analysis of the statistical model of the stick-slip process
Lima, Roberta; Sampaio, Rubens
2017-06-01
In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.
Povoski, Stephen P; Chapman, Gregg J; Murrey, Douglas A; Lee, Robert; Martin, Edward W; Hall, Nathan C
2013-03-04
Intraoperative detection of (18)F-FDG-avid tissue sites during 18F-FDG-directed surgery can be very challenging when utilizing gamma detection probes that rely on a fixed target-to-background (T/B) ratio (ratiometric threshold) for determination of probe positivity. The purpose of our study was to evaluate the counting efficiency and the success rate of in situ intraoperative detection of (18)F-FDG-avid tissue sites (using the three-sigma statistical threshold criteria method and the ratiometric threshold criteria method) for three different gamma detection probe systems. Of 58 patients undergoing (18)F-FDG-directed surgery for known or suspected malignancy using gamma detection probes, we identified nine (18)F-FDG-avid tissue sites (from amongst seven patients) that were seen on same-day preoperative diagnostic PET/CT imaging, and for which each (18)F-FDG-avid tissue site underwent attempted in situ intraoperative detection concurrently using three gamma detection probe systems (K-alpha probe, and two commercially-available PET-probe systems), and then were subsequently surgical excised. The mean relative probe counting efficiency ratio was 6.9 (± 4.4, range 2.2-15.4) for the K-alpha probe, as compared to 1.5 (± 0.3, range 1.0-2.1) and 1.0 (± 0, range 1.0-1.0), respectively, for two commercially-available PET-probe systems (P < 0.001). Successful in situ intraoperative detection of 18F-FDG-avid tissue sites was more frequently accomplished with each of the three gamma detection probes tested by using the three-sigma statistical threshold criteria method than by using the ratiometric threshold criteria method, specifically with the three-sigma statistical threshold criteria method being significantly better than the ratiometric threshold criteria method for determining probe positivity for the K-alpha probe (P = 0.05). Our results suggest that the improved probe counting efficiency of the K-alpha probe design used in conjunction with the three-sigma statistical
Cicconi-Hogan, K M; Gamroth, M; Richert, R; Ruegg, P L; Stiglbauer, K E; Schukken, Y H
2013-01-01
The purpose of this study was to assess the association of bulk tank milk standard plate counts, bulk tank coliform counts (CC), and the presence of Staphylococcus aureus in bulk tank milk with various management and farm characteristics on organic and conventional dairy farms throughout New York, Wisconsin, and Oregon. Data from size-matched organic farms (n=192), conventional nongrazing farms (n=64), and conventional grazing farms (n=36) were collected at a single visit for each farm. Of the 292 farms visited, 290 bulk tank milk samples were collected. Statistical models were created using data from all herds in the study, as well as exclusively for the organic subset of herds. Because of incomplete data, 267 of 290 herds were analyzed for total herd modeling, and 173 of 190 organic herds were analyzed for the organic herd modeling. Overall, more bulk tanks from organic farms had Staph. aureus cultured from them (62% of organic herds, 42% conventional nongrazing herds, and 43% of conventional grazing herds), whereas fewer organic herds had a high CC, defined as ≥50 cfu/mL, than conventional farms in the study. A high standard plate count (×1,000 cfu/mL) was associated with decreased body condition score of adult cows and decreased milk production in both models. Several variables were significant only in the model created using all herds or only in organic herds. The presence of Staph. aureus in the bulk tank milk was associated with fewer people treating mastitis, increased age of housing, and a higher percentage of cows with 3 or fewer teats in both the organic and total herd models. The Staph. aureus total herd model also showed a relationship with fewer first-lactation animals, higher hock scores, and less use of automatic takeoffs at milking. High bulk tank CC was related to feeding a total mixed ration and using natural service in nonlactating heifers in both models. Overall, attentive management and use of outside resources were useful with regard to CC
International Nuclear Information System (INIS)
Robison, W.L.; Sun, C.
1997-01-01
Rongelap and Utirik Atolls were contaminated on 1 March 1954, by a U.S. nuclear test at Bikini Atoll code named BRAVO. The people at both atolls were removed from their atolls in the first few days after the detonation and were returned to their atolls at different times. Detailed studies have been carried out over the years by Lawrence Livermore National Laboratory (LLNL) to determine the radiological conditions at the atolls and estimate the doses to the populations. The contribution of each exposure pathway and radionuclide have been evaluated. All dose assessments show that the major potential contribution to the estimated dose is 137 Cs uptake via the terrestrial food chain. Brookhaven National Laboratory (BNL) has carried out an extensive whole body counting program at both atolls over several years to directly measure the 137 Cs body burden. Here we compare the estimates of the body burdens from the LLNL environmental method with body burdens measured by the BNL whole body counting method. The combination of the results from both methods is used to evaluate proposed diet models to establish more realistic dose assessments. Very good agreement is achieved between the two methods with a diet model that includes both local and imported foods. Other diet models greatly overestimate the body burdens (i.e., dose) observed by whole body counting. The upper 95% confidence limit of interindividual variability around the population mean value based on the environmental method is similar to that calculated from direct measurement by whole body counting. Moreover, the uncertainty in the population mean value based on the environmental method is in very good agreement with the whole body counting data. This provides additional confidence in extrapolating the estimated doses calculated by the environmental method to other islands and atolls. 46 refs., 8 figs., 5 tabs
Liao, Yi; Ma, Xiao-Dong
2018-03-01
We study two aspects of higher dimensional operators in standard model effective field theory. We first introduce a perturbative power counting rule for the entries in the anomalous dimension matrix of operators with equal mass dimension. The power counting is determined by the number of loops and the difference of the indices of the two operators involved, which in turn is defined by assuming that all terms in the standard model Lagrangian have an equal perturbative power. Then we show that the operators with the lowest index are unique at each mass dimension d, i.e., (H † H) d/2 for even d ≥ 4, and (LT∈ H)C(LT∈ H) T (H † H)(d-5)/2 for odd d ≥ 5. Here H, L are the Higgs and lepton doublet, and ∈, C the antisymmetric matrix of rank two and the charge conjugation matrix, respectively. The renormalization group running of these operators can be studied separately from other operators of equal mass dimension at the leading order in power counting. We compute their anomalous dimensions at one loop for general d and find that they are enhanced quadratically in d due to combinatorics. We also make connections with classification of operators in terms of their holomorphic and anti-holomorphic weights. Supported by the National Natural Science Foundation of China under Grant Nos. 11025525, 11575089, and by the CAS Center for Excellence in Particle Physics (CCEPP)
Multiplicity counting from fission detector signals with time delay effects
Nagy, L.; Pázsit, I.; Pál, L.
2018-03-01
In recent work, we have developed the theory of using the first three auto- and joint central moments of the currents of up to three fission chambers to extract the singles, doubles and triples count rates of traditional multiplicity counting (Pázsit and Pál, 2016; Pázsit et al., 2016). The objective is to elaborate a method for determining the fissile mass, neutron multiplication, and (α, n) neutron emission rate of an unknown assembly of fissile material from the statistics of the fission chamber signals, analogous to the traditional multiplicity counting methods with detectors in the pulse mode. Such a method would be an alternative to He-3 detector systems, which would be free from the dead time problems that would be encountered in high counting rate applications, for example the assay of spent nuclear fuel. A significant restriction of our previous work was that all neutrons born in a source event (spontaneous fission) were assumed to be detected simultaneously, which is not fulfilled in reality. In the present work, this restriction is eliminated, by assuming an independent, identically distributed random time delay for all neutrons arising from one source event. Expressions are derived for the same auto- and joint central moments of the detector current(s) as in the previous case, expressed with the singles, doubles, and triples (S, D and T) count rates. It is shown that if the time-dispersion of neutron detections is of the same order of magnitude as the detector pulse width, as they typically are in measurements of fast neutrons, the multiplicity rates can still be extracted from the moments of the detector current, although with more involved calibration factors. The presented formulae, and hence also the performance of the proposed method, are tested by both analytical models of the time delay as well as with numerical simulations. Methods are suggested also for the modification of the method for large time delay effects (for thermalised neutrons).
Determining random counts in liquid scintillation counting
International Nuclear Information System (INIS)
Horrocks, D.L.
1979-01-01
During measurements involving coincidence counting techniques, errors can arise due to the detection of chance or random coincidences in the multiple detectors used. A method and the electronic circuits necessary are here described for eliminating this source of error in liquid scintillation detectors used in coincidence counting. (UK)
Statistical properties of laser light scattering in Brownian medium
International Nuclear Information System (INIS)
Suwono; Santoso, Budi; Baiquni, A.
1983-01-01
Relationship between statistical properties of laser light scattering in Brownian medium and photon-counting distributions are described in detail. A coherence optical detection has been constructed and by using photon-counting technique the ensemble distribution of the scattered field within space and time coherence has been measured. Good agreement between theory and experiment is shown. (author)
Temporal aggregation of migration counts can improve accuracy and precision of trends
Directory of Open Access Journals (Sweden)
Tara L. Crewe
2016-12-01
Full Text Available Temporal replicate counts are often aggregated to improve model fit by reducing zero-inflation and count variability, and in the case of migration counts collected hourly throughout a migration, allows one to ignore nonindependence. However, aggregation can represent a loss of potentially useful information on the hourly or seasonal distribution of counts, which might impact our ability to estimate reliable trends. We simulated 20-year hourly raptor migration count datasets with known rate of change to test the effect of aggregating hourly counts to daily or annual totals on our ability to recover known trend. We simulated data for three types of species, to test whether results varied with species abundance or migration strategy: a commonly detected species, e.g., Northern Harrier, Circus cyaneus; a rarely detected species, e.g., Peregrine Falcon, Falco peregrinus; and a species typically counted in large aggregations with overdispersed counts, e.g., Broad-winged Hawk, Buteo platypterus. We compared accuracy and precision of estimated trends across species and count types (hourly/daily/annual using hierarchical models that assumed a Poisson, negative binomial (NB or zero-inflated negative binomial (ZINB count distribution. We found little benefit of modeling zero-inflation or of modeling the hourly distribution of migration counts. For the rare species, trends analyzed using daily totals and an NB or ZINB data distribution resulted in a higher probability of detecting an accurate and precise trend. In contrast, trends of the common and overdispersed species benefited from aggregation to annual totals, and for the overdispersed species in particular, trends estimating using annual totals were more precise, and resulted in lower probabilities of estimating a trend (1 in the wrong direction, or (2 with credible intervals that excluded the true trend, as compared with hourly and daily counts.
Statistical methods for the analysis of a screening test for chronic beryllium disease
Energy Technology Data Exchange (ETDEWEB)
Frome, E.L.; Neubert, R.L. [Oak Ridge National Lab., TN (United States). Mathematical Sciences Section; Smith, M.H.; Littlefield, L.G.; Colyer, S.P. [Oak Ridge Inst. for Science and Education, TN (United States). Medical Sciences Div.
1994-10-01
The lymphocyte proliferation test (LPT) is a noninvasive screening procedure used to identify persons who may have chronic beryllium disease. A practical problem in the analysis of LPT well counts is the occurrence of outlying data values (approximately 7% of the time). A log-linear regression model is used to describe the expected well counts for each set of test conditions. The variance of the well counts is proportional to the square of the expected counts, and two resistant regression methods are used to estimate the parameters of interest. The first approach uses least absolute values (LAV) on the log of the well counts to estimate beryllium stimulation indices (SIs) and the coefficient of variation. The second approach uses a resistant regression version of maximum quasi-likelihood estimation. A major advantage of the resistant regression methods is that it is not necessary to identify and delete outliers. These two new methods for the statistical analysis of the LPT data and the outlier rejection method that is currently being used are applied to 173 LPT assays. The authors strongly recommend the LAV method for routine analysis of the LPT.
Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams
Directory of Open Access Journals (Sweden)
Chongshi Gu
2013-01-01
Full Text Available Cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Using the parametric statistical model of safety monitoring of cracks in concrete dams, with the help of the semi-parametric statistical theory, and considering the abnormal behaviors of these cracks, the semi-parametric statistical model of safety monitoring of concrete dam cracks is established to overcome the limitation of the parametric model in expressing the objective model. Previous projects show that the semi-parametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semi-parametric statistical model is equivalent to that of the parametric statistical model. The modeling of the semi-parametric statistical model is simple, has a reasonable principle, and has a strong practicality, with a good application prospect in the actual project.
Statistical models for competing risk analysis
International Nuclear Information System (INIS)
Sather, H.N.
1976-08-01
Research results on three new models for potential applications in competing risks problems. One section covers the basic statistical relationships underlying the subsequent competing risks model development. Another discusses the problem of comparing cause-specific risk structure by competing risks theory in two homogeneous populations, P1 and P2. Weibull models which allow more generality than the Berkson and Elveback models are studied for the effect of time on the hazard function. The use of concomitant information for modeling single-risk survival is extended to the multiple failure mode domain of competing risks. The model used to illustrate the use of this methodology is a life table model which has constant hazards within pre-designated intervals of the time scale. Two parametric models for bivariate dependent competing risks, which provide interesting alternatives, are proposed and examined
SoS contract verification using statistical model checking
Directory of Open Access Journals (Sweden)
Alessandro Mignogna
2013-11-01
Full Text Available Exhaustive formal verification for systems of systems (SoS is impractical and cannot be applied on a large scale. In this paper we propose to use statistical model checking for efficient verification of SoS. We address three relevant aspects for systems of systems: 1 the model of the SoS, which includes stochastic aspects; 2 the formalization of the SoS requirements in the form of contracts; 3 the tool-chain to support statistical model checking for SoS. We adapt the SMC technique for application to heterogeneous SoS. We extend the UPDM/SysML specification language to express the SoS requirements that the implemented strategies over the SoS must satisfy. The requirements are specified with a new contract language specifically designed for SoS, targeting a high-level English- pattern language, but relying on an accurate semantics given by the standard temporal logics. The contracts are verified against the UPDM/SysML specification using the Statistical Model Checker (SMC PLASMA combined with the simulation engine DESYRE, which integrates heterogeneous behavioral models through the functional mock-up interface (FMI standard. The tool-chain allows computing an estimation of the satisfiability of the contracts by the SoS. The results help the system architect to trade-off different solutions to guide the evolution of the SoS.
Complex Data Modeling and Computationally Intensive Statistical Methods
Mantovan, Pietro
2010-01-01
The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici
Immature germ cells in semen - correlation with total sperm count and sperm motility.
Patil, Priya S; Humbarwadi, Rajendra S; Patil, Ashalata D; Gune, Anita R
2013-07-01
Current data regarding infertility suggests that male factor contributes up to 30% of the total cases of infertility. Semen analysis reveals the presence of spermatozoa as well as a number of non-sperm cells, presently being mentioned in routine semen report as "round cells" without further differentiating them into leucocytes or immature germ cells. The aim of this work was to study a simple, cost-effective, and convenient method for differentiating the round cells in semen into immature germ cells and leucocytes and correlating them with total sperm counts and motility. Semen samples from 120 males, who had come for investigation for infertility, were collected, semen parameters recorded, and stained smears studied for different round cells. Statistical analysis of the data was done to correlate total sperm counts and sperm motility with the occurrence of immature germ cells and leucocytes. The average shedding of immature germ cells in different groups with normal and low sperm counts was compared. The clinical significance of "round cells" in semen and their differentiation into leucocytes and immature germ cells are discussed. Round cells in semen can be differentiated into immature germ cells and leucocytes using simple staining methods. The differential counts mentioned in a semen report give valuable and clinically relevant information. In this study, we observed a negative correlation between total count and immature germ cells, as well as sperm motility and shedding of immature germ cells. The latter was statistically significant with a P value 0.000.
A statistical model for porous structure of rocks
Institute of Scientific and Technical Information of China (English)
JU Yang; YANG YongMing; SONG ZhenDuo; XU WenJing
2008-01-01
The geometric features and the distribution properties of pores in rocks were In-vestigated by means of CT scanning tests of sandstones. The centroidal coordl-nares of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob-ability density functions upon which the random distribution of pore position, dis-tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex-amine the stress distribution, the pattern of element failure and the inoaculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.
A statistical model for porous structure of rocks
Institute of Scientific and Technical Information of China (English)
2008-01-01
The geometric features and the distribution properties of pores in rocks were in- vestigated by means of CT scanning tests of sandstones. The centroidal coordi- nates of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob- ability density functions upon which the random distribution of pore position, dis- tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex- amine the stress distribution, the pattern of element failure and the inosculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.
Bayesian inference from count data using discrete uniform priors.
Directory of Open Access Journals (Sweden)
Federico Comoglio
Full Text Available We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. We report a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. Our derivation yields a computationally feasible formula that can prove useful in a variety of statistical problems involving absolute quantification under uncertainty. We implemented our algorithm in the R package dupiR and compared it with a previously proposed Bayesian method based on a Gamma prior. As a showcase, we demonstrate that our inference framework can be used to estimate bacterial survival curves from measurements characterized by extremely low or zero counts and rather high sampling fractions. All in all, we provide a versatile, general purpose algorithm to infer population sizes from count data, which can find application in a broad spectrum of biological and physical problems.
(ajst) statistical mechanics model for orientational
African Journals Online (AJOL)
Science and Engineering Series Vol. 6, No. 2, pp. 94 - 101. STATISTICAL MECHANICS MODEL FOR ORIENTATIONAL. MOTION OF TWO-DIMENSIONAL RIGID ROTATOR. Malo, J.O. ... there is no translational motion and that they are well separated so .... constant and I is the moment of inertia of a linear rotator. Thus, the ...
A computer simulation used to investigate optimization in low level counting
International Nuclear Information System (INIS)
Brown, R.C.; Kephart, G.S.
1984-01-01
The differential form of the interval distribution for randomly spaced events such as radioactive decay is represented as dP/sub t/=ae - /supat/dt, the Poisson distribution. As applied to radioactive decay, this states that the probability (dP/sub t/) of the duration of a particular interval (elapsed time between counts) will be between t and t+dt as a function of the count rate (a). Thus a logarithmic transformation of this probability distribution results in a linear function whose slope and intercept are defined by the count rate. The effort expended in defining the interval distribution of a given radiation measurement equates in the laboratory to measuring and accumulating discrete time intervals between events rather than the usual approach of counting events per unit time. It follows from basic information theory that this greater effort should result in an improved statistical confidence in determinations of the ''true'' count rate (a). Using a random number generator as an analog of the discrete decay event, the authors have devised a Monte Carlo approach to investigate application of the above theory to the low level counting situation. This investigative approach is well suited to sensitivity analyses such that any constraints on proposed optimization techniques can be well defined prior to introducing these methods into the counting requirements in the laboratory
Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects
Directory of Open Access Journals (Sweden)
Shuai Luo
2016-02-01
Full Text Available Bioelectrochemical systems (BES are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.
Woody, Carol Ann; Johnson, D.H.; Shrier, Brianna M.; O'Neal, Jennifer S.; Knutzen, John A.; Augerot, Xanthippe; O'Neal, Thomas A.; Pearsons, Todd N.
2007-01-01
Counting towers provide an accurate, low-cost, low-maintenance, low-technology, and easily mobilized escapement estimation program compared to other methods (e.g., weirs, hydroacoustics, mark-recapture, and aerial surveys) (Thompson 1962; Siebel 1967; Cousens et al. 1982; Symons and Waldichuk 1984; Anderson 2000; Alaska Department of Fish and Game 2003). Counting tower data has been found to be consistent with that of digital video counts (Edwards 2005). Counting towers do not interfere with natural fish migration patterns, nor are fish handled or stressed; however, their use is generally limited to clear rivers that meet specific site selection criteria. The data provided by counting tower sampling allow fishery managers to determine reproductive population size, estimate total return (escapement + catch) and its uncertainty, evaluate population productivity and trends, set harvest rates, determine spawning escapement goals, and forecast future returns (Alaska Department of Fish and Game 1974-2000 and 1975-2004). The number of spawning fish is determined by subtracting subsistence, sport-caught fish, and prespawn mortality from the total estimated escapement. The methods outlined in this protocol for tower counts can be used to provide reasonable estimates ( plus or minus 6%-10%) of reproductive salmon population size and run timing in clear rivers.
Hincks, Ian; Granade, Christopher; Cory, David G.
2018-01-01
The analysis of photon count data from the standard nitrogen vacancy (NV) measurement process is treated as a statistical inference problem. This has applications toward gaining better and more rigorous error bars for tasks such as parameter estimation (e.g. magnetometry), tomography, and randomized benchmarking. We start by providing a summary of the standard phenomenological model of the NV optical process in terms of Lindblad jump operators. This model is used to derive random variables describing emitted photons during measurement, to which finite visibility, dark counts, and imperfect state preparation are added. NV spin-state measurement is then stated as an abstract statistical inference problem consisting of an underlying biased coin obstructed by three Poisson rates. Relevant frequentist and Bayesian estimators are provided, discussed, and quantitatively compared. We show numerically that the risk of the maximum likelihood estimator is well approximated by the Cramér-Rao bound, for which we provide a simple formula. Of the estimators, we in particular promote the Bayes estimator, owing to its slightly better risk performance, and straightforward error propagation into more complex experiments. This is illustrated on experimental data, where quantum Hamiltonian learning is performed and cross-validated in a fully Bayesian setting, and compared to a more traditional weighted least squares fit.
Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.
2017-01-01
Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of
Validation of statistical models for creep rupture by parametric analysis
Energy Technology Data Exchange (ETDEWEB)
Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)
2012-01-15
Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).
Fractional counts-the simulation of low probability events
International Nuclear Information System (INIS)
Coldwell, R.L.; Lasche, G.P.; Jadczyk, A.
2001-01-01
The code RobSim has been added to RobWin.1 It simulates spectra resulting from gamma rays striking an array of detectors made up of different components. These are frequently used to set coincidence and anti-coincidence windows that decide if individual events are part of the signal. The first problem addressed is the construction of the detector. Then owing to the statistical nature of the responses of these elements there is a random nature in the response that can be taken into account by including fractional counts in the output spectrum. This somewhat complicates the error analysis, as Poisson statistics are no longer applicable
Statistical modelling of transcript profiles of differentially regulated genes
Directory of Open Access Journals (Sweden)
Sergeant Martin J
2008-07-01
Full Text Available Abstract Background The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t = A + (B + CtRt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data
Study on advancement of in vivo counting using mathematical simulation
Energy Technology Data Exchange (ETDEWEB)
Kinase, Sakae [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
2003-05-01
To obtain an assessment of the committed effective dose, individual monitoring for the estimation of intakes of radionuclides is required. For individual monitoring of exposure to intakes of radionuclides, direct measurement of radionuclides in the body - in vivo counting- is very useful. To advance in a precision in vivo counting which fulfills the requirements of ICRP 1990 recommendations, some problems, such as the investigation of uncertainties in estimates of body burdens by in vivo counting, and the selection of the way to improve the precision, have been studied. In the present study, a calibration technique for in vivo counting application using Monte Carlo simulation was developed. The advantage of the technique is that counting efficiency can be obtained for various shapes and sizes that are very difficult to change for phantoms. To validate the calibration technique, the response functions and counting efficiencies of a whole-body counter installed in JAERI were evaluated using the simulation and measurements. Consequently, the calculations are in good agreement with the measurements. The method for the determination of counting efficiency curves as a function of energy was developed using the present technique and a physiques correction equation was derived from the relationship between parameters of correction factor and counting efficiencies of the JAERI whole-body counter. The uncertainties in body burdens of {sup 137}Cs estimated with the JAERI whole-body counter were also investigated using the Monte Carlo simulation and measurements. It was found that the uncertainties of body burdens estimated with the whole-body counter are strongly dependent on various sources of uncertainty such as radioactivity distribution within the body and counting statistics. Furthermore, the evaluation method of the peak efficiencies of a Ge semi-conductor detector was developed by Monte Carlo simulation for optimum arrangement of Ge semi-conductor detectors for
Acceleration transforms and statistical kinetic models
International Nuclear Information System (INIS)
LuValle, M.J.; Welsher, T.L.; Svoboda, K.
1988-01-01
For a restricted class of problems a mathematical model of microscopic degradation processes, statistical kinetics, is developed and linked through acceleration transforms to the information which can be obtained from a system in which the only observable sign of degradation is sudden and catastrophic failure. The acceleration transforms were developed in accelerated life testing applications as a tool for extrapolating from the observable results of an accelerated life test to the dynamics of the underlying degradation processes. A particular concern of a physicist attempting to interpreted the results of an analysis based on acceleration transforms is determining the physical species involved in the degradation process. These species may be (a) relatively abundant or (b) relatively rare. The main results of this paper are a theorem showing that for an important subclass of statistical kinetic models, acceleration transforms cannot be used to distinguish between cases a and b, and an example showing that in some cases falling outside the restrictions of the theorem, cases a and b can be distinguished by their acceleration transforms
DEFF Research Database (Denmark)
Njage, Patrick Murigu Kamau; Sawe, Chemutai Tonui; Onyango, Cecilia Moraa
2017-01-01
assessment scheme and statistical modeling were used to systematically assess the microbial performance of core control and assurance activities in five Kenyan fresh produce processing and export companies. Generalized linear mixed models and correlated random-effects joint models for multivariate clustered...... the maximum safety level for environmental samples. Escherichia coli was detected in five of the six CSLs, including the final product. Among the processing-environment samples, the hand or glove swabs of personnel revealed a higher level of predicted contamination with E. coli, and 80% of the factories were...... of contamination with coliforms in water at the inlet than in the final rinse water. Four (80%) of the five assessed processors had poor to unacceptable counts of Enterobacteriaceae on processing surfaces. Personnel-, equipment-, and product-related hygiene measures to improve the performance of preventive...
Statistical models describing the energy signature of buildings
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Thavlov, Anders
2010-01-01
Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i...... or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA......-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i...
Müller-Kirsten, Harald J W
2013-01-01
Statistics links microscopic and macroscopic phenomena, and requires for this reason a large number of microscopic elements like atoms. The results are values of maximum probability or of averaging. This introduction to statistical physics concentrates on the basic principles, and attempts to explain these in simple terms supplemented by numerous examples. These basic principles include the difference between classical and quantum statistics, a priori probabilities as related to degeneracies, the vital aspect of indistinguishability as compared with distinguishability in classical physics, the differences between conserved and non-conserved elements, the different ways of counting arrangements in the three statistics (Maxwell-Boltzmann, Fermi-Dirac, Bose-Einstein), the difference between maximization of the number of arrangements of elements, and averaging in the Darwin-Fowler method. Significant applications to solids, radiation and electrons in metals are treated in separate chapters, as well as Bose-Eins...
Kids Count: The State of the Child in Tennessee, 1994.
Tennessee State Commission on Children and Youth, Nashville.
This Kids Count report examines trends in the well being of Tennessee's children. The statistical portrait is based on 23 indicators of child well being: (1) single-parent families; (2) family income/poverty; (3) children receiving Aid to Families with Dependent Children; (4) students participating in school nutrition programs; (5) teen…
STATISTICAL MODELS OF REPRESENTING INTELLECTUAL CAPITAL
Directory of Open Access Journals (Sweden)
Andreea Feraru
2016-06-01
Full Text Available This article entitled Statistical Models of Representing Intellectual Capital approaches and analyses the concept of intellectual capital, as well as the main models which can support enterprisers/managers in evaluating and quantifying the advantages of intellectual capital. Most authors examine intellectual capital from a static perspective and focus on the development of its various evaluation models. In this chapter we surveyed the classical static models: Sveiby, Edvisson, Balanced Scorecard, as well as the canonical model of intellectual capital. Among the group of static models for evaluating organisational intellectual capital the canonical model stands out. This model enables the structuring of organisational intellectual capital in: human capital, structural capital and relational capital. Although the model is widely spread, it is a static one and can thus create a series of errors in the process of evaluation, because all the three entities mentioned above are not independent from the viewpoint of their contents, as any logic of structuring complex entities requires.
Improvement and automatization of a proportional alpha-beta counting system - FAG
International Nuclear Information System (INIS)
German, U.; Levinson, S.; Pelled, O.; Shemesh, Y.; Assido, H.
1997-01-01
An alpha and beta counting system - FAG*, for planchette samples is operated at the Health Physics department's laboratory of the NRCN. The original operation mode of the system was based on manual tasks handled by the FHT1 100 electronics. An option for a basic computer keyboard operation was available too. A computer with an appropriate I/O card was connected to the system and a new operating program was developed which enables full automatic control of the various components. The program includes activity calculations and statistical checks as well as data management. A bar-code laser system for sample number reading was integrated into the Alpha-Beta automatic counting system. The sample identification by means of an attached bar-code label enables unmistakable and reliable attribution of results to the counted sample. authors)
Thogmartin, W.E.; Knutson, M.G.
2007-01-01
Much of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species. ?? 2006 Springer Science+Business Media B.V.
Statistical mechanics of directed models of polymers in the square lattice
Rensburg, J V
2003-01-01
Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce...
Study of the effects of photoelectron statistics on Thomson scattering data
International Nuclear Information System (INIS)
Hart, G.W.; Levinton, F.M.; McNeill, D.H.
1986-01-01
A computer code has been developed which simulates a Thomson scattering measurement, from the counting statistics of the input channels through the mathematical analysis of the data. The scattered and background signals in each of the wavelength channels are assumed to obey Poisson statistics, and the spectral data are fitted to a Gaussian curve using a nonlinear least-squares fitting algorithm. This method goes beyond the usual calculation of the signal-to-noise ratio for the hardware and gives a quantitative measure of the effect of the noise on the final measurement. This method is applicable to Thomson scattering measurements in which the signal-to-noise ratio is low due to either low signal or high background. Thomson scattering data from the S-1 spheromak have been compared to this simulation, and they have been found to be in good agreement. This code has proven to be useful in assessing the effects of counting statistics relative to shot-to-shot variability in producing the observed spread in the data. It was also useful for designing improvements for the S-1 Thomson scattering system, and this method would be applicable to any measurement affected by counting statistics
The epistemology of mathematical and statistical modeling: a quiet methodological revolution.
Rodgers, Joseph Lee
2010-01-01
A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models. Copyrigiht 2009 APA, all rights reserved.
Establishing statistical models of manufacturing parameters
International Nuclear Information System (INIS)
Senevat, J.; Pape, J.L.; Deshayes, J.F.
1991-01-01
This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature
Carb counting; Carbohydrate-controlled diet; Diabetic diet; Diabetes-counting carbohydrates ... Many foods contain carbohydrates (carbs), including: Fruit and fruit juice Cereal, bread, pasta, and rice Milk and milk products, soy milk Beans, legumes, ...
Analysis of electroperforated materials using the quadrat counts method
Energy Technology Data Exchange (ETDEWEB)
Miranda, E; Garzon, C; Garcia-Garcia, J [Departament d' Enginyeria Electronica, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona (Spain); MartInez-Cisneros, C; Alonso, J, E-mail: enrique.miranda@uab.cat [Departament de Quimica AnalItica, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona (Spain)
2011-06-23
The electroperforation distribution in thin porous materials is investigated using the quadrat counts method (QCM), a classical statistical technique aimed to evaluate the deviation from complete spatial randomness (CSR). Perforations are created by means of electrical discharges generated by needle-like tungsten electrodes. The objective of perforating a thin porous material is to enhance its air permeability, a critical issue in many industrial applications involving paper, plastics, textiles, etc. Using image analysis techniques and specialized statistical software it is shown that the perforation locations follow, beyond a certain length scale, a homogeneous 2D Poisson distribution.
Statistical geological discrete fracture network model. Forsmark modelling stage 2.2
Energy Technology Data Exchange (ETDEWEB)
Fox, Aaron; La Pointe, Paul [Golder Associates Inc (United States); Simeonov, Assen [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden); Hermanson, Jan; Oehman, Johan [Golder Associates AB, Stockholm (Sweden)
2007-11-15
The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions
Statistical geological discrete fracture network model. Forsmark modelling stage 2.2
International Nuclear Information System (INIS)
Fox, Aaron; La Pointe, Paul; Simeonov, Assen; Hermanson, Jan; Oehman, Johan
2007-11-01
The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions
Koop, G.; Dik, N.; Nielen, M.; Lipman, L.J.A.
2010-01-01
The aims of this study were to assess how different bacterial groups in bulk milk are related to bulk milk somatic cell count (SCC), bulk milk total bacterial count (TBC), and bulk milk standard plate count (SPC) and to measure the repeatability of bulk milk culturing. On 53 Dutch dairy goat farms, 3 bulk milk samples were collected at intervals of 2 wk. The samples were cultured for SPC, coliform count, and staphylococcal count and for the presence of Staphylococcus aureus. Furthermore, SCC ...
Statistical Modelling of the Soil Dielectric Constant
Usowicz, Boguslaw; Marczewski, Wojciech; Bogdan Usowicz, Jerzy; Lipiec, Jerzy
2010-05-01
The dielectric constant of soil is the physical property being very sensitive on water content. It funds several electrical measurement techniques for determining the water content by means of direct (TDR, FDR, and others related to effects of electrical conductance and/or capacitance) and indirect RS (Remote Sensing) methods. The work is devoted to a particular statistical manner of modelling the dielectric constant as the property accounting a wide range of specific soil composition, porosity, and mass density, within the unsaturated water content. Usually, similar models are determined for few particular soil types, and changing the soil type one needs switching the model on another type or to adjust it by parametrization of soil compounds. Therefore, it is difficult comparing and referring results between models. The presented model was developed for a generic representation of soil being a hypothetical mixture of spheres, each representing a soil fraction, in its proper phase state. The model generates a serial-parallel mesh of conductive and capacitive paths, which is analysed for a total conductive or capacitive property. The model was firstly developed to determine the thermal conductivity property, and now it is extended on the dielectric constant by analysing the capacitive mesh. The analysis is provided by statistical means obeying physical laws related to the serial-parallel branching of the representative electrical mesh. Physical relevance of the analysis is established electrically, but the definition of the electrical mesh is controlled statistically by parametrization of compound fractions, by determining the number of representative spheres per unitary volume per fraction, and by determining the number of fractions. That way the model is capable covering properties of nearly all possible soil types, all phase states within recognition of the Lorenz and Knudsen conditions. In effect the model allows on generating a hypothetical representative of
Qi, D.; Majda, A.
2017-12-01
A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with
Bayesian models based on test statistics for multiple hypothesis testing problems.
Ji, Yuan; Lu, Yiling; Mills, Gordon B
2008-04-01
We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.
On-the-fly confluence detection for statistical model checking (extended version)
Hartmanns, Arnd; Timmer, Mark
Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the
Bias expansion of spatial statistics and approximation of differenced ...
Indian Academy of Sciences (India)
Investigations of spatial statistics, computed from lattice data in the plane, can lead to a special lattice point counting problem. The statistical goal is to expand the asymptotic expectation or large-sample bias of certain spatial covariance estimators, where this bias typically depends on the shape of a spatial sampling region.
International Nuclear Information System (INIS)
Grau Carles, A.; Grau Malonda, A.; Rodriguez Barquero, L.
1993-01-01
The CIEMAT/NIST tracer method has successfully standardized nuclides with diverse quench values and decay schemes in liquid scintillation counting. However, the counting efficiency is computed inaccurately for extremely quenched samples. This article shows that when samples are extremely quenched, the counting efficiency in high-energy beta-ray nuclides depends principally on the Cherenkov effect. A new technique is described for quench determination, which makes the measurement of counting efficiency possible when scintillation counting approaches zero. A new efficiency computation model for pure beta-ray nuclides is also described. The results of the model are tested experimentally for 89 Sr, 90 Y, 36 Cl and 204 Tl nuclides with independence of the quench level. (orig.)
Investigation of reduction in background counts of clover detector
International Nuclear Information System (INIS)
Kshetri, Ritesh
2015-01-01
The peak-to-total ratio can be improved by increasing the full energy peak (FEP) counts and/or by decreasing the background counts. It is notable that FEP counts will be effected by mode of operation, while background counts will be effected by both modes of operation and suppression cases. It would be interesting to know if the reduction in background is caused more by active suppression or by add back process. We introduce a simple formalism to investigate the reduction of background counts for different cases-single crystal or add back mode with active or passive suppression. A more sophisticated formalism for modeling a general composite detector had been presented in a series of six recent papers by the author
Is total lymphocyte count related to nutritional markers in hospitalized older adults?
Directory of Open Access Journals (Sweden)
Vânia Aparecida LEANDRO-MERHI
Full Text Available ABSTRACT BACKGROUND Older patients are commonly malnourished during hospital stay, and a high prevalence of malnutrition is found in hospitalized patients aged more than 65 years. OBJECTIVE To investigate whether total lymphocyte count is related to other nutritional markers in hospitalized older adults. METHODS Hospitalized older adults (N=131 were recruited for a cross-sectional study. Their nutritional status was assessed by the Nutritional Risk Screening (NRS, anthropometry, and total lymphocyte count. The statistical analyses included the chi-square test, Fisher's exact test, and Mann-Whitney test. Spearman's linear correlation coefficient determined whether total lymphocyte count was correlated with the nutritional markers. Multiple linear regression determined the parameters associated with lymphocyte count. The significance level was set at 5%. RESULTS According to the NRS, 41.2% of the patients were at nutritional risk, and 36% had mild or moderate depletion according to total lymphocyte count. Total lymphocyte count was weakly correlated with mid-upper arm circumference (r=0.20507; triceps skinfold thickness (r=0.29036, and length of hospital stay (r= -0.21518. Total lymphocyte count in different NRS categories differed significantly: older adults who were not at nutritional risk had higher mean and median total lymphocyte count ( P =0.0245. Multiple regression analysis showed that higher lymphocyte counts were associated with higher triceps skinfold thicknesses and no nutritional risk according to the NRS. CONCLUSION Total lymphocyte count was correlated with mid-upper arm circumference, triceps skinfold thickness, and nutritional risk according to the NRS. In multiple regression the combined factors that remained associated with lymphocyte count were NRS and triceps skinfold thickness. Therefore, total lymphocyte count may be considered a nutritional marker. Other studies should confirm these findings.
Topology for Statistical Modeling of Petascale Data
Energy Technology Data Exchange (ETDEWEB)
Bennett, Janine Camille [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pebay, Philippe Pierre [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Rojas, Maurice [Texas A & M Univ., College Station, TX (United States)
2014-07-01
This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled "Topology for Statistical Modeling of Petascale Data", funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program.
Correction of the counting up number by dead time in detector systems for radiograph images
International Nuclear Information System (INIS)
Cerdeira E, A.; Cicuttin, A.; Cerdeira, A.; Estrada, M.; Luca, A. de
2002-01-01
The effect of the dead time in a detection system by counting up of particles and the contribution of this error in the final image resolution is analysed. It is given a statistical criteria for the optimization of electronic parameters such as dead time and counting up memory which help in the implementation of these systems with the minimum necessary characteristics which satisfy the resolution requirements. (Author)
Data analysis in emission tomography using emission-count posteriors
International Nuclear Information System (INIS)
Sitek, Arkadiusz
2012-01-01
A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography. (paper)
Data analysis in emission tomography using emission-count posteriors
Sitek, Arkadiusz
2012-11-01
A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.
Directory of Open Access Journals (Sweden)
Anke Hüls
2017-05-01
Full Text Available Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model and (ii to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate
Analyzing sickness absence with statistical models for survival data
DEFF Research Database (Denmark)
Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars
2007-01-01
OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation...
Physics-based statistical model and simulation method of RF propagation in urban environments
Pao, Hsueh-Yuan; Dvorak, Steven L.
2010-09-14
A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.
Absolute nuclear material assay using count distribution (LAMBDA) space
Prasad, Manoj K [Pleasanton, CA; Snyderman, Neal J [Berkeley, CA; Rowland, Mark S [Alamo, CA
2012-06-05
A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.
Mueller, Sherry A; Anderson, James E; Kim, Byung R; Ball, James C
2009-04-01
Effective bacterial control in cooling-tower systems requires accurate and timely methods to count bacteria. Plate-count methods are difficult to implement on-site, because they are time- and labor-intensive and require sterile techniques. Several field-applicable methods (dipslides, Petrifilm, and adenosine triphosphate [ATP] bioluminescence) were compared with the plate count for two sample matrices--phosphate-buffered saline solution containing a pure culture of Pseudomonas fluorescens and cooling-tower water containing an undefined mixed bacterial culture. For the pure culture, (1) counts determined on nutrient agar and plate-count agar (PCA) media and expressed as colony-forming units (CFU) per milliliter were equivalent to those on R2A medium (p = 1.0 and p = 1.0, respectively); (2) Petrifilm counts were not significantly different from R2A plate counts (p = 0.99); (3) the dipslide counts were up to 2 log units higher than R2A plate counts, but this discrepancy was not statistically significant (p = 0.06); and (4) a discernable correlation (r2 = 0.67) existed between ATP readings and plate counts. For cooling-tower water samples (n = 62), (1) bacterial counts using R2A medium were higher (but not significant; p = 0.63) than nutrient agar and significantly higher than tryptone-glucose yeast extract (TGE; p = 0.03) and PCA (p < 0.001); (2) Petrifilm counts were significantly lower than nutrient agar or R2A (p = 0.02 and p < 0.001, respectively), but not statistically different from TGE, PCA, and dipslides (p = 0.55, p = 0.69, and p = 0.91, respectively); (3) the dipslide method yielded bacteria counts 1 to 3 log units lower than nutrient agar and R2A (p < 0.001), but was not significantly different from Petrifilm (p = 0.91), PCA (p = 1.00) or TGE (p = 0.07); (4) the differences between dipslides and the other methods became greater with a 6-day incubation time; and (5) the correlation between ATP readings and plate counts varied from system to system, was poor
Encoding Dissimilarity Data for Statistical Model Building.
Wahba, Grace
2010-12-01
We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.
Analysis of General Power Counting Rules in Effective Field Theory
Gavela, B M; Manohar, A V; Merlo, L
2016-01-01
We derive the general counting rules for a quantum effective field theory (EFT) in $\\mathsf{d}$ dimensions. The rules are valid for strongly and weakly coupled theories, and predict that all kinetic energy terms are canonically normalized. They determine the energy dependence of scattering cross sections in the range of validity of the EFT expansion. The size of cross sections is controlled by the $\\Lambda$ power counting of EFT, not by chiral counting, even for chiral perturbation theory ($\\chi$PT). The relation between $\\Lambda$ and $f$ is generalized to $\\mathsf{d}$ dimensions. We show that the naive dimensional analysis $4\\pi$ counting is related to $\\hbar$ counting. The EFT counting rules are applied to $\\chi$PT, to Standard Model EFT and to the non-trivial case of Higgs EFT, which combines the $\\Lambda$ and chiral counting rules within a single theory.
Simple classical model for Fano statistics in radiation detectors
Energy Technology Data Exchange (ETDEWEB)
Jordan, David V. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)], E-mail: David.Jordan@pnl.gov; Renholds, Andrea S.; Jaffe, John E.; Anderson, Kevin K.; Rene Corrales, L.; Peurrung, Anthony J. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)
2008-02-01
A simple classical model that captures the essential statistics of energy partitioning processes involved in the creation of information carriers (ICs) in radiation detectors is presented. The model pictures IC formation from a fixed amount of deposited energy in terms of the statistically analogous process of successively sampling water from a large, finite-volume container ('bathtub') with a small dipping implement ('shot or whiskey glass'). The model exhibits sub-Poisson variance in the distribution of the number of ICs generated (the 'Fano effect'). Elementary statistical analysis of the model clarifies the role of energy conservation in producing the Fano effect and yields Fano's prescription for computing the relative variance of the IC number distribution in terms of the mean and variance of the underlying, single-IC energy distribution. The partitioning model is applied to the development of the impact ionization cascade in semiconductor radiation detectors. It is shown that, in tandem with simple assumptions regarding the distribution of energies required to create an (electron, hole) pair, the model yields an energy-independent Fano factor of 0.083, in accord with the lower end of the range of literature values reported for silicon and high-purity germanium. The utility of this simple picture as a diagnostic tool for guiding or constraining more detailed, 'microscopic' physical models of detector material response to ionizing radiation is discussed.
Risk prediction model: Statistical and artificial neural network approach
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
2017-04-01
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
Statistical learning modeling method for space debris photometric measurement
Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen
2016-03-01
Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.
Learning word vector representations based on acoustic counts
Ribeiro, Sam; Watts, Oliver; Yamagishi, Junichi
2017-01-01
This paper presents a simple count-based approach to learning word vector representations by leveraging statistics of cooccurrences between text and speech. This type of representation requires two discrete sequences of units defined across modalities. Two possible methods for the discretization of an acoustic signal are presented, which are then applied to fundamental frequency and energy contours of a transcribed corpus of speech, yielding a sequence of textual objects (e.g. words, syllable...
GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science
Caron, L.; Ivins, E. R.; Larour, E.; Adhikari, S.; Nilsson, J.; Blewitt, G.
2018-03-01
We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (≤2 mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7 year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14 years of GRACE data in polar regions.
Simple counting technique for measuring mixtures of two pure beta-emitting radionuclides
CSIR Research Space (South Africa)
Van Wyngaardt, WM
2006-08-01
Full Text Available on counting statistics. The method is demonstrated experimentally for various mixture combinations of C-14 and Ni-63. It is shown that the accuracy of the technique can be enhanced by improving the determination of the figure-of-merit....
Experimental, statistical, and biological models of radon carcinogenesis
International Nuclear Information System (INIS)
Cross, F.T.
1991-09-01
Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared to domestic environments and from uncertainties about the interaction between cigarette-smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research program that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models), and the relationship of radon to smoking and other copollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. 20 refs., 1 fig
An Odyssey of Connecticut's Children: KIDS COUNT Data Book 2001.
Sampson, Amy E.
This Kids Count Data Book provides state and regional trends in the well-being of Connecticut's children. The statistical portrait is based on 19 indicators of well-being: (1) children in families receiving welfare; (2) children receiving free or reduced-price meals; (3) high school employment; (4) births to teen mothers; (5) low birth weight; (6)…
KANSAS KIDS COUNT Data Book, 2000: Kansas Children at Risk.
Kansas Action for Children, Inc., Topeka.
This KIDS COUNT Data Book provides state and county data on the well-being of Kansas' children. The statistical portrait is based on 22 indicators of well-being: (1) births to single teens; (2) children living in poverty; (3) children receiving free school meals; (4) children in families receiving economic assistance; (5) childhood deaths; (6)…
Data and statistical methods for analysis of trends and patterns
International Nuclear Information System (INIS)
Atwood, C.L.; Gentillon, C.D.; Wilson, G.E.
1992-11-01
This report summarizes topics considered at a working meeting on data and statistical methods for analysis of trends and patterns in US commercial nuclear power plants. This meeting was sponsored by the Office of Analysis and Evaluation of Operational Data (AEOD) of the Nuclear Regulatory Commission (NRC). Three data sets are briefly described: Nuclear Plant Reliability Data System (NPRDS), Licensee Event Report (LER) data, and Performance Indicator data. Two types of study are emphasized: screening studies, to see if any trends or patterns appear to be present; and detailed studies, which are more concerned with checking the analysis assumptions, modeling any patterns that are present, and searching for causes. A prescription is given for a screening study, and ideas are suggested for a detailed study, when the data take of any of three forms: counts of events per time, counts of events per demand, and non-event data
CD4 cell count recovery in HIV/TB co-infected patients versus TB uninfected HIV patients
Directory of Open Access Journals (Sweden)
Wanchu A
2010-10-01
Full Text Available Background: There is lack of data comparing the improvement in CD4 count following antitubercular (ATT and antiretroviral therapy (ART in patients presenting with Human Immunodeficiency Virus/Tuberculosis (HIV/TB dual infection compared with CD4 matched cohort of TB uninfected HIV patients initiated on ART. We sought to test the hypothesis; TB additionally contributes to reduction in CD4 count in HIV/TB co-infected patients and this would result in greater improvement in count following treatment compared with CD4 matched TB uninfected individuals. Materials and Methods: In a retrospective cohort study design we studied the change in CD4 cell counts in two groups of patients - those with CD4 cell count >100 cells / mm 3 (Group 1 and <100/mm 3 (Group 2 at presentation. In each group the change in CD4 cell count in dually infected patients following six-month ATT and ART was compared to cohorts of CD4 matched TB uninfected patients initiated on ART. Results: In Group 1 (52 patients dually infected subjects′ CD4 count improved from 150 cells/ mm 3 to 345 cells/mm 3 (P=0.001. In the control TB uninfected patients, the change was from 159 cells/mm 3 to 317 cells/mm 3 (P=0.001. Additional improvement in dually infected patients compared to the control group was not statistically significant (P=0.24. In Group 2 (65 patients dually infected subjects count improved from 49 cells/mm3 to 249 cells/mm 3 (P=0.001 where as in control TB uninfected patients improvement was from 50 cells/ mm 3 to 205 cells/mm 3 (P=0.001, there being statistically significant additional improvement in dually infected subjects (P=0.01. Conclusion: Greater increment in CD4 counts with ATT and ART in dually infected patients suggests that TB additionally influences the reduction of CD4 counts in HIV patients.
International Nuclear Information System (INIS)
Balpardo, C.; Capoulat, M.E.; Rodrigues, D.; Arenillas, P.
2010-01-01
The nuclide 241 Am decays by alpha emission to 237 Np. Most of the decays (84.6%) populate the excited level of 237 Np with energy of 59.54 keV. Digital coincidence counting was applied to standardize a solution of 241 Am by alpha-gamma coincidence counting with efficiency extrapolation. Electronic discrimination was implemented with a pressurized proportional counter and the results were compared with two other independent techniques: Liquid scintillation counting using the logical sum of double coincidences in a TDCR array and defined solid angle counting taking into account activity inhomogeneity in the active deposit. The results show consistency between the three methods within a limit of a 0.3%. An ampoule of this solution will be sent to the International Reference System (SIR) during 2009. Uncertainties were analysed and compared in detail for the three applied methods.
Positron imaging system with improved count rate and tomographic capability
International Nuclear Information System (INIS)
Muehllehner, G.; Buchin, M.P.
1980-01-01
Improvements to a positron camera imaging system are described. A pair of Angear-type scintillation cameras serve as the detectors, each camera being positioned on opposite sides of the organ of interest. Pulse shaping circuits reduce the pulse duration below 900 nanoseconds and the integration time below 500 noneseconds, improving the count rate capability and the counting statistics of the system and thus the image quality and processing speed. The invention also provides means for rotating the opposed camera heads about an axis which passes through the organ of interest. The cameras do not use collimators, and are capable of accepting radiation travelling in planes not perpendicular to the scintillation crystals. (LL)
A statistical model for instable thermodynamical systems
International Nuclear Information System (INIS)
Sommer, Jens-Uwe
2003-01-01
A generic model is presented for statistical systems which display thermodynamic features in contrast to our everyday experience, such as infinite and negative heat capacities. Such system are instable in terms of classical equilibrium thermodynamics. Using our statistical model, we are able to investigate states of instable systems which are undefined in the framework of equilibrium thermodynamics. We show that a region of negative heat capacity in the adiabatic environment, leads to a first order like phase transition when the system is coupled to a heat reservoir. This phase transition takes place without a phase coexistence. Nevertheless, all intermediate states are stable due to fluctuations. When two instable system are brought in thermal contact, the temperature of the composed system is lower than the minimum temperature of the individual systems. Generally, the equilibrium states of instable system cannot be simply decomposed into equilibrium states of the individual systems. The properties of instable system depend on the environment, ensemble equivalence is broken
Benjamin Thompson, Count Rumford Count Rumford on the nature of heat
Brown, Sanborn C
1967-01-01
Men of Physics: Benjamin Thompson - Count Rumford: Count Rumford on the Nature of Heat covers the significant contributions of Count Rumford in the fields of physics. Count Rumford was born with the name Benjamin Thompson on March 23, 1753, in Woburn, Massachusetts. This book is composed of two parts encompassing 11 chapters, and begins with a presentation of Benjamin Thompson's biography and his interest in physics, particularly as an advocate of an """"anti-caloric"""" theory of heat. The subsequent chapters are devoted to his many discoveries that profoundly affected the physical thought
Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas
Zhang, Yue; Fu, Kun; Sun, Xian; Xu, Guangluan; Wang, Hongqi
2016-06-01
The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.
MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS
Directory of Open Access Journals (Sweden)
Y. Zhang
2016-06-01
Full Text Available The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.
International Nuclear Information System (INIS)
Koyumdjieva, N.
2006-01-01
A statistical model for the resonant cross section structure in the Unresolved Resonance Region has been developed in the framework of the R-matrix formalism in Reich Moore approach with effective accounting of the resonance parameters fluctuations. The model uses only the average resonance parameters and can be effectively applied for analyses of cross sections functional, averaged over many resonances. Those are cross section moments, transmission and self-indication functions measured through thick sample. In this statistical model the resonant cross sections structure is accepted to be periodic and the R-matrix is a function of ε=E/D with period 0≤ε≤N; R nc (ε)=π/2√(S n *S c )1/NΣ(i=1,N)(β in *β ic *ctg[π(ε i - = ε-iS i )/N]; Here S n ,S c ,S i is respectively neutron strength function, strength function for fission or inelastic channel and strength function for radiative capture, N is the number of resonances (ε i ,β i ) that obey the statistic of Porter-Thomas and Wigner's one. The simple case of this statistical model concerns the resonant cross section structure for non-fissile nuclei under the threshold for inelastic scattering - the model of the characteristic function with HARFOR program. In the above model some improvements of calculation of the phases and logarithmic derivatives of neutron channels have been done. In the parameterization we use the free parameter R l ∞ , which accounts the influence of long-distant resonances. The above scheme for statistical modelling of the resonant cross section structure has been applied for evaluation of experimental data for total, capture and inelastic cross sections for 232 Th in the URR (4-150) keV and also the transmission and self-indication functions in (4-175) keV. The set of evaluated average resonance parameters have been obtained. The evaluated average resonance parameters in the URR are consistent with those in the Resolved Resonance Region (CRP for Th-U cycle, Vienna, 2006
Preverbal and verbal counting and computation.
Gallistel, C R; Gelman, R
1992-08-01
We describe the preverbal system of counting and arithmetic reasoning revealed by experiments on numerical representations in animals. In this system, numerosities are represented by magnitudes, which are rapidly but inaccurately generated by the Meck and Church (1983) preverbal counting mechanism. We suggest the following. (1) The preverbal counting mechanism is the source of the implicit principles that guide the acquisition of verbal counting. (2) The preverbal system of arithmetic computation provides the framework for the assimilation of the verbal system. (3) Learning to count involves, in part, learning a mapping from the preverbal numerical magnitudes to the verbal and written number symbols and the inverse mappings from these symbols to the preverbal magnitudes. (4) Subitizing is the use of the preverbal counting process and the mapping from the resulting magnitudes to number words in order to generate rapidly the number words for small numerosities. (5) The retrieval of the number facts, which plays a central role in verbal computation, is mediated via the inverse mappings from verbal and written numbers to the preverbal magnitudes and the use of these magnitudes to find the appropriate cells in tabular arrangements of the answers. (6) This model of the fact retrieval process accounts for the salient features of the reaction time differences and error patterns revealed by experiments on mental arithmetic. (7) The application of verbal and written computational algorithms goes on in parallel with, and is to some extent guided by, preverbal computations, both in the child and in the adult.
Statistical models for expert judgement and wear prediction
International Nuclear Information System (INIS)
Pulkkinen, U.
1994-01-01
This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)
Security of statistical data bases: invasion of privacy through attribute correlational modeling
Energy Technology Data Exchange (ETDEWEB)
Palley, M.A.
1985-01-01
This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queries of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.
A no extensive statistical model for the nucleon structure function
International Nuclear Information System (INIS)
Trevisan, Luis A.; Mirez, Carlos
2013-01-01
We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon.
PVeStA: A Parallel Statistical Model Checking and Quantitative Analysis Tool
AlTurki, Musab
2011-01-01
Statistical model checking is an attractive formal analysis method for probabilistic systems such as, for example, cyber-physical systems which are often probabilistic in nature. This paper is about drastically increasing the scalability of statistical model checking, and making such scalability of analysis available to tools like Maude, where probabilistic systems can be specified at a high level as probabilistic rewrite theories. It presents PVeStA, an extension and parallelization of the VeStA statistical model checking tool [10]. PVeStA supports statistical model checking of probabilistic real-time systems specified as either: (i) discrete or continuous Markov Chains; or (ii) probabilistic rewrite theories in Maude. Furthermore, the properties that it can model check can be expressed in either: (i) PCTL/CSL, or (ii) the QuaTEx quantitative temporal logic. As our experiments show, the performance gains obtained from parallelization can be very high. © 2011 Springer-Verlag.
Factor V Leiden is associated with increased sperm count
van Mens, T. E.; Joensen, U. N.; Bochdanovits, Z.; Takizawa, A.; Peter, J.; Jørgensen, N.; Szecsi, P. B.; Meijers, J. C. M.; Weiler, H.; Rajpert-de Meyts, E.; Repping, S.; Middeldorp, S.
2017-01-01
STUDY QUESTION: Is the thrombophilia mutation factor V Leiden (FVL) associated with an increased total sperm count? SUMMARY ANSWER: Carriers of FVL have a higher total sperm count than non-FVL-carriers, which could not be explained by genetic linkage or by observations in a FVL-mouse model. WHAT IS
Lundbye-Christensen, S; Dethlefsen, C; Gorst-Rasmussen, A; Fischer, T; Schønheyder, H C; Rothman, K J; Sørensen, H T
2009-01-01
Time series of incidence counts often show secular trends and seasonal patterns. We present a model for incidence counts capable of handling a possible gradual change in growth rates and seasonal patterns, serial correlation, and overdispersion. The model resembles an ordinary time series regression model for Poisson counts. It differs in allowing the regression coefficients to vary gradually over time in a random fashion. During the 1983-1999 period, 17,989 incidents of acute myocardial infarction were recorded in the Hospital Discharge Registry for the county of North Jutland, Denmark. Records were updated daily. A dynamic model with a seasonal pattern and an approximately linear trend was fitted to the data, and diagnostic plots indicated a good model fit. The analysis conducted with the dynamic model revealed peaks coinciding with above-average influenza A activity. On average the dynamic model estimated a higher peak-to-trough ratio than traditional models, and showed gradual changes in seasonal patterns. Analyses conducted with this model provide insights not available from more traditional approaches.
Sudhir, R; Praveen, P; Anantharaj, A; Venkataraghavan, Karthik
2012-07-01
Antimicrobial methods of controlling dental caries that include probiotic agents can play a valuable role in establishing caries control in children at moderate to high risk for developing dental caries. Several studies have demonstrated the beneficial effects of use of various Probiotic products including curd. The objective of this study was to compare the effect of short-term consumption of probiotic curd containing Lactobacillus acidophilus and normal curd on salivary Streptococcus Mutans counts, as well as salivary pH. Forty, caries-free, 10-12 years old children were selected and randomly allocated to two groups. Test Group consisted of 20 children who consumed 200ml of probiotic curd daily for 30 days. Control Group consisted of 20 children who were given 200ml of regular curd for 30 days. Salivary pH and salivary Streptococcus Mutans counts were recorded at baseline and after 30 days and statistically compared using the Student's t-test. Consumption of probiotic curd resulted in a statistically significant reduction in S. Mutans colony counts (PpH (P>0.05) in both the groups. Short-term consumption of probiotic curds can reduce oral S. Mutans counts. However, this caused a slight reduction in salivary pH.
Inclusion of temperature dependence of fission barriers in statistical model calculations
International Nuclear Information System (INIS)
Newton, J.O.; Popescu, D.G.; Leigh, J.R.
1990-08-01
The temperature dependence of fission barriers has been interpolated from the results of recent theoretical calculations and included in the statistical model code PACE2. It is shown that the inclusion of temperature dependence causes significant changes to the values of the statistical model parameters deduced from fits to experimental data. 21 refs., 2 figs
Martin, Justin D.
2017-01-01
This essay presents data from a census of statistics requirements and offerings at all 4-year journalism programs in the United States (N = 369) and proposes a model of a potential course in statistics for journalism majors. The author proposes that three philosophies underlie a statistics course for journalism students. Such a course should (a)…
Denwood, M J; Love, S; Innocent, G T; Matthews, L; McKendrick, I J; Hillary, N; Smith, A; Reid, S W J
2012-08-13
The faecal egg count (FEC) is the most widely used means of quantifying the nematode burden of horses, and is frequently used in clinical practice to inform treatment and prevention. The statistical process underlying the FEC is complex, comprising a Poisson counting error process for each sample, compounded with an underlying continuous distribution of means between samples. Being able to quantify the sources of variability contributing to this distribution of means is a necessary step towards providing estimates of statistical power for future FEC and FECRT studies, and may help to improve the usefulness of the FEC technique by identifying and minimising unwanted sources of variability. Obtaining such estimates require a hierarchical statistical model coupled with repeated FEC observations from a single animal over a short period of time. Here, we use this approach to provide the first comparative estimate of multiple sources of within-horse FEC variability. The results demonstrate that a substantial proportion of the observed variation in FEC between horses occurs as a result of variation in FEC within an animal, with the major sources being aggregation of eggs within faeces and variation in egg concentration between faecal piles. The McMaster procedure itself is associated with a comparatively small coefficient of variation, and is therefore highly repeatable when a sufficiently large number of eggs are observed to reduce the error associated with the counting process. We conclude that the variation between samples taken from the same animal is substantial, but can be reduced through the use of larger homogenised faecal samples. Estimates are provided for the coefficient of variation (cv) associated with each within animal source of variability in observed FEC, allowing the usefulness of individual FEC to be quantified, and providing a basis for future FEC and FECRT studies. Copyright © 2012 Elsevier B.V. All rights reserved.
DEFF Research Database (Denmark)
Bregnballe, Thomas; Carss, David N; Lorentsen, Svein-Håkon
2013-01-01
This chapter focuses on Cormorant population counts for both summer (i.e. breeding) and winter (i.e. migration, winter roosts) seasons. It also explains differences in the data collected from undertaking ‘day’ versus ‘roost’ counts, gives some definitions of the term ‘numbers’, and presents two...
Mohebbi, Mohammadreza; Wolfe, Rory; Forbes, Andrew
2014-01-01
This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference. PMID:24413702
Structural reliability in context of statistical uncertainties and modelling discrepancies
International Nuclear Information System (INIS)
Pendola, Maurice
2000-01-01
Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr
Nishino, Ko; Lombardi, Stephen
2011-01-01
We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.
Analysis of statistical misconception in terms of statistical reasoning
Maryati, I.; Priatna, N.
2018-05-01
Reasoning skill is needed for everyone to face globalization era, because every person have to be able to manage and use information from all over the world which can be obtained easily. Statistical reasoning skill is the ability to collect, group, process, interpret, and draw conclusion of information. Developing this skill can be done through various levels of education. However, the skill is low because many people assume that statistics is just the ability to count and using formulas and so do students. Students still have negative attitude toward course which is related to research. The purpose of this research is analyzing students’ misconception in descriptive statistic course toward the statistical reasoning skill. The observation was done by analyzing the misconception test result and statistical reasoning skill test; observing the students’ misconception effect toward statistical reasoning skill. The sample of this research was 32 students of math education department who had taken descriptive statistic course. The mean value of misconception test was 49,7 and standard deviation was 10,6 whereas the mean value of statistical reasoning skill test was 51,8 and standard deviation was 8,5. If the minimal value is 65 to state the standard achievement of a course competence, students’ mean value is lower than the standard competence. The result of students’ misconception study emphasized on which sub discussion that should be considered. Based on the assessment result, it was found that students’ misconception happen on this: 1) writing mathematical sentence and symbol well, 2) understanding basic definitions, 3) determining concept that will be used in solving problem. In statistical reasoning skill, the assessment was done to measure reasoning from: 1) data, 2) representation, 3) statistic format, 4) probability, 5) sample, and 6) association.
International Nuclear Information System (INIS)
De Backer, A; De Wael, A; Gonnissen, J; Martinez, G T; Béché, A; Van Aert, S; MacArthur, K E; Jones, L; Nellist, P D
2015-01-01
Quantitative atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) has become a powerful technique for nanoparticle atom-counting. However, a lot of nanoparticles provide a severe characterisation challenge because of their limited size and beam sensitivity. Therefore, quantitative ADF STEM may greatly benefit from statistical detection theory in order to optimise the instrumental microscope settings such that the incoming electron dose can be kept as low as possible whilst still retaining single-atom precision. The principles of detection theory are used to quantify the probability of error for atom-counting. This enables us to decide between different image performance measures and to optimise the experimental detector settings for atom-counting in ADF STEM in an objective manner. To demonstrate this, ADF STEM imaging of an industrial catalyst has been conducted using the near-optimal detector settings. For this experiment, we discussed the limits for atomcounting diagnosed by combining a thorough statistical method and detailed image simulations. (paper)
Pedestrian count estimation using texture feature with spatial distribution
Directory of Open Access Journals (Sweden)
Hongyu Hu
2016-12-01
Full Text Available We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.
Koop, G; Dik, N; Nielen, M; Lipman, L J A
2010-06-01
The aims of this study were to assess how different bacterial groups in bulk milk are related to bulk milk somatic cell count (SCC), bulk milk total bacterial count (TBC), and bulk milk standard plate count (SPC) and to measure the repeatability of bulk milk culturing. On 53 Dutch dairy goat farms, 3 bulk milk samples were collected at intervals of 2 wk. The samples were cultured for SPC, coliform count, and staphylococcal count and for the presence of Staphylococcus aureus. Furthermore, SCC (Fossomatic 5000, Foss, Hillerød, Denmark) and TBC (BactoScan FC 150, Foss) were measured. Staphylococcal count was correlated to SCC (r=0.40), TBC (r=0.51), and SPC (r=0.53). Coliform count was correlated to TBC (r=0.33), but not to any of the other variables. Staphylococcus aureus did not correlate to SCC. The contribution of the staphylococcal count to the SPC was 31%, whereas the coliform count comprised only 1% of the SPC. The agreement of the repeated measurements was low. This study indicates that staphylococci in goat bulk milk are related to SCC and make a significant contribution to SPC. Because of the high variation in bacterial counts, repeated sampling is necessary to draw valid conclusions from bulk milk culturing. 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Detecting overdispersion in count data: A zero-inflated Poisson regression analysis
Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah
2017-09-01
This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.
Statistical approach for selection of regression model during validation of bioanalytical method
Directory of Open Access Journals (Sweden)
Natalija Nakov
2014-06-01
Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.
Statistical modeling of geopressured geothermal reservoirs
Ansari, Esmail; Hughes, Richard; White, Christopher D.
2017-06-01
Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.
Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.
MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C
2018-03-29
This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Current statistics of correlated charge tunnelling through an impurity in a 1D wire
Energy Technology Data Exchange (ETDEWEB)
Herzog, Alexander; Weiss, Ulrich [Institut fuer Theoretische Physik, Universitaet Stuttgart, Pfaffenwaldring 57, D-70550 Stuttgart (Germany)], E-mail: herzog@theo2.physik.uni-stuttgart.de, E-mail: weiss@theo2.physik.uni-stuttgart.de
2008-04-15
We review recent advances in the full counting statistics of a generic transport model of a quantum Brownian particle. The model applies to charge transfer through an impurity embedded in a Luttinger liquid environment, to a coherent one-channel conductor in a resistive environment, and to a resistively shunted Josephson junction device. We derive analytic expressions for the cumulant generating function (CGF) at two particular values of the Luttinger parameter which are related by self-duality. We determine the self-duality relations between the CGFs and between the cumulants.
texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables
Directory of Open Access Journals (Sweden)
Philip Leifeld
2013-11-01
Full Text Available A recurrent task in applied statistics is the (mostly manual preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.
Bayesian statistic methods and theri application in probabilistic simulation models
Directory of Open Access Journals (Sweden)
Sergio Iannazzo
2007-03-01
Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.
Counting the peaks in the excitation function for precompound processes
International Nuclear Information System (INIS)
Bonetti, R.; Hussein, M.S.; Mello, P.A.
1983-01-01
The ''counting of maxima'' method of Brink and Stephen, conventionally used for the extraction of the correlation width of statistical (compound nucleus) reactions, is generalized to include precompound processes as well. It is found that this method supplies an important independent check of the results obtained from autocorrelation studies. An application is made to the reaction 25 Mg( 3 He, p)
Statistical modelling for recurrent events: an application to sports injuries.
Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F
2014-09-01
Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
A classical statistical model of heavy ion collisions
International Nuclear Information System (INIS)
Schmidt, R.; Teichert, J.
1980-01-01
The use of the computer code TRAJEC which represents the numerical realization of a classical statistical model for heavy ion collisions is described. The code calculates the results of a classical friction model as well as various multi-differential cross sections for heavy ion collisions. INPUT and OUTPUT information of the code are described. Two examples of data sets are given [ru