Is It True That "Blonds Have More Fun"?

ERIC Educational Resources Information Center

Bonsangue, Martin V.

1992-01-01

Describes the model for decision making used in inferential statistics and real-world applications that parallel the statistical model. Discusses two activities that ask students to write about a personal decision-making experience and create a mock trial in which the class makes the decision of guilt or innocence. (MDH)

A Statistical Decision Model for Periodical Selection for a Specialized Information Center

ERIC Educational Resources Information Center

Dym, Eleanor D.; Shirey, Donald L.

1973-01-01

An experiment is described which attempts to define a quantitative methodology for the identification and evaluation of all possibly relevant periodical titles containing toxicological-biological information. A statistical decision model was designed and employed, along with yes/no criteria questions, a training technique and a quality control…

Electrospining of polyaniline/poly(lactic acid) ultrathin fibers: process and statistical modeling using a non-gaussian approach

USDA-ARS?s Scientific Manuscript database

Cover: The electrospinning technique was employed to obtain conducting nanofibers based on polyaniline and poly(lactic acid). A statistical model was employed to describe how the process factors (solution concentration, applied voltage, and flow rate) govern the fiber dimensions. Nanofibers down to ...

Bayesian Posterior Odds Ratios: Statistical Tools for Collaborative Evaluations

ERIC Educational Resources Information Center

Hicks, Tyler; Rodríguez-Campos, Liliana; Choi, Jeong Hoon

2018-01-01

To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices…

Cure Models as a Useful Statistical Tool for Analyzing Survival

PubMed Central

Othus, Megan; Barlogie, Bart; LeBlanc, Michael L.; Crowley, John J.

2013-01-01

Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors. PMID:22675175

Communication Dynamics of Blog Networks

NASA Astrophysics Data System (ADS)

Goldberg, Mark; Kelley, Stephen; Magdon-Ismail, Malik; Mertsalov, Konstantin; Wallace, William (Al)

We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communication (blogger-to-blogger links) in such online communication networks is very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two fundamental questions are: (i) what models adequately describe such dynamic communication behavior; and (ii) how does one detect the phase transitions, i.e. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe models of the communication dynamics in large social networks based on the principle of locality of communication: a node's communication energy is spent mostly within its own "social area," the locality of the node.

A Simple Model for Estimating Total and Merchantable Tree Heights

Treesearch

Alan R. Ek; Earl T. Birdsall; Rebecca J. Spears

1984-01-01

A model is described for estimating total and merchantable tree heights for Lake States tree species. It is intended to be used for compiling forest survey data and in conjunction with growth models for developing projections of tree product yield. Model coefficients are given for 25 species along with fit statistics. Supporting data sets are also described.

A statistical model describing combined irreversible electroporation and electroporation-induced blood-brain barrier disruption.

PubMed

Sharabi, Shirley; Kos, Bor; Last, David; Guez, David; Daniels, Dianne; Harnof, Sagi; Mardor, Yael; Miklavcic, Damijan

2016-03-01

Electroporation-based therapies such as electrochemotherapy (ECT) and irreversible electroporation (IRE) are emerging as promising tools for treatment of tumors. When applied to the brain, electroporation can also induce transient blood-brain-barrier (BBB) disruption in volumes extending beyond IRE, thus enabling efficient drug penetration. The main objective of this study was to develop a statistical model predicting cell death and BBB disruption induced by electroporation. This model can be used for individual treatment planning. Cell death and BBB disruption models were developed based on the Peleg-Fermi model in combination with numerical models of the electric field. The model calculates the electric field thresholds for cell kill and BBB disruption and describes the dependence on the number of treatment pulses. The model was validated using in vivo experimental data consisting of rats brains MRIs post electroporation treatments. Linear regression analysis confirmed that the model described the IRE and BBB disruption volumes as a function of treatment pulses number (r(2) = 0.79; p < 0.008, r(2) = 0.91; p < 0.001). The results presented a strong plateau effect as the pulse number increased. The ratio between complete cell death and no cell death thresholds was relatively narrow (between 0.88-0.91) even for small numbers of pulses and depended weakly on the number of pulses. For BBB disruption, the ratio increased with the number of pulses. BBB disruption radii were on average 67% ± 11% larger than IRE volumes. The statistical model can be used to describe the dependence of treatment-effects on the number of pulses independent of the experimental setup.

Equilibrium statistical-thermal models in high-energy physics

NASA Astrophysics Data System (ADS)

Tawfik, Abdel Nasser

2014-05-01

We review some recent highlights from the applications of statistical-thermal models to different experimental measurements and lattice QCD thermodynamics that have been made during the last decade. We start with a short review of the historical milestones on the path of constructing statistical-thermal models for heavy-ion physics. We discovered that Heinz Koppe formulated in 1948, an almost complete recipe for the statistical-thermal models. In 1950, Enrico Fermi generalized this statistical approach, in which he started with a general cross-section formula and inserted into it, the simplifying assumptions about the matrix element of the interaction process that likely reflects many features of the high-energy reactions dominated by density in the phase space of final states. In 1964, Hagedorn systematically analyzed the high-energy phenomena using all tools of statistical physics and introduced the concept of limiting temperature based on the statistical bootstrap model. It turns to be quite often that many-particle systems can be studied with the help of statistical-thermal methods. The analysis of yield multiplicities in high-energy collisions gives an overwhelming evidence for the chemical equilibrium in the final state. The strange particles might be an exception, as they are suppressed at lower beam energies. However, their relative yields fulfill statistical equilibrium, as well. We review the equilibrium statistical-thermal models for particle production, fluctuations and collective flow in heavy-ion experiments. We also review their reproduction of the lattice QCD thermodynamics at vanishing and finite chemical potential. During the last decade, five conditions have been suggested to describe the universal behavior of the chemical freeze-out parameters. The higher order moments of multiplicity have been discussed. They offer deep insights about particle production and to critical fluctuations. Therefore, we use them to describe the freeze-out parameters and suggest the location of the QCD critical endpoint. Various extensions have been proposed in order to take into consideration the possible deviations of the ideal hadron gas. We highlight various types of interactions, dissipative properties and location-dependences (spatial rapidity). Furthermore, we review three models combining hadronic with partonic phases; quasi-particle model, linear sigma model with Polyakov potentials and compressible bag model.

75 FR 72611 - Assessments, Large Bank Pricing

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-24

... the worst risk ranking and are included in the statistical analysis. Appendix 1 to the NPR describes the statistical analysis in detail. \\12\\ The percentage approximated by factors is based on the statistical model for that particual year. Actual weights assigned to each scorecard measure are largely based...

Applying Regression Analysis to Problems in Institutional Research.

ERIC Educational Resources Information Center

Bohannon, Tom R.

1988-01-01

Regression analysis is one of the most frequently used statistical techniques in institutional research. Principles of least squares, model building, residual analysis, influence statistics, and multi-collinearity are described and illustrated. (Author/MSE)

Statistical reconstruction for cosmic ray muon tomography.

PubMed

Schultz, Larry J; Blanpied, Gary S; Borozdin, Konstantin N; Fraser, Andrew M; Hengartner, Nicolas W; Klimenko, Alexei V; Morris, Christopher L; Orum, Chris; Sossong, Michael J

2007-08-01

Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm2 per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work [1]-[3], we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algorithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.

A Model for Investigating Predictive Validity at Highly Selective Institutions.

ERIC Educational Resources Information Center

Gross, Alan L.; And Others

A statistical model for investigating predictive validity at highly selective institutions is described. When the selection ratio is small, one must typically deal with a data set containing relatively large amounts of missing data on both criterion and predictor variables. Standard statistical approaches are based on the strong assumption that…

Nonelastic nuclear reactions and accompanying gamma radiation

NASA Technical Reports Server (NTRS)

Snow, R.; Rosner, H. R.; George, M. C.; Hayes, J. D.

1971-01-01

Several aspects of nonelastic nuclear reactions which proceed through the formation of a compound nucleus are dealt with. The full statistical model and the partial statistical model are described and computer programs based on these models are presented along with operating instructions and input and output for sample problems. A theoretical development of the expression for the reaction cross section for the hybrid case which involves a combination of the continuum aspects of the full statistical model with the discrete level aspects of the partial statistical model is presented. Cross sections for level excitation and gamma production by neutron inelastic scattering from the nuclei Al-27, Fe-56, Si-28, and Pb-208 are calculated and compared with avaliable experimental data.

Towards random matrix model of breaking the time-reversal invariance of elastic waves in chaotic cavities by feedback

NASA Astrophysics Data System (ADS)

Antoniuk, Oleg; Sprik, Rudolf

2010-03-01

We developed a random matrix model to describe the statistics of resonances in an acoustic cavity with broken time-reversal invariance. Time-reversal invariance braking is achieved by connecting an amplified feedback loop between two transducers on the surface of the cavity. The model is based on approach [1] that describes time- reversal properties of the cavity without a feedback loop. Statistics of eigenvalues (nearest neighbor resonance spacing distributions and spectral rigidity) has been calculated and compared to the statistics obtained from our experimental data. Experiments have been performed on aluminum block of chaotic shape confining ultrasound waves. [1] Carsten Draeger and Mathias Fink, One-channel time- reversal in chaotic cavities: Theoretical limits, Journal of Acoustical Society of America, vol. 105, Nr. 2, pp. 611-617 (1999)

LES/PDF studies of joint statistics of mixture fraction and progress variable in piloted methane jet flames with inhomogeneous inlet flows

NASA Astrophysics Data System (ADS)

Zhang, Pei; Barlow, Robert; Masri, Assaad; Wang, Haifeng

2016-11-01

The mixture fraction and progress variable are often used as independent variables for describing turbulent premixed and non-premixed flames. There is a growing interest in using these two variables for describing partially premixed flames. The joint statistical distribution of the mixture fraction and progress variable is of great interest in developing models for partially premixed flames. In this work, we conduct predictive studies of the joint statistics of mixture fraction and progress variable in a series of piloted methane jet flames with inhomogeneous inlet flows. The employed models combine large eddy simulations with the Monte Carlo probability density function (PDF) method. The joint PDFs and marginal PDFs are examined in detail by comparing the model predictions and the measurements. Different presumed shapes of the joint PDFs are also evaluated.

RooStatsCms: A tool for analysis modelling, combination and statistical studies

NASA Astrophysics Data System (ADS)

Piparo, D.; Schott, G.; Quast, G.

2010-04-01

RooStatsCms is an object oriented statistical framework based on the RooFit technology. Its scope is to allow the modelling, statistical analysis and combination of multiple search channels for new phenomena in High Energy Physics. It provides a variety of methods described in literature implemented as classes, whose design is oriented to the execution of multiple CPU intensive jobs on batch systems or on the Grid.

Teaching Classical Statistical Mechanics: A Simulation Approach.

ERIC Educational Resources Information Center

Sauer, G.

1981-01-01

Describes a one-dimensional model for an ideal gas to study development of disordered motion in Newtonian mechanics. A Monte Carlo procedure for simulation of the statistical ensemble of an ideal gas with fixed total energy is developed. Compares both approaches for a pseudoexperimental foundation of statistical mechanics. (Author/JN)

A simple statistical model for geomagnetic reversals

NASA Technical Reports Server (NTRS)

Constable, Catherine

1990-01-01

The diversity of paleomagnetic records of geomagnetic reversals now available indicate that the field configuration during transitions cannot be adequately described by simple zonal or standing field models. A new model described here is based on statistical properties inferred from the present field and is capable of simulating field transitions like those observed. Some insight is obtained into what one can hope to learn from paleomagnetic records. In particular, it is crucial that the effects of smoothing in the remanence acquisition process be separated from true geomagnetic field behavior. This might enable us to determine the time constants associated with the dominant field configuration during a reversal.

A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pasqualini, Donatella

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 estimatedmore » stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.« less

Menzerath-Altmann Law: Statistical Mechanical Interpretation as Applied to a Linguistic Organization

NASA Astrophysics Data System (ADS)

Eroglu, Sertac

2014-10-01

The distribution behavior described by the empirical Menzerath-Altmann law is frequently encountered during the self-organization of linguistic and non-linguistic natural organizations at various structural levels. This study presents a statistical mechanical derivation of the law based on the analogy between the classical particles of a statistical mechanical organization and the distinct words of a textual organization. The derived model, a transformed (generalized) form of the Menzerath-Altmann model, was termed as the statistical mechanical Menzerath-Altmann model. The derived model allows interpreting the model parameters in terms of physical concepts. We also propose that many organizations presenting the Menzerath-Altmann law behavior, whether linguistic or not, can be methodically examined by the transformed distribution model through the properly defined structure-dependent parameter and the energy associated states.

a Statistical Theory of the Epilepsies.

NASA Astrophysics Data System (ADS)

Thomas, Kuryan

1988-12-01

A new physical and mathematical model for the epilepsies is proposed, based on the theory of bond percolation on finite lattices. Within this model, the onset of seizures in the brain is identified with the appearance of spanning clusters of neurons engaged in the spurious and uncontrollable electrical activity characteristic of seizures. It is proposed that the fraction of excitatory to inhibitory synapses can be identified with a bond probability, and that the bond probability is a randomly varying quantity displaying Gaussian statistics. The consequences of the proposed model to the treatment of the epilepsies is explored. The nature of the data on the epilepsies which can be acquired in a clinical setting is described. It is shown that such data can be analyzed to provide preliminary support for the bond percolation hypothesis, and to quantify the efficacy of anti-epileptic drugs in a treatment program. The results of a battery of statistical tests on seizure distributions are discussed. The physical theory of the electroencephalogram (EEG) is described, and extant models of the electrical activity measured by the EEG are discussed, with an emphasis on their physical behavior. A proposal is made to explain the difference between the power spectra of electrical activity measured with cranial probes and with the EEG. Statistical tests on the characteristic EEG manifestations of epileptic activity are conducted, and their results described. Computer simulations of a correlated bond percolating system are constructed. It is shown that the statistical properties of the results of such a simulation are strongly suggestive of the statistical properties of clinical data. The study finds no contradictions between the predictions of the bond percolation model and the observed properties of the available data. Suggestions are made for further research and for techniques based on the proposed model which may be used for tuning the effects of anti -epileptic drugs.

A Stochastic Model of Space-Time Variability of Mesoscale Rainfall: Statistics of Spatial Averages

NASA Technical Reports Server (NTRS)

Kundu, Prasun K.; Bell, Thomas L.

2003-01-01

A characteristic feature of rainfall statistics is that they depend on the space and time scales over which rain data are averaged. A previously developed spectral model of rain statistics that is designed to capture this property, predicts power law scaling behavior for the second moment statistics of area-averaged rain rate on the averaging length scale L as L right arrow 0. In the present work a more efficient method of estimating the model parameters is presented, and used to fit the model to the statistics of area-averaged rain rate derived from gridded radar precipitation data from TOGA COARE. Statistical properties of the data and the model predictions are compared over a wide range of averaging scales. An extension of the spectral model scaling relations to describe the dependence of the average fraction of grid boxes within an area containing nonzero rain (the "rainy area fraction") on the grid scale L is also explored.

Spatial Statistical Network Models for Stream and River Temperatures in the Chesapeake Bay Watershed

EPA Science Inventory

Numerous metrics have been proposed to describe stream/river thermal regimes, and researchers are still struggling with the need to describe thermal regimes in a parsimonious fashion. Regional temperature models are needed for characterizing and mapping current stream thermal re...

A statistical model describing combined irreversible electroporation and electroporation-induced blood-brain barrier disruption

PubMed Central

Sharabi, Shirley; Kos, Bor; Last, David; Guez, David; Daniels, Dianne; Harnof, Sagi; Miklavcic, Damijan

2016-01-01

Background Electroporation-based therapies such as electrochemotherapy (ECT) and irreversible electroporation (IRE) are emerging as promising tools for treatment of tumors. When applied to the brain, electroporation can also induce transient blood-brain-barrier (BBB) disruption in volumes extending beyond IRE, thus enabling efficient drug penetration. The main objective of this study was to develop a statistical model predicting cell death and BBB disruption induced by electroporation. This model can be used for individual treatment planning. Material and methods Cell death and BBB disruption models were developed based on the Peleg-Fermi model in combination with numerical models of the electric field. The model calculates the electric field thresholds for cell kill and BBB disruption and describes the dependence on the number of treatment pulses. The model was validated using in vivo experimental data consisting of rats brains MRIs post electroporation treatments. Results Linear regression analysis confirmed that the model described the IRE and BBB disruption volumes as a function of treatment pulses number (r2 = 0.79; p < 0.008, r2 = 0.91; p < 0.001). The results presented a strong plateau effect as the pulse number increased. The ratio between complete cell death and no cell death thresholds was relatively narrow (between 0.88-0.91) even for small numbers of pulses and depended weakly on the number of pulses. For BBB disruption, the ratio increased with the number of pulses. BBB disruption radii were on average 67% ± 11% larger than IRE volumes. Conclusions The statistical model can be used to describe the dependence of treatment-effects on the number of pulses independent of the experimental setup. PMID:27069447

A Field-Effect Transistor (FET) model for ASAP

NASA Technical Reports Server (NTRS)

Ming, L.

1965-01-01

The derivation of the circuitry of a field effect transistor (FET) model, the procedure for adapting the model to automated statistical analysis program (ASAP), and the results of applying ASAP on this model are described.

Multivariate normality

NASA Technical Reports Server (NTRS)

Crutcher, H. L.; Falls, L. W.

1976-01-01

Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

A General Model for Estimating and Correcting the Effects of Nonindependence in Meta-Analysis.

ERIC Educational Resources Information Center

Strube, Michael J.

A general model is described which can be used to represent the four common types of meta-analysis: (1) estimation of effect size by combining study outcomes; (2) estimation of effect size by contrasting study outcomes; (3) estimation of statistical significance by combining study outcomes; and (4) estimation of statistical significance by…

Statistical model for forecasting monthly large wildfire events in western United States

Treesearch

Haiganoush K. Preisler; Anthony L. Westerling

2006-01-01

The ability to forecast the number and location of large wildfire events (with specified confidence bounds) is important to fire managers attempting to allocate and distribute suppression efforts during severe fire seasons. This paper describes the development of a statistical model for assessing the forecasting skills of fire-danger predictors and producing 1-month-...

A statistical approach to develop a detailed soot growth model using PAH characteristics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raj, Abhijeet; Celnik, Matthew; Shirley, Raphael

A detailed PAH growth model is developed, which is solved using a kinetic Monte Carlo algorithm. The model describes the structure and growth of planar PAH molecules, and is referred to as the kinetic Monte Carlo-aromatic site (KMC-ARS) model. A detailed PAH growth mechanism based on reactions at radical sites available in the literature, and additional reactions obtained from quantum chemistry calculations are used to model the PAH growth processes. New rates for the reactions involved in the cyclodehydrogenation process for the formation of 6-member rings on PAHs are calculated in this work based on density functional theory simulations. Themore » KMC-ARS model is validated by comparing experimentally observed ensembles on PAHs with the computed ensembles for a C{sub 2}H{sub 2} and a C{sub 6}H{sub 6} flame at different heights above the burner. The motivation for this model is the development of a detailed soot particle population balance model which describes the evolution of an ensemble of soot particles based on their PAH structure. However, at present incorporating such a detailed model into a population balance is computationally unfeasible. Therefore, a simpler model referred to as the site-counting model has been developed, which replaces the structural information of the PAH molecules by their functional groups augmented with statistical closure expressions. This closure is obtained from the KMC-ARS model, which is used to develop correlations and statistics in different flame environments which describe such PAH structural information. These correlations and statistics are implemented in the site-counting model, and results from the site-counting model and the KMC-ARS model are in good agreement. Additionally the effect of steric hindrance in large PAH structures is investigated and correlations for sites unavailable for reaction are presented. (author)« less

Statistical ecology comes of age.

PubMed

Gimenez, Olivier; Buckland, Stephen T; Morgan, Byron J T; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-12-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Statistical ecology comes of age

PubMed Central

Gimenez, Olivier; Buckland, Stephen T.; Morgan, Byron J. T.; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M.; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M.; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-01-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data. PMID:25540151

Mathematical and statistical models for determining the crop load in grapevine

NASA Astrophysics Data System (ADS)

Alina, Dobrei; Alin, Dobrei; Eleonora, Nistor; Teodor, Cristea; Marius, Boldea; Florin, Sala

2016-06-01

Ensuring a balance between vine crop load and vine vegetative growth is a dynamic process, so it is necessary to develop models for describing this relationship. This study analyzed the interrelationship between the crop load and growing specific parameters (viable buds - VB, dead (frost-injured) buds - DB, total shoots growth-TSG, one-year-old wood - MSG), in two vine grapes varieties: Muscat Ottonel cultivar for wine and Victoria cultivar for fresh grapes. In both varieties interrelationship between the buds number and vegetative growth parameters were described by polynomial functions statistically assured. Using regression analysis it was possible to develop predictive models for one-year-old wood (MSG), an important parameter for the yield and quality of wine grape production, with statistical significance results (R2 = 0.884, p <0.001, F = 45.957 in Muscat Ottonel cultivar and R2 = 0.893, p = 0.001, F = 49.886 in Victoria cultivar).

New approach in the quantum statistical parton distribution

NASA Astrophysics Data System (ADS)

Sohaily, Sozha; Vaziri (Khamedi), Mohammad

2017-12-01

An attempt to find simple parton distribution functions (PDFs) based on quantum statistical approach is presented. The PDFs described by the statistical model have very interesting physical properties which help to understand the structure of partons. The longitudinal portion of distribution functions are given by applying the maximum entropy principle. An interesting and simple approach to determine the statistical variables exactly without fitting and fixing parameters is surveyed. Analytic expressions of the x-dependent PDFs are obtained in the whole x region [0, 1], and the computed distributions are consistent with the experimental observations. The agreement with experimental data, gives a robust confirm of our simple presented statistical model.

Evaluation of the ecological relevance of mysid toxicity tests using population modeling techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuhn-Hines, A.; Munns, W.R. Jr.; Lussier, S.

1995-12-31

A number of acute and chronic bioassay statistics are used to evaluate the toxicity and risks of chemical stressors to the mysid shrimp, Mysidopsis bahia. These include LC{sub 50}S from acute tests, NOECs from 7-day and life-cycle tests, and the US EPA Water Quality Criteria Criterion Continuous Concentrations (CCC). Because these statistics are generated from endpoints which focus upon the responses of individual organisms, their relationships to significant effects at higher levels of ecological organization are unknown. This study was conducted to evaluate the quantitative relationships between toxicity test statistics and a concentration-based statistic derived from exposure-response models describing populationmore » growth rate ({lambda}) to stressor concentration. This statistic, C{sup {sm_bullet}} (concentration where {lambda} = I, zero population growth) describes the concentration above which mysid populations are projected to decline in abundance as determined using population modeling techniques. An analysis of M. bahia responses to 9 metals and 9 organic contaminants indicated the NOEC from life-cycle tests to be the best predictor of C{sup {sm_bullet}}, although the acute LC{sub 50} predicted population-level response surprisingly well. These analyses provide useful information regarding uncertainties of extrapolation among test statistics in assessments of ecological risk.« less

A d-statistic for single-case designs that is equivalent to the usual between-groups d-statistic.

PubMed

Shadish, William R; Hedges, Larry V; Pustejovsky, James E; Boyajian, Jonathan G; Sullivan, Kristynn J; Andrade, Alma; Barrientos, Jeannette L

2014-01-01

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

Development of a funding, cost, and spending model for satellite projects

NASA Technical Reports Server (NTRS)

Johnson, Jesse P.

1989-01-01

The need for a predictive budget/funging model is obvious. The current models used by the Resource Analysis Office (RAO) are used to predict the total costs of satellite projects. An effort to extend the modeling capabilities from total budget analysis to total budget and budget outlays over time analysis was conducted. A statistical based and data driven methodology was used to derive and develop the model. Th budget data for the last 18 GSFC-sponsored satellite projects were analyzed and used to build a funding model which would describe the historical spending patterns. This raw data consisted of dollars spent in that specific year and their 1989 dollar equivalent. This data was converted to the standard format used by the RAO group and placed in a database. A simple statistical analysis was performed to calculate the gross statistics associated with project length and project cost ant the conditional statistics on project length and project cost. The modeling approach used is derived form the theory of embedded statistics which states that properly analyzed data will produce the underlying generating function. The process of funding large scale projects over extended periods of time is described by Life Cycle Cost Models (LCCM). The data was analyzed to find a model in the generic form of a LCCM. The model developed is based on a Weibull function whose parameters are found by both nonlinear optimization and nonlinear regression. In order to use this model it is necessary to transform the problem from a dollar/time space to a percentage of total budget/time space. This transformation is equivalent to moving to a probability space. By using the basic rules of probability, the validity of both the optimization and the regression steps are insured. This statistically significant model is then integrated and inverted. The resulting output represents a project schedule which relates the amount of money spent to the percentage of project completion.

A Meta-Meta-Analysis: Empirical Review of Statistical Power, Type I Error Rates, Effect Sizes, and Model Selection of Meta-Analyses Published in Psychology

ERIC Educational Resources Information Center

Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T.

2010-01-01

This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…

A statistical approach to estimate O3 uptake of ponderosa pine in a mediterranean climate

Treesearch

N.E. Grulke; H.K. Preisler; C.C. Fan; W.A. Retzlaff

2002-01-01

In highly polluted sites, stomatal behavior is sluggish with respect to light, vapor pressure deficit, and internal CO2 concentration (Ci) and poorly described by existing models. Statistical models were developed to estimate stomatal conductance (gs) of 40-year-old ponderosa pine at three sites differing in pollutant exposure for the purpose of...

THE ATMOSPHERIC MODEL EVALUATION TOOL

EPA Science Inventory

This poster describes a model evaluation tool that is currently being developed and applied for meteorological and air quality model evaluation. The poster outlines the framework and provides examples of statistical evaluations that can be performed with the model evaluation tool...

The epistemology of mathematical and statistical modeling: a quiet methodological revolution.

PubMed

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.

Quantum-like model for the adaptive dynamics of the genetic regulation of E. coli's metabolism of glucose/lactose.

PubMed

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2012-06-01

We developed a quantum-like model describing the gene regulation of glucose/lactose metabolism in a bacterium, Escherichia coli. Our quantum-like model can be considered as a kind of the operational formalism for microbiology and genetics. Instead of trying to describe processes in a cell in the very detail, we propose a formal operator description. Such a description may be very useful in situation in which the detailed description of processes is impossible or extremely complicated. We analyze statistical data obtained from experiments, and we compute the degree of E. coli's preference within adaptive dynamics. It is known that there are several types of E. coli characterized by the metabolic system. We demonstrate that the same type of E. coli can be described by the well determined operators; we find invariant operator quantities characterizing each type. Such invariant quantities can be calculated from the obtained statistical data.

Development and Implementation of an Empirical Ionosphere Variability Model

NASA Technical Reports Server (NTRS)

Minow, Joesph I.; Almond, Deborah (Technical Monitor)

2002-01-01

Spacecraft designers and operations support personnel involved in space environment analysis for low Earth orbit missions require ionospheric specification and forecast models that provide not only average ionospheric plasma parameters for a given set of geophysical conditions but the statistical variations about the mean as well. This presentation describes the development of a prototype empirical model intended for use with the International Reference Ionosphere (IRI) to provide ionospheric Ne and Te variability. We first describe the database of on-orbit observations from a variety of spacecraft and ground based radars over a wide range of latitudes and altitudes used to obtain estimates of the environment variability. Next, comparison of the observations with the IRI model provide estimates of the deviations from the average model as well as the range of possible values that may correspond to a given IRI output. Options for implementation of the statistical variations in software that can be run with the IRI model are described. Finally, we provide example applications including thrust estimates for tethered satellites and specification of sunrise Ne, Te conditions required to support spacecraft charging issues for satellites with high voltage solar arrays.

Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan

USGS Publications Warehouse

Safaie, Ammar; Wendzel, Aaron; Ge, Zhongfu; Nevers, Meredith; Whitman, Richard L.; Corsi, Steven R.; Phanikumar, Mantha S.

2016-01-01

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

Topographic and Roughness Characteristics of the Vastitas Borealis Formation on Mars Described by Fractal Statistics

NASA Technical Reports Server (NTRS)

Garneau, S.; Plaut, J. J.

2000-01-01

The surface roughness of the Vastitas Borealis Formation on Mars was analyzed with fractal statistics. Root mean square slopes and fractal dimensions were calculated for 74 topographic profiles. Results have implications for radar scattering models.

Statistical analysis and model validation of automobile emissions

DOT National Transportation Integrated Search

2000-09-01

The article discusses the development of a comprehensive modal emissions model that is currently being integrated with a variety of transportation models as part of National Cooperative Highway Research Program project 25-11. Described is the second-...

Effective temperature in an interacting vertex system: theory and experiment on artificial spin ice.

PubMed

Nisoli, Cristiano; Li, Jie; Ke, Xianglin; Garand, D; Schiffer, Peter; Crespi, Vincent H

2010-07-23

Frustrated arrays of interacting single-domain nanomagnets provide important model systems for statistical mechanics, as they map closely onto well-studied vertex models and are amenable to direct imaging and custom engineering. Although these systems are manifestly athermal, we demonstrate that an effective temperature, controlled by an external magnetic drive, describes their microstates and therefore their full statistical properties.

Catalytic conversion reactions in nanoporous systems with concentration-dependent selectivity: Statistical mechanical modeling

DOE PAGES

Garcia, Andres; Wang, Jing; Windus, Theresa L.; ...

2016-05-20

Statistical mechanical modeling is developed to describe a catalytic conversion reaction A → B c or B t with concentration-dependent selectivity of the products, B c or B t, where reaction occurs inside catalytic particles traversed by narrow linear nanopores. The associated restricted diffusive transport, which in the extreme case is described by single-file diffusion, naturally induces strong concentration gradients. Hence, by comparing kinetic Monte Carlo simulation results with analytic treatments, selectivity is shown to be impacted by strong spatial correlations induced by restricted diffusivity in the presence of reaction and also by a subtle clustering of reactants, A.

Generalized theory of semiflexible polymers.

PubMed

Wiggins, Paul A; Nelson, Philip C

2006-03-01

DNA bending on length scales shorter than a persistence length plays an integral role in the translation of genetic information from DNA to cellular function. Quantitative experimental studies of these biological systems have led to a renewed interest in the polymer mechanics relevant for describing the conformational free energy of DNA bending induced by protein-DNA complexes. Recent experimental results from DNA cyclization studies have cast doubt on the applicability of the canonical semiflexible polymer theory, the wormlike chain (WLC) model, to DNA bending on biologically relevant length scales. This paper develops a theory of the chain statistics of a class of generalized semiflexible polymer models. Our focus is on the theoretical development of these models and the calculation of experimental observables. To illustrate our methods, we focus on a specific, illustrative model of DNA bending. We show that the WLC model generically describes the long-length-scale chain statistics of semiflexible polymers, as predicted by renormalization group arguments. In particular, we show that either the WLC or our present model adequately describes force-extension, solution scattering, and long-contour-length cyclization experiments, regardless of the details of DNA bend elasticity. In contrast, experiments sensitive to short-length-scale chain behavior can in principle reveal dramatic departures from the linear elastic behavior assumed in the WLC model. We demonstrate this explicitly by showing that our toy model can reproduce the anomalously large short-contour-length cyclization factors recently measured by Cloutier and Widom. Finally, we discuss the applicability of these models to DNA chain statistics in the context of future experiments.

Predicting long-term catchment nutrient export: the use of nonlinear time series models

NASA Astrophysics Data System (ADS)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the ARMA class. In most cases the relative improvement of SETAR models against AR models of first order was low ranging between 1% and 4% with the exception of the three-regime model for the River Stour time-series where the improvement was 48.9%. In comparison, the relative improvement of MSW models was between 44.6% and 52.5 for two-regime and from 60.4% to 75% for three-regime models. However, the visual assessment of models plotted against original datasets showed that despite a high value of RSS, some ARMA models could describe the analyzed time-series better than AR, MA and SETAR models with lower values of RSS. In both datasets MSW models provided a very good visual fit describing most of the extreme values.

The construction and assessment of a statistical model for the prediction of protein assay data.

PubMed

Pittman, J; Sacks, J; Young, S Stanley

2002-01-01

The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

NASA Astrophysics Data System (ADS)

Cofré, Rodrigo; Maldonado, Cesar

2018-01-01

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

Modeling the Test-Retest Statistics of a Localization Experiment in the Full Horizontal Plane.

PubMed

Morsnowski, André; Maune, Steffen

2016-10-01

Two approaches to model the test-retest statistics of a localization experiment basing on Gaussian distribution and on surrogate data are introduced. Their efficiency is investigated using different measures describing directional hearing ability. A localization experiment in the full horizontal plane is a challenging task for hearing impaired patients. In clinical routine, we use this experiment to evaluate the progress of our cochlear implant (CI) recipients. Listening and time effort limit the reproducibility. The localization experiment consists of a 12 loudspeaker circle, placed in an anechoic room, a "camera silens". In darkness, HSM sentences are presented at 65 dB pseudo-erratically from all 12 directions with five repetitions. This experiment is modeled by a set of Gaussian distributions with different standard deviations added to a perfect estimator, as well as by surrogate data. Five repetitions per direction are used to produce surrogate data distributions for the sensation directions. To investigate the statistics, we retrospectively use the data of 33 CI patients with 92 pairs of test-retest-measurements from the same day. The first model does not take inversions into account, (i.e., permutations of the direction from back to front and vice versa are not considered), although they are common for hearing impaired persons particularly in the rear hemisphere. The second model considers these inversions but does not work with all measures. The introduced models successfully describe test-retest statistics of directional hearing. However, since their applications on the investigated measures perform differently no general recommendation can be provided. The presented test-retest statistics enable pair test comparisons for localization experiments.

Statistical Description of Associative Memory

NASA Astrophysics Data System (ADS)

Samengo, Inés

2003-03-01

The storage of memories, in the brain, induces some kind of modification in the structural and functional properties of a neural network. Here, a few neuropsychological and neurophysiological experiments are reviewed, suggesting that the plastic changes taking place during memory storage are governed, among other things, by the correlations in the activity of a set of neurons. The Hopfield model is briefly described, showing the way the methods of statistical physics can be useful to describe the storage and retrieval of memories.

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

Computationally efficient statistical differential equation modeling using homogenization

USGS Publications Warehouse

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.

Structured statistical models of inductive reasoning.

PubMed

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.

Statistical methods for quantitative mass spectrometry proteomic experiments with labeling.

PubMed

Oberg, Ann L; Mahoney, Douglas W

2012-01-01

Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.

Statistical Compression for Climate Model Output

NASA Astrophysics Data System (ADS)

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.

Introductory Life Science Mathematics and Quantitative Neuroscience Courses

ERIC Educational Resources Information Center

Duffus, Dwight; Olifer, Andrei

2010-01-01

We describe two sets of courses designed to enhance the mathematical, statistical, and computational training of life science undergraduates at Emory College. The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics. The second is an…

Modeling Cell Size Regulation: From Single-Cell-Level Statistics to Molecular Mechanisms and Population-Level Effects.

PubMed

Ho, Po-Yi; Lin, Jie; Amir, Ariel

2018-05-20

Most microorganisms regulate their cell size. In this article, we review some of the mathematical formulations of the problem of cell size regulation. We focus on coarse-grained stochastic models and the statistics that they generate. We review the biologically relevant insights obtained from these models. We then describe cell cycle regulation and its molecular implementations, protein number regulation, and population growth, all in relation to size regulation. Finally, we discuss several future directions for developing understanding beyond phenomenological models of cell size regulation.

Application of Hierarchy Theory to Cross-Scale Hydrologic Modeling of Nutrient Loads

EPA Science Inventory

We describe a model called Regional Hydrologic Modeling for Environmental Evaluation 16 (RHyME²) for quantifying annual nutrient loads in stream networks and watersheds. RHyME² is 17 a cross-scale statistical and process-based water-quality model. The model ...

Teaching "Instant Experience" with Graphical Model Validation Techniques

ERIC Educational Resources Information Center

Ekstrøm, Claus Thorn

2014-01-01

Graphical model validation techniques for linear normal models are often used to check the assumptions underlying a statistical model. We describe an approach to provide "instant experience" in looking at a graphical model validation plot, so it becomes easier to validate if any of the underlying assumptions are violated.

Linearised and non-linearised isotherm models optimization analysis by error functions and statistical means

PubMed Central

2014-01-01

In adsorption study, to describe sorption process and evaluation of best-fitting isotherm model is a key analysis to investigate the theoretical hypothesis. Hence, numerous statistically analysis have been extensively used to estimate validity of the experimental equilibrium adsorption values with the predicted equilibrium values. Several statistical error analysis were carried out. In the present study, the following statistical analysis were carried out to evaluate the adsorption isotherm model fitness, like the Pearson correlation, the coefficient of determination and the Chi-square test, have been used. The ANOVA test was carried out for evaluating significance of various error functions and also coefficient of dispersion were evaluated for linearised and non-linearised models. The adsorption of phenol onto natural soil (Local name Kalathur soil) was carried out, in batch mode at 30 ± 20 C. For estimating the isotherm parameters, to get a holistic view of the analysis the models were compared between linear and non-linear isotherm models. The result reveled that, among above mentioned error functions and statistical functions were designed to determine the best fitting isotherm. PMID:25018878

Development of failure model for nickel cadmium cells

NASA Technical Reports Server (NTRS)

Gupta, A.

1980-01-01

The development of a method for the life prediction of nickel cadmium cells is discussed. The approach described involves acquiring an understanding of the mechanisms of degradation and failure and at the same time developing nondestructive evaluation techniques for the nickel cadmium cells. The development of a statistical failure model which will describe the mechanisms of degradation and failure is outlined.

75 FR 16202 - Notice of Issuance of Regulatory Guide

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-31

..., Revision 2, ``An Acceptable Model and Related Statistical Methods for the Analysis of Fuel Densification.... Introduction The U.S. Nuclear Regulatory Commission (NRC) is issuing a revision to an existing guide in the... nuclear power reactors. To meet these objectives, the guide describes statistical methods related to...

Assessment of credit risk based on fuzzy relations

NASA Astrophysics Data System (ADS)

Tsabadze, Teimuraz

2017-06-01

The purpose of this paper is to develop a new approach for an assessment of the credit risk to corporate borrowers. There are different models for borrowers' risk assessment. These models are divided into two groups: statistical and theoretical. When assessing the credit risk for corporate borrowers, statistical model is unacceptable due to the lack of sufficiently large history of defaults. At the same time, we cannot use some theoretical models due to the lack of stock exchange. In those cases, when studying a particular borrower given that statistical base does not exist, the decision-making process is always of expert nature. The paper describes a new approach that may be used in group decision-making. An example of the application of the proposed approach is given.

Statistical models of lunar rocks and regolith

NASA Technical Reports Server (NTRS)

Marcus, A. H.

1973-01-01

The mathematical, statistical, and computational approaches used in the investigation of the interrelationship of lunar fragmental material, regolith, lunar rocks, and lunar craters are described. The first two phases of the work explored the sensitivity of the production model of fragmental material to mathematical assumptions, and then completed earlier studies on the survival of lunar surface rocks with respect to competing processes. The third phase combined earlier work into a detailed statistical analysis and probabilistic model of regolith formation by lithologically distinct layers, interpreted as modified crater ejecta blankets. The fourth phase of the work dealt with problems encountered in combining the results of the entire project into a comprehensive, multipurpose computer simulation model for the craters and regolith. Highlights of each phase of research are given.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Unwin, Stephen D.; Lowry, Peter P.; Layton, Robert F.

This is a working report drafted under the Risk-Informed Safety Margin Characterization pathway of the Light Water Reactor Sustainability Program, describing statistical models of passives component reliabilities.

Localized Statistics for DW-MRI Fiber Bundle Segmentation

PubMed Central

Lankton, Shawn; Melonakos, John; Malcolm, James; Dambreville, Samuel; Tannenbaum, Allen

2013-01-01

We describe a method for segmenting neural fiber bundles in diffusion-weighted magnetic resonance images (DWMRI). As these bundles traverse the brain to connect regions, their local orientation of diffusion changes drastically, hence a constant global model is inaccurate. We propose a method to compute localized statistics on orientation information and use it to drive a variational active contour segmentation that accurately models the non-homogeneous orientation information present along the bundle. Initialized from a single fiber path, the proposed method proceeds to capture the entire bundle. We demonstrate results using the technique to segment the cingulum bundle and describe several extensions making the technique applicable to a wide range of tissues. PMID:23652079

The effect of clulstering of galaxies on the statistics of gravitational lenses

NASA Technical Reports Server (NTRS)

Anderson, N.; Alcock, C.

1986-01-01

It is examined whether clustering of galaxies can significantly alter the statistical properties of gravitational lenses? Only models of clustering that resemble the observed distribution of galaxies in the properties of the two-point correlation function are considered. Monte-Carlo simulations of the imaging process are described. It is found that the effect of clustering is too small to be significant, unless the mass of the deflectors is so large that gravitational lenses become common occurrences. A special model is described which was concocted to optimize the effect of clustering on gravitational lensing but still resemble the observed distribution of galaxies; even this simulation did not satisfactorily produce large numbers of wide-angle lenses.

Use of microcomputers for planning and managing silviculture habitat relationships.

Treesearch

B.G. Marcot; R.S. McNay; R.E. Page

1988-01-01

Microcomputers aid in monitoring, modeling, and decision support for integrating objectives of silviculture and wildlife habitat management. Spreadsheets, data bases, statistics, and graphics programs are described for use in monitoring. Stand growth models, modeling languages, area and geobased information systems, and optimization models are discussed for use in...

Specifying and Refining a Complex Measurement Model.

ERIC Educational Resources Information Center

Levy, Roy; Mislevy, Robert J.

This paper aims to describe a Bayesian approach to modeling and estimating cognitive models both in terms of statistical machinery and actual instrument development. Such a method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines…

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

PubMed

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.

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants

PubMed Central

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-01-01

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. PMID:24834325

How Statisticians Speak Risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Redus, K.S.

2007-07-01

The foundation of statistics deals with (a) how to measure and collect data and (b) how to identify models using estimates of statistical parameters derived from the data. Risk is a term used by the statistical community and those that employ statistics to express the results of a statistically based study. Statistical risk is represented as a probability that, for example, a statistical model is sufficient to describe a data set; but, risk is also interpreted as a measure of worth of one alternative when compared to another. The common thread of any risk-based problem is the combination of (a)more » the chance an event will occur, with (b) the value of the event. This paper presents an introduction to, and some examples of, statistical risk-based decision making from a quantitative, visual, and linguistic sense. This should help in understanding areas of radioactive waste management that can be suitably expressed using statistical risk and vice-versa. (authors)« less

Probabilistic Graphical Model Representation in Phylogenetics

PubMed Central

Höhna, Sebastian; Heath, Tracy A.; Boussau, Bastien; Landis, Michael J.; Ronquist, Fredrik; Huelsenbeck, John P.

2014-01-01

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.] PMID:24951559

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task

NASA Astrophysics Data System (ADS)

Halbrügge, Marc

2010-12-01

This paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

Biophysical model for assessment of risk of acute exposures in combination with low level chronic irradiation

NASA Astrophysics Data System (ADS)

Smirnova, O. A.

A biophysical model is developed which describes the mortality dynamics in mammalian populations unexposed and exposed to radiation The model relates statistical biometric functions mortality rate life span probability density and life span probability with statistical characteristics and dynamics of a critical body system in individuals composing the population The model describing the dynamics of thrombocytopoiesis in nonirradiated and irradiated mammals is also developed this hematopoietic line being considered as the critical body system under exposures in question The mortality model constructed in the framework of the proposed approach was identified to reproduce the irradiation effects on populations of mice The most parameters of the thrombocytopoiesis model were determined from the data available in the literature on hematology and radiobiology the rest parameters were evaluated by fitting some experimental data on the dynamics of this system in acutely irradiated mice The successful verification of the thrombocytopoiesis model was fulfilled by the quantitative juxtaposition of the modeling predictions and experimental data on the dynamics of this system in mice exposed to either acute or chronic irradiation at wide ranges of doses and dose rates It is important that only experimental data on the mortality rate in nonirradiated population and the relevant statistical characteristics of the thrombocytopoiesis system in mice which are also available in the literature on radiobiology are needed for the final identification of

Statistical learning and selective inference.

PubMed

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Developing Risk Prediction Models for Kidney Injury and Assessing Incremental Value for Novel Biomarkers

PubMed Central

Kerr, Kathleen F.; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G.

2014-01-01

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients’ risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. PMID:24855282

Dark energy models through nonextensive Tsallis' statistics

NASA Astrophysics Data System (ADS)

Barboza, Edésio M.; Nunes, Rafael da C.; Abreu, Everton M. C.; Ananias Neto, Jorge

2015-10-01

The accelerated expansion of the Universe is one of the greatest challenges of modern physics. One candidate to explain this phenomenon is a new field called dark energy. In this work we have used the Tsallis nonextensive statistical formulation of the Friedmann equation to explore the Barboza-Alcaniz and Chevalier-Polarski-Linder parametric dark energy models and the Wang-Meng and Dalal vacuum decay models. After that, we have discussed the observational tests and the constraints concerning the Tsallis nonextensive parameter. Finally, we have described the dark energy physics through the role of the q-parameter.

Use of transport models for wildfire behavior simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Linn, R.R.; Harlow, F.H.

1998-01-01

Investigators have attempted to describe the behavior of wildfires for over fifty years. Current models for numerical description are mainly algebraic and based on statistical or empirical ideas. The authors have developed a transport model called FIRETEC. The use of transport formulations connects the propagation rates to the full conservation equations for energy, momentum, species concentrations, mass, and turbulence. In this paper, highlights of the model formulation and results are described. The goal of the FIRETEC model is to describe most probable average behavior of wildfires in a wide variety of conditions. FIRETEC represents the essence of the combination ofmore » many small-scale processes without resolving each process in complete detail.« less

Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

PubMed

LaBudde, Robert A; Harnly, James M

2012-01-01

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

Integrating Mediators and Moderators in Research Design

ERIC Educational Resources Information Center

MacKinnon, David P.

2011-01-01

The purpose of this article is to describe mediating variables and moderating variables and provide reasons for integrating them in outcome studies. Separate sections describe examples of moderating and mediating variables and the simplest statistical model for investigating each variable. The strengths and limitations of incorporating mediating…

Assessment and prediction of inter-joint upper limb movement correlations based on kinematic analysis and statistical regression

NASA Astrophysics Data System (ADS)

Toth-Tascau, Mirela; Balanean, Flavia; Krepelka, Mircea

2013-10-01

Musculoskeletal impairment of the upper limb can cause difficulties in performing basic daily activities. Three dimensional motion analyses can provide valuable data of arm movement in order to precisely determine arm movement and inter-joint coordination. The purpose of this study was to develop a method to evaluate the degree of impairment based on the influence of shoulder movements in the amplitude of elbow flexion and extension based on the assumption that a lack of motion of the elbow joint will be compensated by an increased shoulder activity. In order to develop and validate a statistical model, one healthy young volunteer has been involved in the study. The activity of choice simulated blowing the nose, starting from a slight flexion of the elbow and raising the hand until the middle finger touches the tip of the nose and return to the start position. Inter-joint coordination between the elbow and shoulder movements showed significant correlation. Statistical regression was used to fit an equation model describing the influence of shoulder movements on the elbow mobility. The study provides a brief description of the kinematic analysis protocol and statistical models that may be useful in describing the relation between inter-joint movements of daily activities.

Computer program to minimize prediction error in models from experiments with 16 hypercube points and 0 to 6 center points

NASA Technical Reports Server (NTRS)

Holms, A. G.

1982-01-01

A previous report described a backward deletion procedure of model selection that was optimized for minimum prediction error and which used a multiparameter combination of the F - distribution and an order statistics distribution of Cochran's. A computer program is described that applies the previously optimized procedure to real data. The use of the program is illustrated by examples.

[Hungarian health resource allocation from the viewpoint of the English methodology].

PubMed

Fadgyas-Freyler, Petra

2018-02-01

This paper describes both the English health resource allocation and the attempt of its Hungarian adaptation. We describe calculations for a Hungarian regression model using the English 'weighted capitation formula'. The model has proven statistically correct. New independent variables and homogenous regional units have to be found for Hungary. The English method can be used with adequate variables. Hungarian patient-level health data can support a much more sophisticated model. Further research activities are needed. Orv Hetil. 2018; 159(5): 183-191.

AGARD Bulletin. Technical Programme, 1981.

DTIC Science & Technology

1980-08-01

ionospheric effect models and their associated codes. Physical, statistical , and nybrid models will be described in a comprehensive manner. Descriptions...will be to review: The various conventional modes of optical correction required either by ametropias or by normal or pathological drops in visual

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. Part 2: Theoretical development of a dynamic model and application to rain fade durations and tolerable control delays for fade countermeasures

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1987-01-01

A dynamic rain attenuation prediction model is developed for use in obtaining the temporal characteristics, on time scales of minutes or hours, of satellite communication link availability. Analagous to the associated static rain attenuation model, which yields yearly attenuation predictions, this dynamic model is applicable at any location in the world that is characterized by the static rain attenuation statistics peculiar to the geometry of the satellite link and the rain statistics of the location. Such statistics are calculated by employing the formalism of Part I of this report. In fact, the dynamic model presented here is an extension of the static model and reduces to the static model in the appropriate limit. By assuming that rain attenuation is dynamically described by a first-order stochastic differential equation in time and that this random attenuation process is a Markov process, an expression for the associated transition probability is obtained by solving the related forward Kolmogorov equation. This transition probability is then used to obtain such temporal rain attenuation statistics as attenuation durations and allowable attenuation margins versus control system delay.

Damage modeling and statistical analysis of optics damage performance in MJ-class laser systems.

PubMed

Liao, Zhi M; Raymond, B; Gaylord, J; Fallejo, R; Bude, J; Wegner, P

2014-11-17

Modeling the lifetime of a fused silica optic is described for a multiple beam, MJ-class laser system. This entails combining optic processing data along with laser shot data to account for complete history of optic processing and shot exposure. Integrating with online inspection data allows for the construction of a performance metric to describe how an optic performs with respect to the model. This methodology helps to validate the damage model as well as allows strategic planning and identifying potential hidden parameters that are affecting the optic's performance.

Multilevel modelling: Beyond the basic applications.

PubMed

Wright, Daniel B; London, Kamala

2009-05-01

Over the last 30 years statistical algorithms have been developed to analyse datasets that have a hierarchical/multilevel structure. Particularly within developmental and educational psychology these techniques have become common where the sample has an obvious hierarchical structure, like pupils nested within a classroom. We describe two areas beyond the basic applications of multilevel modelling that are important to psychology: modelling the covariance structure in longitudinal designs and using generalized linear multilevel modelling as an alternative to methods from signal detection theory (SDT). Detailed code for all analyses is described using packages for the freeware R.

Analysis and modeling of wafer-level process variability in 28 nm FD-SOI using split C-V measurements

NASA Astrophysics Data System (ADS)

Pradeep, Krishna; Poiroux, Thierry; Scheer, Patrick; Juge, André; Gouget, Gilles; Ghibaudo, Gérard

2018-07-01

This work details the analysis of wafer level global process variability in 28 nm FD-SOI using split C-V measurements. The proposed approach initially evaluates the native on wafer process variability using efficient extraction methods on split C-V measurements. The on-wafer threshold voltage (VT) variability is first studied and modeled using a simple analytical model. Then, a statistical model based on the Leti-UTSOI compact model is proposed to describe the total C-V variability in different bias conditions. This statistical model is finally used to study the contribution of each process parameter to the total C-V variability.

United States Census 2000 Population with Bridged Race Categories. Vital and Health Statistics. Data Evaluation and Methods Research.

ERIC Educational Resources Information Center

Ingram, Deborah D.; Parker, Jennifer D.; Schenker, Nathaniel; Weed, James A.; Hamilton, Brady; Arias, Elizabeth; Madans, Jennifer H.

This report documents the National Center for Health Statistics' (NCHS) methods for bridging the Census 2000 multiple-race resident population to single-race categories and describing bridged race resident population estimates. Data came from the pooled 1997-2000 National Health Interview Surveys. The bridging models included demographic and…

Comparison of thermal signatures of a mine buried in mineral and organic soils

NASA Astrophysics Data System (ADS)

Lamorski, K.; Pregowski, Piotr; Swiderski, Waldemar; Usowicz, B.; Walczak, R. T.

2001-10-01

Values of thermal signature of a mine buried in soils, which ave different properties, were compared using mathematical- statistical modeling. There was applied a model of transport phenomena in the soil, which takes into consideration water and energy transfer. The energy transport is described using Fourier's equation. Liquid phase transport of water is calculated using Richard's model of water flow in porous medium. For the comparison, there were selected two soils: mineral and organic, which differs significantly in thermal and hydrological properties. The heat capacity of soil was estimated using de Vries model. The thermal conductivity was calculated using a statistical model, which incorprates fundamental soil physical properties. The model of soil thermal conductivity was built on the base of heat resistance, two Kirchhoff's laws and polynomial distribution. Soil hydrological properties were described using Mualem-van Genuchten model. The impact of thermal properties of the medium in which a mien had been placed on its thermal signature in the conditions of heat input was presented. The dependence was stated between observed thermal signature of a mine and thermal parameters of the medium.

Statistical Parameter Study of the Time Interval Distribution for Nonparalyzable, Paralyzable, and Hybrid Dead Time Models

NASA Astrophysics Data System (ADS)

Syam, Nur Syamsi; Maeng, Seongjin; Kim, Myo Gwang; Lim, Soo Yeon; Lee, Sang Hoon

2018-05-01

A large dead time of a Geiger Mueller (GM) detector may cause a large count loss in radiation measurements and consequently may cause distortion of the Poisson statistic of radiation events into a new distribution. The new distribution will have different statistical parameters compared to the original distribution. Therefore, the variance, skewness, and excess kurtosis in association with the observed count rate of the time interval distribution for well-known nonparalyzable, paralyzable, and nonparalyzable-paralyzable hybrid dead time models of a Geiger Mueller detector were studied using Monte Carlo simulation (GMSIM). These parameters were then compared with the statistical parameters of a perfect detector to observe the change in the distribution. The results show that the behaviors of the statistical parameters for the three dead time models were different. The values of the skewness and the excess kurtosis of the nonparalyzable model are equal or very close to those of the perfect detector, which are ≅2 for skewness, and ≅6 for excess kurtosis, while the statistical parameters in the paralyzable and hybrid model obtain minimum values that occur around the maximum observed count rates. The different trends of the three models resulting from the GMSIM simulation can be used to distinguish the dead time behavior of a GM counter; i.e. whether the GM counter can be described best by using the nonparalyzable, paralyzable, or hybrid model. In a future study, these statistical parameters need to be analyzed further to determine the possibility of using them to determine a dead time for each model, particularly for paralyzable and hybrid models.

Statistics of Dark Matter Halos from Gravitational Lensing.

PubMed

Jain; Van Waerbeke L

2000-02-10

We present a new approach to measure the mass function of dark matter halos and to discriminate models with differing values of Omega through weak gravitational lensing. We measure the distribution of peaks from simulated lensing surveys and show that the lensing signal due to dark matter halos can be detected for a wide range of peak heights. Even when the signal-to-noise ratio is well below the limit for detection of individual halos, projected halo statistics can be constrained for halo masses spanning galactic to cluster halos. The use of peak statistics relies on an analytical model of the noise due to the intrinsic ellipticities of source galaxies. The noise model has been shown to accurately describe simulated data for a variety of input ellipticity distributions. We show that the measured peak distribution has distinct signatures of gravitational lensing, and its non-Gaussian shape can be used to distinguish models with different values of Omega. The use of peak statistics is complementary to the measurement of field statistics, such as the ellipticity correlation function, and is possibly not susceptible to the same systematic errors.

Pattern-Based Inverse Modeling for Characterization of Subsurface Flow Models with Complex Geologic Heterogeneity

NASA Astrophysics Data System (ADS)

Golmohammadi, A.; Jafarpour, B.; M Khaninezhad, M. R.

2017-12-01

Calibration of heterogeneous subsurface flow models leads to ill-posed nonlinear inverse problems, where too many unknown parameters are estimated from limited response measurements. When the underlying parameters form complex (non-Gaussian) structured spatial connectivity patterns, classical variogram-based geostatistical techniques cannot describe the underlying connectivity patterns. Modern pattern-based geostatistical methods that incorporate higher-order spatial statistics are more suitable for describing such complex spatial patterns. Moreover, when the underlying unknown parameters are discrete (geologic facies distribution), conventional model calibration techniques that are designed for continuous parameters cannot be applied directly. In this paper, we introduce a novel pattern-based model calibration method to reconstruct discrete and spatially complex facies distributions from dynamic flow response data. To reproduce complex connectivity patterns during model calibration, we impose a feasibility constraint to ensure that the solution follows the expected higher-order spatial statistics. For model calibration, we adopt a regularized least-squares formulation, involving data mismatch, pattern connectivity, and feasibility constraint terms. Using an alternating directions optimization algorithm, the regularized objective function is divided into a continuous model calibration problem, followed by mapping the solution onto the feasible set. The feasibility constraint to honor the expected spatial statistics is implemented using a supervised machine learning algorithm. The two steps of the model calibration formulation are repeated until the convergence criterion is met. Several numerical examples are used to evaluate the performance of the developed method.

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Significance testing of rules in rule-based models of human problem solving

NASA Technical Reports Server (NTRS)

Lewis, C. M.; Hammer, J. M.

1986-01-01

Rule-based models of human problem solving have typically not been tested for statistical significance. Three methods of testing rules - analysis of variance, randomization, and contingency tables - are presented. Advantages and disadvantages of the methods are also described.

Forecast and virtual weather driven plant disease risk modeling system

USDA-ARS?s Scientific Manuscript database

We describe a system in use and development that leverages public weather station data, several spatialized weather forecast types, leaf wetness estimation, generic plant disease models, and online statistical evaluation. Convergent technological developments in all these areas allow, with funding f...

Statistical label fusion with hierarchical performance models

PubMed Central

Asman, Andrew J.; Dagley, Alexander S.; Landman, Bennett A.

2014-01-01

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy. PMID:24817809

Statistical Models for the Analysis and Design of Digital Polymerase Chain Reaction (dPCR) Experiments.

PubMed

Dorazio, Robert M; Hunter, Margaret E

2015-11-03

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log-log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model's parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

DOE PAGES

Castruccio, Stefano; McInerney, David J.; Stein, Michael L.; ...

2014-02-24

The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO 2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as patternmore » scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. In conclusion, it may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.« less

Vibration Response Models of a Stiffened Aluminum Plate Excited by a Shaker

NASA Technical Reports Server (NTRS)

Cabell, Randolph H.

2008-01-01

Numerical models of structural-acoustic interactions are of interest to aircraft designers and the space program. This paper describes a comparison between two energy finite element codes, a statistical energy analysis code, a structural finite element code, and the experimentally measured response of a stiffened aluminum plate excited by a shaker. Different methods for modeling the stiffeners and the power input from the shaker are discussed. The results show that the energy codes (energy finite element and statistical energy analysis) accurately predicted the measured mean square velocity of the plate. In addition, predictions from an energy finite element code had the best spatial correlation with measured velocities. However, predictions from a considerably simpler, single subsystem, statistical energy analysis model also correlated well with the spatial velocity distribution. The results highlight a need for further work to understand the relationship between modeling assumptions and the prediction results.

Use of observational and model-derived fields and regime model output statistics in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Forbes, G. S.; Pielke, R. A.

1985-01-01

Various empirical and statistical weather-forecasting studies which utilize stratification by weather regime are described. Objective classification was used to determine weather regime in some studies. In other cases the weather pattern was determined on the basis of a parameter representing the physical and dynamical processes relevant to the anticipated mesoscale phenomena, such as low level moisture convergence and convective precipitation, or the Froude number and the occurrence of cold-air damming. For mesoscale phenomena already in existence, new forecasting techniques were developed. The use of cloud models in operational forecasting is discussed. Models to calculate the spatial scales of forcings and resultant response for mesoscale systems are presented. The use of these models to represent the climatologically most prevalent systems, and to perform case-by-case simulations is reviewed. Operational implementation of mesoscale data into weather forecasts, using both actual simulation output and method-output statistics is discussed.

Incorporating signal-dependent noise for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Morman, Christopher J.; Meola, Joseph

2015-05-01

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

Statistical methods for investigating quiescence and other temporal seismicity patterns

USGS Publications Warehouse

Matthews, M.V.; Reasenberg, P.A.

1988-01-01

We propose a statistical model and a technique for objective recognition of one of the most commonly cited seismicity patterns:microearthquake quiescence. We use a Poisson process model for seismicity and define a process with quiescence as one with a particular type of piece-wise constant intensity function. From this model, we derive a statistic for testing stationarity against a 'quiescence' alternative. The large-sample null distribution of this statistic is approximated from simulated distributions of appropriate functionals applied to Brownian bridge processes. We point out the restrictiveness of the particular model we propose and of the quiescence idea in general. The fact that there are many point processes which have neither constant nor quiescent rate functions underscores the need to test for and describe nonuniformity thoroughly. We advocate the use of the quiescence test in conjunction with various other tests for nonuniformity and with graphical methods such as density estimation. ideally these methods may promote accurate description of temporal seismicity distributions and useful characterizations of interesting patterns. ?? 1988 Birkha??user Verlag.

Composite Linear Models | Division of Cancer Prevention

Cancer.gov

By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

HINDERED DIFFUSION OF COAL LIQUIDS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Theodore T. Tsotsis; Muhammad Sahimi; Ian A. Webster

1996-01-01

It was the purpose of the project described here to carry out careful and detailed investigations of petroleum and coal asphaltene transport through model porous systems under a broad range of temperature conditions. The experimental studies were to be coupled with detailed, in-depth statistical and molecular dynamics models intended to provide a fundamental understanding of the overall transport mechanisms and a more accurate concept of the asphaltene structure. The following discussion describes some of our accomplishments.

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory. [Project Psychometric Aspects of Item Banking No. 53.] Research Report 91-1.

ERIC Educational Resources Information Center

Kelderman, Henk

In this paper, algorithms are described for obtaining the maximum likelihood estimates of the parameters in log-linear models. Modified versions of the iterative proportional fitting and Newton-Raphson algorithms are described that work on the minimal sufficient statistics rather than on the usual counts in the full contingency table. This is…

Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process.

PubMed

Jahn, Patrick; Berg, Rune W; Hounsgaard, Jørn; Ditlevsen, Susanne

2011-11-01

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein-Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein-Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential.

Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

PubMed

Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2014-08-07

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

A Stochastic Fractional Dynamics Model of Rainfall Statistics

NASA Astrophysics Data System (ADS)

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.

Using decision trees to understand structure in missing data

PubMed Central

Tierney, Nicholas J; Harden, Fiona A; Harden, Maurice J; Mengersen, Kerrie L

2015-01-01

Objectives Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting Data taken from employees at 3 different industrial sites in Australia. Participants 7915 observations were included. Materials and methods The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions Researchers are encouraged to use CART and BRT models to explore and understand missing data. PMID:26124509

Analysis and meta-analysis of single-case designs: an introduction.

PubMed

Shadish, William R

2014-04-01

The last 10 years have seen great progress in the analysis and meta-analysis of single-case designs (SCDs). This special issue includes five articles that provide an overview of current work on that topic, including standardized mean difference statistics, multilevel models, Bayesian statistics, and generalized additive models. Each article analyzes a common example across articles and presents syntax or macros for how to do them. These articles are followed by commentaries from single-case design researchers and journal editors. This introduction briefly describes each article and then discusses several issues that must be addressed before we can know what analyses will eventually be best to use in SCD research. These issues include modeling trend, modeling error covariances, computing standardized effect size estimates, assessing statistical power, incorporating more accurate models of outcome distributions, exploring whether Bayesian statistics can improve estimation given the small samples common in SCDs, and the need for annotated syntax and graphical user interfaces that make complex statistics accessible to SCD researchers. The article then discusses reasons why SCD researchers are likely to incorporate statistical analyses into their research more often in the future, including changing expectations and contingencies regarding SCD research from outside SCD communities, changes and diversity within SCD communities, corrections of erroneous beliefs about the relationship between SCD research and statistics, and demonstrations of how statistics can help SCD researchers better meet their goals. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Power Analysis for Complex Mediational Designs Using Monte Carlo Methods

ERIC Educational Resources Information Center

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2010-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex…

Goodness-of-Fit Assessment of Item Response Theory Models

ERIC Educational Resources Information Center

Maydeu-Olivares, Alberto

2013-01-01

The article provides an overview of goodness-of-fit assessment methods for item response theory (IRT) models. It is now possible to obtain accurate "p"-values of the overall fit of the model if bivariate information statistics are used. Several alternative approaches are described. As the validity of inferences drawn on the fitted model…

Microgenetic Patterns of Children's Multiplication Learning: Confirming the Overlapping Waves Model by Latent Growth Modeling

ERIC Educational Resources Information Center

van der Ven, Sanne H. G.; Boom, Jan; Kroesbergen, Evelyn H.; Leseman, Paul P. M.

2012-01-01

Variability in strategy selection is an important characteristic of learning new skills such as mathematical skills. Strategies gradually come and go during this development. In 1996, Siegler described this phenomenon as ''overlapping waves.'' In the current microgenetic study, we attempted to model these overlapping waves statistically. In…

Modeling Ka-band low elevation angle propagation statistics

NASA Technical Reports Server (NTRS)

Russell, Thomas A.; Weinfield, John; Pearson, Chris; Ippolito, Louis J.

1995-01-01

The statistical variability of the secondary atmospheric propagation effects on satellite communications cannot be ignored at frequencies of 20 GHz or higher, particularly if the propagation margin allocation is such that link availability falls below 99 percent. The secondary effects considered in this paper are gaseous absorption, cloud absorption, and tropospheric scintillation; rain attenuation is the primary effect. Techniques and example results are presented for estimation of the overall combined impact of the atmosphere on satellite communications reliability. Statistical methods are employed throughout and the most widely accepted models for the individual effects are used wherever possible. The degree of correlation between the effects is addressed and some bounds on the expected variability in the combined effects statistics are derived from the expected variability in correlation. Example estimates are presented of combined effects statistics in the Washington D.C. area of 20 GHz and 5 deg elevation angle. The statistics of water vapor are shown to be sufficient for estimation of the statistics of gaseous absorption at 20 GHz. A computer model based on monthly surface weather is described and tested. Significant improvement in prediction of absorption extremes is demonstrated with the use of path weather data instead of surface data.

Testing statistical self-similarity in the topology of river networks

USGS Publications Warehouse

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2010-01-01

Recent work has demonstrated that the topological properties of real river networks deviate significantly from predictions of Shreve's random model. At the same time the property of mean self-similarity postulated by Tokunaga's model is well supported by data. Recently, a new class of network model called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to replicate important topological features observed in real river networks. We investigate if the hypothesis of statistical self-similarity in the RSN model is supported by data on a set of 30 basins located across the continental United States that encompass a wide range of hydroclimatic variability. We demonstrate that the generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. The parameters describing the distribution of interior and exterior generators are tested to be statistically different and the difference is shown to produce the well-known Hack's law. The inter-basin variability of RSN parameters is found to be statistically significant. We also test generator dependence on two climatic indices, mean annual precipitation and radiative index of dryness. Some indication of climatic influence on the generators is detected, but this influence is not statistically significant with the sample size available. Finally, two key applications of the RSN model to hydrology and geomorphology are briefly discussed.

Clinical study of the Erlanger silver catheter--data management and biometry.

PubMed

Martus, P; Geis, C; Lugauer, S; Böswald, M; Guggenbichler, J P

1999-01-01

The clinical evaluation of venous catheters for catheter-induced infections must conform to a strict biometric methodology. The statistical planning of the study (target population, design, degree of blinding), data management (database design, definition of variables, coding), quality assurance (data inspection at several levels) and the biometric evaluation of the Erlanger silver catheter project are described. The three-step data flow included: 1) primary data from the hospital, 2) relational database, 3) files accessible for statistical evaluation. Two different statistical models were compared: analyzing the first catheter only of a patient in the analysis (independent data) and analyzing several catheters from the same patient (dependent data) by means of the generalized estimating equations (GEE) method. The main result of the study was based on the comparison of both statistical models.

Statistical Analysis of Large-Scale Structure of Universe

NASA Astrophysics Data System (ADS)

Tugay, A. V.

While galaxy cluster catalogs were compiled many decades ago, other structural elements of cosmic web are detected at definite level only in the newest works. For example, extragalactic filaments were described by velocity field and SDSS galaxy distribution during the last years. Large-scale structure of the Universe could be also mapped in the future using ATHENA observations in X-rays and SKA in radio band. Until detailed observations are not available for the most volume of Universe, some integral statistical parameters can be used for its description. Such methods as galaxy correlation function, power spectrum, statistical moments and peak statistics are commonly used with this aim. The parameters of power spectrum and other statistics are important for constraining the models of dark matter, dark energy, inflation and brane cosmology. In the present work we describe the growth of large-scale density fluctuations in one- and three-dimensional case with Fourier harmonics of hydrodynamical parameters. In result we get power-law relation for the matter power spectrum.

Bayesian demography 250 years after Bayes

PubMed Central

Bijak, Jakub; Bryant, John

2016-01-01

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889

A statistical mechanics model for free-for-all airplane passenger boarding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steffen, Jason H.; /Fermilab

2008-08-01

I discuss a model for free-for-all passenger boarding which is employed by some discount air carriers. The model is based on the principles of statistical mechanics where each seat in the aircraft has an associated energy which reflects the preferences of travelers. As each passenger enters the airplane they select their seats using Boltzmann statistics, proceed to that location, load their luggage, sit down, and the partition function seen by remaining passengers is modified to reflect this fact. I discuss the various model parameters and make qualitative comparisons of this passenger boarding model with those that involve assigned seats. Themore » model can be used to predict the probability that certain seats will be occupied at different times during the boarding process. These results might provide a useful description of this boarding method. The model is a relatively unusual application of undergraduate level physics and describes a situation familiar to many students and faculty.« less

Virtual Model Validation of Complex Multiscale Systems: Applications to Nonlinear Elastostatics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oden, John Tinsley; Prudencio, Ernest E.; Bauman, Paul T.

We propose a virtual statistical validation process as an aid to the design of experiments for the validation of phenomenological models of the behavior of material bodies, with focus on those cases in which knowledge of the fabrication process used to manufacture the body can provide information on the micro-molecular-scale properties underlying macroscale behavior. One example is given by models of elastomeric solids fabricated using polymerization processes. We describe a framework for model validation that involves Bayesian updates of parameters in statistical calibration and validation phases. The process enables the quanti cation of uncertainty in quantities of interest (QoIs) andmore » the determination of model consistency using tools of statistical information theory. We assert that microscale information drawn from molecular models of the fabrication of the body provides a valuable source of prior information on parameters as well as a means for estimating model bias and designing virtual validation experiments to provide information gain over calibration posteriors.« less

Bayesian inference of physiologically meaningful parameters from body sway measurements.

PubMed

Tietäväinen, A; Gutmann, M U; Keski-Vakkuri, E; Corander, J; Hæggström, E

2017-06-19

The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.

Observations, theoretical ideas and modeling of turbulent flows: Past, present and future

NASA Technical Reports Server (NTRS)

Chapman, G. T.; Tobak, M.

1985-01-01

Turbulence was analyzed in a historical context featuring the interactions between observations, theoretical ideas, and modeling within three successive movements. These are identified as predominantly statistical, structural and deterministic. The statistical movement is criticized for its failure to deal with the structural elements observed in turbulent flows. The structural movement is criticized for its failure to embody observed structural elements within a formal theory. The deterministic movement is described as having the potential of overcoming these deficiencies by allowing structural elements to exhibit chaotic behavior that is nevertheless embodied within a theory. Four major ideas of this movement are described: bifurcation theory, strange attractors, fractals, and the renormalization group. A framework for the future study of turbulent flows is proposed, based on the premises of the deterministic movement.

Exciton-photon correlations in bosonic condensates of exciton-polaritons

PubMed Central

Kavokin, Alexey V.; Sheremet, Alexandra S.; Shelykh, Ivan A.; Lagoudakis, Pavlos G.; Rubo, Yuri G.

2015-01-01

Exciton-polaritons are mixed light-matter quasiparticles. We have developed a statistical model describing stochastic exciton-photon transitions within a condensate of exciton polaritons. We show that the exciton-photon correlator depends on the rate of incoherent exciton-photon transformations in the condensate. We discuss implications of this effect for the quantum statistics of photons emitted by polariton lasers. PMID:26153979

Exciton-photon correlations in bosonic condensates of exciton-polaritons.

PubMed

Kavokin, Alexey V; Sheremet, Alexandra S; Shelykh, Ivan A; Lagoudakis, Pavlos G; Rubo, Yuri G

2015-07-08

Exciton-polaritons are mixed light-matter quasiparticles. We have developed a statistical model describing stochastic exciton-photon transitions within a condensate of exciton polaritons. We show that the exciton-photon correlator depends on the rate of incoherent exciton-photon transformations in the condensate. We discuss implications of this effect for the quantum statistics of photons emitted by polariton lasers.

Estimating chloramine C x T for the synergistic inactivation of Cryptosporidium with ozone followed by chloramine

EPA Science Inventory

The author describes a statistical model that can be used to account for the error in estimating the required chloramine concentration times time (C x T) to inactivate Cryptosporidium oocysts with ozone followed by chloramine in drinking water. The safety factor described in the ...

Localized Smart-Interpretation

NASA Astrophysics Data System (ADS)

Lundh Gulbrandsen, Mats; Mejer Hansen, Thomas; Bach, Torben; Pallesen, Tom

2014-05-01

The complex task of setting up a geological model consists not only of combining available geological information into a conceptual plausible model, but also requires consistency with availably data, e.g. geophysical data. However, in many cases the direct geological information, e.g borehole samples, are very sparse, so in order to create a geological model, the geologist needs to rely on the geophysical data. The problem is however, that the amount of geophysical data in many cases are so vast that it is practically impossible to integrate all of them in the manual interpretation process. This means that a lot of the information available from the geophysical surveys are unexploited, which is a problem, due to the fact that the resulting geological model does not fulfill its full potential and hence are less trustworthy. We suggest an approach to geological modeling that 1. allow all geophysical data to be considered when building the geological model 2. is fast 3. allow quantification of geological modeling. The method is constructed to build a statistical model, f(d,m), describing the relation between what the geologists interpret, d, and what the geologist knows, m. The para- meter m reflects any available information that can be quantified, such as geophysical data, the result of a geophysical inversion, elevation maps, etc... The parameter d reflects an actual interpretation, such as for example the depth to the base of a ground water reservoir. First we infer a statistical model f(d,m), by examining sets of actual interpretations made by a geological expert, [d1, d2, ...], and the information used to perform the interpretation; [m1, m2, ...]. This makes it possible to quantify how the geological expert performs interpolation through f(d,m). As the geological expert proceeds interpreting, the number of interpreted datapoints from which the statistical model is inferred increases, and therefore the accuracy of the statistical model increases. When a model f(d,m) successfully has been inferred, we are able to simulate how the geological expert would perform an interpretation given some external information m, through f(d|m). We will demonstrate this method applied on geological interpretation and densely sampled airborne electromagnetic data. In short, our goal is to build a statistical model describing how a geological expert performs geological interpretation given some geophysical data. We then wish to use this statistical model to perform semi automatic interpretation, everywhere where such geophysical data exist, in a manner consistent with the choices made by a geological expert. Benefits of such a statistical model are that 1. it provides a quantification of how a geological expert performs interpretation based on available diverse data 2. all available geophysical information can be used 3. it allows much faster interpretation of large data sets.

Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments

USGS Publications Warehouse

Dorazio, Robert; Hunter, Margaret

2015-01-01

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log–log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model’s parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Dynamic modelling of n-of-1 data: powerful and flexible data analytics applied to individualised studies.

PubMed

Vieira, Rute; McDonald, Suzanne; Araújo-Soares, Vera; Sniehotta, Falko F; Henderson, Robin

2017-09-01

N-of-1 studies are based on repeated observations within an individual or unit over time and are acknowledged as an important research method for generating scientific evidence about the health or behaviour of an individual. Statistical analyses of n-of-1 data require accurate modelling of the outcome while accounting for its distribution, time-related trend and error structures (e.g., autocorrelation) as well as reporting readily usable contextualised effect sizes for decision-making. A number of statistical approaches have been documented but no consensus exists on which method is most appropriate for which type of n-of-1 design. We discuss the statistical considerations for analysing n-of-1 studies and briefly review some currently used methodologies. We describe dynamic regression modelling as a flexible and powerful approach, adaptable to different types of outcomes and capable of dealing with the different challenges inherent to n-of-1 statistical modelling. Dynamic modelling borrows ideas from longitudinal and event history methodologies which explicitly incorporate the role of time and the influence of past on future. We also present an illustrative example of the use of dynamic regression on monitoring physical activity during the retirement transition. Dynamic modelling has the potential to expand researchers' access to robust and user-friendly statistical methods for individualised studies.

The MSFC UNIVAC 1108 EXEC 8 simulation model

NASA Technical Reports Server (NTRS)

Williams, T. G.; Richards, F. M.; Weatherbee, J. E.; Paul, L. K.

1972-01-01

A model is presented which simulates the MSFC Univac 1108 multiprocessor system. The hardware/operating system is described to enable a good statistical measurement of the system behavior. The performance of the 1108 is evaluated by performing twenty-four different experiments designed to locate system bottlenecks and also to test the sensitivity of system throughput with respect to perturbation of the various Exec 8 scheduling algorithms. The model is implemented in the general purpose system simulation language and the techniques described can be used to assist in the design, development, and evaluation of multiprocessor systems.

Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

NASA Astrophysics Data System (ADS)

Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Montaru, Maxime; Riu, Delphine

2014-01-01

This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models.

Statistical properties of a cloud ensemble - A numerical study

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Simpson, Joanne; Soong, Su-Tzai

1987-01-01

The statistical properties of cloud ensembles under a specified large-scale environment, such as mass flux by cloud drafts and vertical velocity as well as the condensation and evaporation associated with these cloud drafts, are examined using a three-dimensional numerical cloud ensemble model described by Soong and Ogura (1980) and Tao and Soong (1986). The cloud drafts are classified as active and inactive, and separate contributions to cloud statistics in areas of different cloud activity are then evaluated. The model results compare well with results obtained from aircraft measurements of a well-organized ITCZ rainband that occurred on August 12, 1974, during the Global Atmospheric Research Program's Atlantic Tropical Experiment.

Hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis method for mid-frequency analysis of built-up systems with epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yin, Shengwen; Yu, Dejie; Yin, Hui; Lü, Hui; Xia, Baizhan

2017-09-01

Considering the epistemic uncertainties within the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model when it is used for the response analysis of built-up systems in the mid-frequency range, the hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis (ETFE/SEA) model is established by introducing the evidence theory. Based on the hybrid ETFE/SEA model and the sub-interval perturbation technique, the hybrid Sub-interval Perturbation and Evidence Theory-based Finite Element/Statistical Energy Analysis (SIP-ETFE/SEA) approach is proposed. In the hybrid ETFE/SEA model, the uncertainty in the SEA subsystem is modeled by a non-parametric ensemble, while the uncertainty in the FE subsystem is described by the focal element and basic probability assignment (BPA), and dealt with evidence theory. Within the hybrid SIP-ETFE/SEA approach, the mid-frequency response of interest, such as the ensemble average of the energy response and the cross-spectrum response, is calculated analytically by using the conventional hybrid FE/SEA method. Inspired by the probability theory, the intervals of the mean value, variance and cumulative distribution are used to describe the distribution characteristics of mid-frequency responses of built-up systems with epistemic uncertainties. In order to alleviate the computational burdens for the extreme value analysis, the sub-interval perturbation technique based on the first-order Taylor series expansion is used in ETFE/SEA model to acquire the lower and upper bounds of the mid-frequency responses over each focal element. Three numerical examples are given to illustrate the feasibility and effectiveness of the proposed method.

Numerical and Qualitative Contrasts of Two Statistical Models ...

EPA Pesticide Factsheets

Two statistical approaches, weighted regression on time, discharge, and season and generalized additive models, have recently been used to evaluate water quality trends in estuaries. Both models have been used in similar contexts despite differences in statistical foundations and products. This study provided an empirical and qualitative comparison of both models using 29 years of data for two discrete time series of chlorophyll-a (chl-a) in the Patuxent River estuary. Empirical descriptions of each model were based on predictive performance against the observed data, ability to reproduce flow-normalized trends with simulated data, and comparisons of performance with validation datasets. Between-model differences were apparent but minor and both models had comparable abilities to remove flow effects from simulated time series. Both models similarly predicted observations for missing data with different characteristics. Trends from each model revealed distinct mainstem influences of the Chesapeake Bay with both models predicting a roughly 65% increase in chl-a over time in the lower estuary, whereas flow-normalized predictions for the upper estuary showed a more dynamic pattern, with a nearly 100% increase in chl-a in the last 10 years. Qualitative comparisons highlighted important differences in the statistical structure, available products, and characteristics of the data and desired analysis. This manuscript describes a quantitative comparison of two recently-

Spatial statistical network models for stream and river temperature in New England, USA

EPA Science Inventory

Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May–September growing ...

A Stochastic Fractional Dynamics Model of Space-time Variability of Rain

NASA Technical Reports Server (NTRS)

Kundu, Prasun K.; Travis, James E.

2013-01-01

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 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. 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 radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

PubMed Central

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

Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

PubMed

Mørk, Søren; Holmes, Ian

2012-03-01

Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.

PubMed

MacLean, Adam L; Harrington, Heather A; Stumpf, Michael P H; Byrne, Helen M

2016-01-01

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Gaussian statistics of the cosmic microwave background: Correlation of temperature extrema in the COBE DMR two-year sky maps

NASA Technical Reports Server (NTRS)

Kogut, A.; Banday, A. J.; Bennett, C. L.; Hinshaw, G.; Lubin, P. M.; Smoot, G. F.

1995-01-01

We use the two-point correlation function of the extrema points (peaks and valleys) in the Cosmic Background Explorer (COBE) Differential Microwave Radiometers (DMR) 2 year sky maps as a test for non-Gaussian temperature distribution in the cosmic microwave background anisotropy. A maximum-likelihood analysis compares the DMR data to n = 1 toy models whose random-phase spherical harmonic components a(sub lm) are drawn from either Gaussian, chi-square, or log-normal parent populations. The likelihood of the 53 GHz (A+B)/2 data is greatest for the exact Gaussian model. There is less than 10% chance that the non-Gaussian models tested describe the DMR data, limited primarily by type II errors in the statistical inference. The extrema correlation function is a stronger test for this class of non-Gaussian models than topological statistics such as the genus.

The coalescent process in models with selection and recombination.

PubMed

Hudson, R R; Kaplan, N L

1988-11-01

The statistical properties of the process describing the genealogical history of a random sample of genes at a selectively neutral locus which is linked to a locus at which natural selection operates are investigated. It is found that the equations describing this process are simple modifications of the equations describing the process assuming that the two loci are completely linked. Thus, the statistical properties of the genealogical process for a random sample at a neutral locus linked to a locus with selection follow from the results obtained for the selected locus. Sequence data from the alcohol dehydrogenase (Adh) region of Drosophila melanogaster are examined and compared to predictions based on the theory. It is found that the spatial distribution of nucleotide differences between Fast and Slow alleles of Adh is very similar to the spatial distribution predicted if balancing selection operates to maintain the allozyme variation at the Adh locus. The spatial distribution of nucleotide differences between different Slow alleles of Adh do not match the predictions of this simple model very well.

How to hit HIV where it hurts

NASA Astrophysics Data System (ADS)

Chakraborty, Arup

No medical procedure has saved more lives than vaccination. But, today, some pathogens have evolved which have defied successful vaccination using the empirical paradigms pioneered by Pasteur and Jenner. One characteristic of many pathogens for which successful vaccines do not exist is that they present themselves in various guises. HIV is an extreme example because of its high mutability. This highly mutable virus can evade natural or vaccine induced immune responses, often by mutating at multiple sites linked by compensatory interactions. I will describe first how by bringing to bear ideas from statistical physics (e.g., maximum entropy models, Hopfield models, Feynman variational theory) together with in vitro experiments and clinical data, the fitness landscape of HIV is beginning to be defined with explicit account for collective mutational pathways. I will describe how this knowledge can be harnessed for vaccine design. Finally, I will describe how ideas at the intersection of evolutionary biology, immunology, and statistical physics can help guide the design of strategies that may be able to induce broadly neutralizing antibodies.

Emergent Societal Effects of Crimino-Social Forces in an Animat Agent Model

NASA Astrophysics Data System (ADS)

Scogings, Chris J.; Hawick, Ken A.

Societal behaviour can be studied at a causal level by perturbing a stable multi-agent model with new microscopic behaviours and observing the statistical response over an ensemble of simulated model systems. We report on the effects of introducing criminal and law-enforcing behaviours into a large scale animat agent model and describe the complex spatial agent patterns and population changes that result. Our well-established predator-prey substrate model provides a background framework against which these new microscopic behaviours can be trialled and investigated. We describe some quantitative results and some surprising conclusions concerning the overall societal health when individually anti-social behaviour is introduced.

Spacing distribution functions for the one-dimensional point-island model with irreversible attachment

NASA Astrophysics Data System (ADS)

González, Diego Luis; Pimpinelli, Alberto; Einstein, T. L.

2011-07-01

We study the configurational structure of the point-island model for epitaxial growth in one dimension. In particular, we calculate the island gap and capture zone distributions. Our model is based on an approximate description of nucleation inside the gaps. Nucleation is described by the joint probability density pnXY(x,y), which represents the probability density to have nucleation at position x within a gap of size y. Our proposed functional form for pnXY(x,y) describes excellently the statistical behavior of the system. We compare our analytical model with extensive numerical simulations. Our model retains the most relevant physical properties of the system.

Analysis of Variance in Statistical Image Processing

NASA Astrophysics Data System (ADS)

Kurz, Ludwik; Hafed Benteftifa, M.

1997-04-01

A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

Turbulence modeling for hypersonic flows

NASA Technical Reports Server (NTRS)

Marvin, J. G.; Coakley, T. J.

1989-01-01

Turbulence modeling for high speed compressible flows is described and discussed. Starting with the compressible Navier-Stokes equations, methods of statistical averaging are described by means of which the Reynolds-averaged Navier-Stokes equations are developed. Unknown averages in these equations are approximated using various closure concepts. Zero-, one-, and two-equation eddy viscosity models, algebraic stress models and Reynolds stress transport models are discussed. Computations of supersonic and hypersonic flows obtained using several of the models are discussed and compared with experimental results. Specific examples include attached boundary layer flows, shock wave boundary layer interactions and compressible shear layers. From these examples, conclusions regarding the status of modeling and recommendations for future studies are discussed.

Designing a Qualitative Data Collection Strategy (QDCS) for Africa - Phase 1: A Gap Analysis of Existing Models, Simulations, and Tools Relating to Africa

DTIC Science & Technology

2012-06-01

generalized behavioral model characterized after the fictional Seldon equations (the one elaborated upon by Isaac Asimov in the 1951 novel, The...Foundation). Asimov described the Seldon equations as essentially statistical models with historical data of a sufficient size and variability that they

Modeling forest scenic beauty: Concepts and application to ponderosa pine

Treesearch

Thomas C. Brown; Terry C. Daniel

1984-01-01

Statistical models are presented which relate near-view scenic beauty of ponderosa pine stands in the Southwest to variables describing physical characteristics. The models suggest that herbage and large ponderosa pine contribute to scenic beauty, while numbers of small and intermediate-sized pine trees and downed wood, especially as slash, detract from scenic beauty....

Supporting the Development of Conceptions of Statistics by Engaging Students in Measuring and Modeling Variability

ERIC Educational Resources Information Center

Lehrer, Richard; Kim, Min-joung; Schauble, Leona

2007-01-01

New capabilities in "TinkerPlots 2.0" supported the conceptual development of fifth- and sixth-grade students as they pursued several weeks of instruction that emphasized data modeling. The instruction highlighted links between data analysis, chance, and modeling in the context of describing and explaining the distributions of measures that result…

[Methodology of the description of atmospheric air pollution by nitrogen dioxide by land use regression method in Ekaterinburg].

PubMed

Antropov, K M; Varaksin, A N

2013-01-01

This paper provides the description of Land Use Regression (LUR) modeling and the result of its application in the study of nitrogen dioxide air pollution in Ekaterinburg. The paper describes the difficulties of the modeling for air pollution caused by motor vehicles exhaust, and the ways to address these challenges. To create LUR model of the NO2 air pollution in Ekaterinburg, concentrations of NO2 were measured, data on factors affecting air pollution were collected, a statistical analysis of the data were held. A statistical model of NO2 air pollution (coefficient of determination R2 = 0.70) and a map of pollution were created.

A statistical model for radar images of agricultural scenes

NASA Technical Reports Server (NTRS)

Frost, V. S.; Shanmugan, K. S.; Holtzman, J. C.; Stiles, J. A.

1982-01-01

The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

High Accuracy Transistor Compact Model Calibrations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hembree, Charles E.; Mar, Alan; Robertson, Perry J.

2015-09-01

Typically, transistors are modeled by the application of calibrated nominal and range models. These models consists of differing parameter values that describe the location and the upper and lower limits of a distribution of some transistor characteristic such as current capacity. Correspond- ingly, when using this approach, high degrees of accuracy of the transistor models are not expected since the set of models is a surrogate for a statistical description of the devices. The use of these types of models describes expected performances considering the extremes of process or transistor deviations. In contrast, circuits that have very stringent accuracy requirementsmore » require modeling techniques with higher accuracy. Since these accurate models have low error in transistor descriptions, these models can be used to describe part to part variations as well as an accurate description of a single circuit instance. Thus, models that meet these stipulations also enable the calculation of quantifi- cation of margins with respect to a functional threshold and uncertainties in these margins. Given this need, new model high accuracy calibration techniques for bipolar junction transis- tors have been developed and are described in this report.« less

Directional Statistics for Polarization Observations of Individual Pulses from Radio Pulsars

NASA Astrophysics Data System (ADS)

McKinnon, M. M.

2010-10-01

Radio polarimetry is a three-dimensional statistical problem. The three-dimensional aspect of the problem arises from the Stokes parameters Q, U, and V, which completely describe the polarization of electromagnetic radiation and conceptually define the orientation of a polarization vector in the Poincaré sphere. The statistical aspect of the problem arises from the random fluctuations in the source-intrinsic polarization and the instrumental noise. A simple model for the polarization of pulsar radio emission has been used to derive the three-dimensional statistics of radio polarimetry. The model is based upon the proposition that the observed polarization is due to the incoherent superposition of two, highly polarized, orthogonal modes. The directional statistics derived from the model follow the Bingham-Mardia and Fisher family of distributions. The model assumptions are supported by the qualitative agreement between the statistics derived from it and those measured with polarization observations of the individual pulses from pulsars. The orthogonal modes are thought to be the natural modes of radio wave propagation in the pulsar magnetosphere. The intensities of the modes become statistically independent when generalized Faraday rotation (GFR) in the magnetosphere causes the difference in their phases to be large. A stochastic version of GFR occurs when fluctuations in the phase difference are also large, and may be responsible for the more complicated polarization patterns observed in pulsar radio emission.

Incorporating Covariates into Stochastic Blockmodels

ERIC Educational Resources Information Center

Sweet, Tracy M.

2015-01-01

Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…

Investigating the American Time Use Survey from an Exposure Modeling Perspective

EPA Science Inventory

This paper describes an evaluation of the U.S. Bureau of Labor Statistics' American Time Use Survey (ATUS) for potential use in modeling human exposures to environmental pollutants. The ATUS is a large, on-going, cross-sectional survey of where Americans spend time and what activ...

Evaluation Statistics Computed for the Wave Information Studies (WIS)

DTIC Science & Technology

2016-07-01

Studies (WIS) by Mary A. Bryant, Tyler J. Hesser, and Robert E. Jensen PURPOSE: This Coastal and Hydraulics Engineering Technical Note (CHETN...describes the statistical metrics used by the Wave Information Studies (WIS) and produced as part of the model evaluation process. INTRODUCTION: The...gauge locations along the Pacific, Great Lakes, Gulf of Mexico , Atlantic, and Western Alaska coasts. Estimates of wave climatology produced by ocean

Modeling Cross-Situational Word–Referent Learning: Prior Questions

PubMed Central

Yu, Chen; Smith, Linda B.

2013-01-01

Both adults and young children possess powerful statistical computation capabilities—they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together. PMID:22229490

Statistical properties of exciton fine structure splitting and polarization angles in quantum dot ensembles

NASA Astrophysics Data System (ADS)

Gong, Ming; Hofer, B.; Zallo, E.; Trotta, R.; Luo, Jun-Wei; Schmidt, O. G.; Zhang, Chuanwei

2014-05-01

We develop an effective model to describe the statistical properties of exciton fine structure splitting (FSS) and polarization angle in quantum dot ensembles (QDEs) using only a few symmetry-related parameters. The connection between the effective model and the random matrix theory is established. Such effective model is verified both theoretically and experimentally using several rather different types of QDEs, each of which contains hundreds to thousands of QDs. The model naturally addresses three fundamental issues regarding the FSS and polarization angels of QDEs, which are frequently encountered in both theories and experiments. The answers to these fundamental questions yield an approach to characterize the optical properties of QDEs. Potential applications of the effective model are also discussed.

Avalanches, loading and finite size effects in 2D amorphous plasticity: results from a finite element model

NASA Astrophysics Data System (ADS)

Sandfeld, Stefan; Budrikis, Zoe; Zapperi, Stefano; Fernandez Castellanos, David

2015-02-01

Crystalline plasticity is strongly interlinked with dislocation mechanics and nowadays is relatively well understood. Concepts and physical models of plastic deformation in amorphous materials on the other hand—where the concept of linear lattice defects is not applicable—still are lagging behind. We introduce an eigenstrain-based finite element lattice model for simulations of shear band formation and strain avalanches. Our model allows us to study the influence of surfaces and finite size effects on the statistics of avalanches. We find that even with relatively complex loading conditions and open boundary conditions, critical exponents describing avalanche statistics are unchanged, which validates the use of simpler scalar lattice-based models to study these phenomena.

Symmetry Transition Preserving Chirality in QCD: A Versatile Random Matrix Model

NASA Astrophysics Data System (ADS)

Kanazawa, Takuya; Kieburg, Mario

2018-06-01

We consider a random matrix model which interpolates between the chiral Gaussian unitary ensemble and the Gaussian unitary ensemble while preserving chiral symmetry. This ensemble describes flavor symmetry breaking for staggered fermions in 3D QCD as well as in 4D QCD at high temperature or in 3D QCD at a finite isospin chemical potential. Our model is an Osborn-type two-matrix model which is equivalent to the elliptic ensemble but we consider the singular value statistics rather than the complex eigenvalue statistics. We report on exact results for the partition function and the microscopic level density of the Dirac operator in the ɛ regime of QCD. We compare these analytical results with Monte Carlo simulations of the matrix model.

An operational GLS model for hydrologic regression

USGS Publications Warehouse

Tasker, Gary D.; Stedinger, J.R.

1989-01-01

Recent Monte Carlo studies have documented the value of generalized least squares (GLS) procedures to estimate empirical relationships between streamflow statistics and physiographic basin characteristics. This paper presents a number of extensions of the GLS method that deal with realities and complexities of regional hydrologic data sets that were not addressed in the simulation studies. These extensions include: (1) a more realistic model of the underlying model errors; (2) smoothed estimates of cross correlation of flows; (3) procedures for including historical flow data; (4) diagnostic statistics describing leverage and influence for GLS regression; and (5) the formulation of a mathematical program for evaluating future gaging activities. ?? 1989.

Mathematic model analysis of Gaussian beam propagation through an arbitrary thickness random phase screen.

PubMed

Tian, Yuzhen; Guo, Jin; Wang, Rui; Wang, Tingfeng

2011-09-12

In order to research the statistical properties of Gaussian beam propagation through an arbitrary thickness random phase screen for adaptive optics and laser communication application in the laboratory, we establish mathematic models of statistical quantities, which are based on the Rytov method and the thin phase screen model, involved in the propagation process. And the analytic results are developed for an arbitrary thickness phase screen based on the Kolmogorov power spectrum. The comparison between the arbitrary thickness phase screen and the thin phase screen shows that it is more suitable for our results to describe the generalized case, especially the scintillation index.

Statistical dielectronic recombination rates for multielectron ions in plasma

NASA Astrophysics Data System (ADS)

Demura, A. V.; Leont'iev, D. S.; Lisitsa, V. S.; Shurygin, V. A.

2017-10-01

We describe the general analytic derivation of the dielectronic recombination (DR) rate coefficient for multielectron ions in a plasma based on the statistical theory of an atom in terms of the spatial distribution of the atomic electron density. The dielectronic recombination rates for complex multielectron tungsten ions are calculated numerically in a wide range of variation of the plasma temperature, which is important for modern nuclear fusion studies. The results of statistical theory are compared with the data obtained using level-by-level codes ADPAK, FAC, HULLAC, and experimental results. We consider different statistical DR models based on the Thomas-Fermi distribution, viz., integral and differential with respect to the orbital angular momenta of the ion core and the trapped electron, as well as the Rost model, which is an analog of the Frank-Condon model as applied to atomic structures. In view of its universality and relative simplicity, the statistical approach can be used for obtaining express estimates of the dielectronic recombination rate coefficients in complex calculations of the parameters of the thermonuclear plasmas. The application of statistical methods also provides information for the dielectronic recombination rates with much smaller computer time expenditures as compared to available level-by-level codes.

Modeling of Dissipation Element Statistics in Turbulent Non-Premixed Jet Flames

NASA Astrophysics Data System (ADS)

Denker, Dominik; Attili, Antonio; Boschung, Jonas; Hennig, Fabian; Pitsch, Heinz

2017-11-01

The dissipation element (DE) analysis is a method for analyzing and compartmentalizing turbulent scalar fields. DEs can be described by two parameters, namely the Euclidean distance l between their extremal points and the scalar difference in the respective points Δϕ . The joint probability density function (jPDF) of these two parameters P(Δϕ , l) is expected to suffice for a statistical reconstruction of the scalar field. In addition, reacting scalars show a strong correlation with these DE parameters in both premixed and non-premixed flames. Normalized DE statistics show a remarkable invariance towards changes in Reynolds numbers. This feature of DE statistics was exploited in a Boltzmann-type evolution equation based model for the probability density function (PDF) of the distance between the extremal points P(l) in isotropic turbulence. Later, this model was extended for the jPDF P(Δϕ , l) and then adapted for the use in free shear flows. The effect of heat release on the scalar scales and DE statistics is investigated and an extended model for non-premixed jet flames is introduced, which accounts for the presence of chemical reactions. This new model is validated against a series of DNS of temporally evolving jet flames. European Research Council Project ``Milestone''.

Magnetic Helicity and Planetary Dynamos

NASA Technical Reports Server (NTRS)

Shebalin, John V.

2012-01-01

A model planetary dynamo based on the Boussinesq approximation along with homogeneous boundary conditions is considered. A statistical theory describing a large-scale MHD dynamo is found, in which magnetic helicity is the critical parameter

Strengthen forensic entomology in court--the need for data exploration and the validation of a generalised additive mixed model.

PubMed

Baqué, Michèle; Amendt, Jens

2013-01-01

Developmental data of juvenile blow flies (Diptera: Calliphoridae) are typically used to calculate the age of immature stages found on or around a corpse and thus to estimate a minimum post-mortem interval (PMI(min)). However, many of those data sets don't take into account that immature blow flies grow in a non-linear fashion. Linear models do not supply a sufficient reliability on age estimates and may even lead to an erroneous determination of the PMI(min). According to the Daubert standard and the need for improvements in forensic science, new statistic tools like smoothing methods and mixed models allow the modelling of non-linear relationships and expand the field of statistical analyses. The present study introduces into the background and application of these statistical techniques by analysing a model which describes the development of the forensically important blow fly Calliphora vicina at different temperatures. The comparison of three statistical methods (linear regression, generalised additive modelling and generalised additive mixed modelling) clearly demonstrates that only the latter provided regression parameters that reflect the data adequately. We focus explicitly on both the exploration of the data--to assure their quality and to show the importance of checking it carefully prior to conducting the statistical tests--and the validation of the resulting models. Hence, we present a common method for evaluating and testing forensic entomological data sets by using for the first time generalised additive mixed models.

Combustion Technology for Incinerating Wastes from Air Force Industrial Processes.

DTIC Science & Technology

1984-02-01

The assumption of equilibrium between environmental compartments. * The statistical extrapolations yielding "safe" doses of various constituents...would be contacted to identify the assumptions and data requirements needed to design, construct and implement the model. The model’s primary objective...Recovery Planning Model (RRPLAN) is described. This section of the paper summarizes the model’s assumptions , major components and modes of operation

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.

Hidden Markov model analysis of force/torque information in telemanipulation

NASA Technical Reports Server (NTRS)

Hannaford, Blake; Lee, Paul

1991-01-01

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

Action detection by double hierarchical multi-structure space-time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-03-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

A statistical inference for concentrations of benzo[a]pyrene partially measured in the ambient air of an industrial city in Korea

NASA Astrophysics Data System (ADS)

Kim, Yongku; Seo, Young-Kyo; Baek, Sung-Ok

2013-12-01

Although large quantities of air pollutants are released into the atmosphere, they are partially monitored and routinely assessed for their health implications. This paper proposes a statistical model describing the temporal behavior of hazardous air pollutants (HAPs), which can have negative effects on human health. Benzo[a]pyrene (BaP) is selected for statistical modeling. The proposed model incorporates the linkage between BaP and meteorology and is specifically formulated to identify meteorological effects and allow for seasonal trends. The model is used to estimate and forecast temporal fields of BaP conditional on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing health indicators, namely the cancer risk and the hazard quotient. The model provides useful information for the optimal monitoring period and projection of future BaP concentrations for both industrial and residential areas in Korea.

Action detection by double hierarchical multi-structure space–time statistical matching model

NASA Astrophysics Data System (ADS)

Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang

2018-06-01

Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.

Implication of correlations among some common stability statistics - a Monte Carlo simulations.

PubMed

Piepho, H P

1995-03-01

Stability analysis of multilocation trials is often based on a mixed two-way model. Two stability measures in frequent use are the environmental variance (S i (2) )and the ecovalence (W i). Under the two-way model the rank orders of the expected values of these two statistics are identical for a given set of genotypes. By contrast, empirical rank correlations among these measures are consistently low. This suggests that the two-way mixed model may not be appropriate for describing real data. To check this hypothesis, a Monte Carlo simulation was conducted. It revealed that the low empirical rank correlation amongS i (2) and W i is most likely due to sampling errors. It is concluded that the observed low rank correlation does not invalidate the two-way model. The paper also discusses tests for homogeneity of S i (2) as well as implications of the two-way model for the classification of stability statistics.

Atmospheric Tracer Inverse Modeling Using Markov Chain Monte Carlo (MCMC)

NASA Astrophysics Data System (ADS)

Kasibhatla, P.

2004-12-01

In recent years, there has been an increasing emphasis on the use of Bayesian statistical estimation techniques to characterize the temporal and spatial variability of atmospheric trace gas sources and sinks. The applications have been varied in terms of the particular species of interest, as well as in terms of the spatial and temporal resolution of the estimated fluxes. However, one common characteristic has been the use of relatively simple statistical models for describing the measurement and chemical transport model error statistics and prior source statistics. For example, multivariate normal probability distribution functions (pdfs) are commonly used to model these quantities and inverse source estimates are derived for fixed values of pdf paramaters. While the advantage of this approach is that closed form analytical solutions for the a posteriori pdfs of interest are available, it is worth exploring Bayesian analysis approaches which allow for a more general treatment of error and prior source statistics. Here, we present an application of the Markov Chain Monte Carlo (MCMC) methodology to an atmospheric tracer inversion problem to demonstrate how more gereral statistical models for errors can be incorporated into the analysis in a relatively straightforward manner. The MCMC approach to Bayesian analysis, which has found wide application in a variety of fields, is a statistical simulation approach that involves computing moments of interest of the a posteriori pdf by efficiently sampling this pdf. The specific inverse problem that we focus on is the annual mean CO2 source/sink estimation problem considered by the TransCom3 project. TransCom3 was a collaborative effort involving various modeling groups and followed a common modeling and analysis protocoal. As such, this problem provides a convenient case study to demonstrate the applicability of the MCMC methodology to atmospheric tracer source/sink estimation problems.

Scale Dependence of Statistics of Spatially Averaged Rain Rate Seen in TOGA COARE Comparison with Predictions from a Stochastic Model

NASA Technical Reports Server (NTRS)

Kundu, Prasun K.; Bell, T. L.; Lau, William K. M. (Technical Monitor)

2002-01-01

A characteristic feature of rainfall statistics is that they in general depend on the space and time scales over which rain data are averaged. As a part of an earlier effort to determine the sampling error of satellite rain averages, a space-time model of rainfall statistics was developed to describe the statistics of gridded rain observed in GATE. The model allows one to compute the second moment statistics of space- and time-averaged rain rate which can be fitted to satellite or rain gauge data to determine the four model parameters appearing in the precipitation spectrum - an overall strength parameter, a characteristic length separating the long and short wavelength regimes and a characteristic relaxation time for decay of the autocorrelation of the instantaneous local rain rate and a certain 'fractal' power law exponent. For area-averaged instantaneous rain rate, this exponent governs the power law dependence of these statistics on the averaging length scale $L$ predicted by the model in the limit of small $L$. In particular, the variance of rain rate averaged over an $L \\times L$ area exhibits a power law singularity as $L \\rightarrow 0$. In the present work the model is used to investigate how the statistics of area-averaged rain rate over the tropical Western Pacific measured with ship borne radar during TOGA COARE (Tropical Ocean Global Atmosphere Coupled Ocean Atmospheric Response Experiment) and gridded on a 2 km grid depends on the size of the spatial averaging scale. Good agreement is found between the data and predictions from the model over a wide range of averaging length scales.

A novel model incorporating two variability sources for describing motor evoked potentials

PubMed Central

Goetz, Stefan M.; Luber, Bruce; Lisanby, Sarah H.; Peterchev, Angel V.

2014-01-01

Objective Motor evoked potentials (MEPs) play a pivotal role in transcranial magnetic stimulation (TMS), e.g., for determining the motor threshold and probing cortical excitability. Sampled across the range of stimulation strengths, MEPs outline an input–output (IO) curve, which is often used to characterize the corticospinal tract. More detailed understanding of the signal generation and variability of MEPs would provide insight into the underlying physiology and aid correct statistical treatment of MEP data. Methods A novel regression model is tested using measured IO data of twelve subjects. The model splits MEP variability into two independent contributions, acting on both sides of a strong sigmoidal nonlinearity that represents neural recruitment. Traditional sigmoidal regression with a single variability source after the nonlinearity is used for comparison. Results The distribution of MEP amplitudes varied across different stimulation strengths, violating statistical assumptions in traditional regression models. In contrast to the conventional regression model, the dual variability source model better described the IO characteristics including phenomena such as changing distribution spread and skewness along the IO curve. Conclusions MEP variability is best described by two sources that most likely separate variability in the initial excitation process from effects occurring later on. The new model enables more accurate and sensitive estimation of the IO curve characteristics, enhancing its power as a detection tool, and may apply to other brain stimulation modalities. Furthermore, it extracts new information from the IO data concerning the neural variability—information that has previously been treated as noise. PMID:24794287

Lognormal Distribution of Cellular Uptake of Radioactivity: Statistical Analysis of α-Particle Track Autoradiography

PubMed Central

Neti, Prasad V.S.V.; Howell, Roger W.

2010-01-01

Recently, the distribution of radioactivity among a population of cells labeled with 210Po was shown to be well described by a log-normal (LN) distribution function (J Nucl Med. 2006;47:1049–1058) with the aid of autoradiography. To ascertain the influence of Poisson statistics on the interpretation of the autoradiographic data, the present work reports on a detailed statistical analysis of these earlier data. Methods The measured distributions of α-particle tracks per cell were subjected to statistical tests with Poisson, LN, and Poisson-lognormal (P-LN) models. Results The LN distribution function best describes the distribution of radioactivity among cell populations exposed to 0.52 and 3.8 kBq/mL of 210Po-citrate. When cells were exposed to 67 kBq/mL, the P-LN distribution function gave a better fit; however, the underlying activity distribution remained log-normal. Conclusion The present analysis generally provides further support for the use of LN distributions to describe the cellular uptake of radioactivity. Care should be exercised when analyzing autoradiographic data on activity distributions to ensure that Poisson processes do not distort the underlying LN distribution. PMID:18483086

Log Normal Distribution of Cellular Uptake of Radioactivity: Statistical Analysis of Alpha Particle Track Autoradiography

PubMed Central

Neti, Prasad V.S.V.; Howell, Roger W.

2008-01-01

Recently, the distribution of radioactivity among a population of cells labeled with 210Po was shown to be well described by a log normal distribution function (J Nucl Med 47, 6 (2006) 1049-1058) with the aid of an autoradiographic approach. To ascertain the influence of Poisson statistics on the interpretation of the autoradiographic data, the present work reports on a detailed statistical analyses of these data. Methods The measured distributions of alpha particle tracks per cell were subjected to statistical tests with Poisson (P), log normal (LN), and Poisson – log normal (P – LN) models. Results The LN distribution function best describes the distribution of radioactivity among cell populations exposed to 0.52 and 3.8 kBq/mL 210Po-citrate. When cells were exposed to 67 kBq/mL, the P – LN distribution function gave a better fit, however, the underlying activity distribution remained log normal. Conclusions The present analysis generally provides further support for the use of LN distributions to describe the cellular uptake of radioactivity. Care should be exercised when analyzing autoradiographic data on activity distributions to ensure that Poisson processes do not distort the underlying LN distribution. PMID:16741316

A General Model for Testing Mediation and Moderation Effects

PubMed Central

MacKinnon, David P.

2010-01-01

This paper describes methods for testing mediation and moderation effects in a dataset, both together and separately. Investigations of this kind are especially valuable in prevention research to obtain information on the process by which a program achieves its effects and whether the program is effective for subgroups of individuals. A general model that simultaneously estimates mediation and moderation effects is presented, and the utility of combining the effects into a single model is described. Possible effects of interest in the model are explained, as are statistical methods to assess these effects. The methods are further illustrated in a hypothetical prevention program example. PMID:19003535

A new computer code for discrete fracture network modelling

NASA Astrophysics Data System (ADS)

Xu, Chaoshui; Dowd, Peter

2010-03-01

The authors describe a comprehensive software package for two- and three-dimensional stochastic rock fracture simulation using marked point processes. Fracture locations can be modelled by a Poisson, a non-homogeneous, a cluster or a Cox point process; fracture geometries and properties are modelled by their respective probability distributions. Virtual sampling tools such as plane, window and scanline sampling are included in the software together with a comprehensive set of statistical tools including histogram analysis, probability plots, rose diagrams and hemispherical projections. The paper describes in detail the theoretical basis of the implementation and provides a case study in rock fracture modelling to demonstrate the application of the software.

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 1: Theoretical development and application to yearly predictions for selected cities in the United States

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1986-01-01

A rain attenuation prediction model is described for use in calculating satellite communication link availability for any specific location in the world that is characterized by an extended record of rainfall. Such a formalism is necessary for the accurate assessment of such availability predictions in the case of the small user-terminal concept of the Advanced Communication Technology Satellite (ACTS) Project. The model employs the theory of extreme value statistics to generate the necessary statistical rainrate parameters from rain data in the form compiled by the National Weather Service. These location dependent rain statistics are then applied to a rain attenuation model to obtain a yearly prediction of the occurrence of attenuation on any satellite link at that location. The predictions of this model are compared to those of the Crane Two-Component Rain Model and some empirical data and found to be very good. The model is then used to calculate rain attenuation statistics at 59 locations in the United States (including Alaska and Hawaii) for the 20 GHz downlinks and 30 GHz uplinks of the proposed ACTS system. The flexibility of this modeling formalism is such that it allows a complete and unified treatment of the temporal aspects of rain attenuation that leads to the design of an optimum stochastic power control algorithm, the purpose of which is to efficiently counter such rain fades on a satellite link.

Modeling Antimicrobial Activity of Clorox(R) Using an Agar-Diffusion Test: A New Twist On an Old Experiment.

ERIC Educational Resources Information Center

Mitchell, James K.; Carter, William E.

2000-01-01

Describes using a computer statistical software package called Minitab to model the sensitivity of several microbes to the disinfectant NaOCl (Clorox') using the Kirby-Bauer technique. Each group of students collects data from one microbe, conducts regression analyses, then chooses the best-fit model based on the highest r-values obtained.…

A probabilistic mechanical model for prediction of aggregates’ size distribution effect on concrete compressive strength

NASA Astrophysics Data System (ADS)

Miled, Karim; Limam, Oualid; Sab, Karam

2012-06-01

To predict aggregates' size distribution effect on the concrete compressive strength, a probabilistic mechanical model is proposed. Within this model, a Voronoi tessellation of a set of non-overlapping and rigid spherical aggregates is used to describe the concrete microstructure. Moreover, aggregates' diameters are defined as statistical variables and their size distribution function is identified to the experimental sieve curve. Then, an inter-aggregate failure criterion is proposed to describe the compressive-shear crushing of the hardened cement paste when concrete is subjected to uniaxial compression. Using a homogenization approach based on statistical homogenization and on geometrical simplifications, an analytical formula predicting the concrete compressive strength is obtained. This formula highlights the effects of cement paste strength and aggregates' size distribution and volume fraction on the concrete compressive strength. According to the proposed model, increasing the concrete strength for the same cement paste and the same aggregates' volume fraction is obtained by decreasing both aggregates' maximum size and the percentage of coarse aggregates. Finally, the validity of the model has been discussed through a comparison with experimental results (15 concrete compressive strengths ranging between 46 and 106 MPa) taken from literature and showing a good agreement with the model predictions.

Self-affirmation model for football goal distributions

NASA Astrophysics Data System (ADS)

Bittner, E.; Nußbaumer, A.; Janke, W.; Weigel, M.

2007-06-01

Analyzing football score data with statistical techniques, we investigate how the highly co-operative nature of the game is reflected in averaged properties such as the distributions of scored goals for the home and away teams. It turns out that in particular the tails of the distributions are not well described by independent Bernoulli trials, but rather well modeled by negative binomial or generalized extreme value distributions. To understand this behavior from first principles, we suggest to modify the Bernoulli random process to include a simple component of self-affirmation which seems to describe the data surprisingly well and allows to interpret the observed deviation from Gaussian statistics. The phenomenological distributions used before can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments and found the proposed models to be applicable rather universally. In particular, here we compare men's and women's leagues and the separate German leagues during the cold war times and find some remarkable differences.

The Matching Relation and Situation-Specific Bias Modulation in Professional Football Play Selection

PubMed Central

Stilling, Stephanie T; Critchfield, Thomas S

2010-01-01

The utility of a quantitative model depends on the extent to which its fitted parameters vary systematically with environmental events of interest. Professional football statistics were analyzed to determine whether play selection (passing versus rushing plays) could be accounted for with the generalized matching equation, and in particular whether variations in play selection across game situations would manifest as changes in the equation's fitted parameters. Statistically significant changes in bias were found for each of five types of game situations; no systematic changes in sensitivity were observed. Further analyses suggested relationships between play selection bias and both turnover probability (which can be described in terms of punishment) and yards-gained variance (which can be described in terms of variable-magnitude reinforcement schedules). The present investigation provides a useful demonstration of association between face-valid, situation-specific effects in a domain of everyday interest, and a theoretically important term of a quantitative model of behavior. Such associations, we argue, are an essential focus in translational extensions of quantitative models. PMID:21119855

Change and Stability in Educational Stratification.

ERIC Educational Resources Information Center

Mare, Robert D.

1981-01-01

Using statistical models, discusses educational stratification with respect to socioeconomic origins. Describes the effects of social background on grade progression change across cohorts born during the 20th century and the consequences of these changes. (JW)

Dynamical topology and statistical properties of spatiotemporal chaos.

PubMed

Zhuang, Quntao; Gao, Xun; Ouyang, Qi; Wang, Hongli

2012-12-01

For spatiotemporal chaos described by partial differential equations, there are generally locations where the dynamical variable achieves its local extremum or where the time partial derivative of the variable vanishes instantaneously. To a large extent, the location and movement of these topologically special points determine the qualitative structure of the disordered states. We analyze numerically statistical properties of the topologically special points in one-dimensional spatiotemporal chaos. The probability distribution functions for the number of point, the lifespan, and the distance covered during their lifetime are obtained from numerical simulations. Mathematically, we establish a probabilistic model to describe the dynamics of these topologically special points. In spite of the different definitions in different spatiotemporal chaos, the dynamics of these special points can be described in a uniform approach.

A stochastic fractional dynamics model of space-time variability of rain

NASA Astrophysics Data System (ADS)

Kundu, Prasun K.; Travis, James E.

2013-09-01

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, which allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model 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 time scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and on the Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to fit the second moment statistics of radar data at the smaller spatiotemporal scales. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well at these scales without any further adjustment.

Specifying the ISS Plasma Environment

NASA Technical Reports Server (NTRS)

Minow, Joseph I.; Diekmann, Anne; Neergaard, Linda; Bui, Them; Mikatarian, Ronald; Barsamian, Hagop; Koontz, Steven

2002-01-01

Quantifying the spacecraft charging risks and corresponding hazards for the International Space Station (ISS) requires a plasma environment specification describing the natural variability of ionospheric temperature (Te) and density (Ne). Empirical ionospheric specification and forecast models such as the International Reference Ionosphere (IN) model typically only provide estimates of long term (seasonal) mean Te and Ne values for the low Earth orbit environment. Knowledge of the Te and Ne variability as well as the likelihood of extreme deviations from the mean values are required to estimate both the magnitude and frequency of occurrence of potentially hazardous spacecraft charging environments for a given ISS construction stage and flight configuration. This paper describes the statistical analysis of historical ionospheric low Earth orbit plasma measurements used to estimate Ne, Te variability in the ISS flight environment. The statistical variability analysis of Ne and Te enables calculation of the expected frequency of occurrence of any particular values of Ne and Te, especially those that correspond to possibly hazardous spacecraft charging environments. The database used in the original analysis included measurements from the AE-C, AE-D, and DE-2 satellites. Recent work on the database has added additional satellites to the database and ground based incoherent scatter radar observations as well. Deviations of the data values from the IRI estimated Ne, Te parameters for each data point provide a statistical basis for modeling the deviations of the plasma environment from the IRI model output.

Bayesian inference for the spatio-temporal invasion of alien species.

PubMed

Cook, Alex; Marion, Glenn; Butler, Adam; Gibson, Gavin

2007-08-01

In this paper we develop a Bayesian approach to parameter estimation in a stochastic spatio-temporal model of the spread of invasive species across a landscape. To date, statistical techniques, such as logistic and autologistic regression, have outstripped stochastic spatio-temporal models in their ability to handle large numbers of covariates. Here we seek to address this problem by making use of a range of covariates describing the bio-geographical features of the landscape. Relative to regression techniques, stochastic spatio-temporal models are more transparent in their representation of biological processes. They also explicitly model temporal change, and therefore do not require the assumption that the species' distribution (or other spatial pattern) has already reached equilibrium as is often the case with standard statistical approaches. In order to illustrate the use of such techniques we apply them to the analysis of data detailing the spread of an invasive plant, Heracleum mantegazzianum, across Britain in the 20th Century using geo-referenced covariate information describing local temperature, elevation and habitat type. The use of Markov chain Monte Carlo sampling within a Bayesian framework facilitates statistical assessments of differences in the suitability of different habitat classes for H. mantegazzianum, and enables predictions of future spread to account for parametric uncertainty and system variability. Our results show that ignoring such covariate information may lead to biased estimates of key processes and implausible predictions of future distributions.

Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach

ERIC Educational Resources Information Center

Selig, James P.; Preacher, Kristopher J.; Little, Todd D.

2012-01-01

We describe a straightforward, yet novel, approach to examine time-dependent association between variables. The approach relies on a measurement-lag research design in conjunction with statistical interaction models. We base arguments in favor of this approach on the potential for better understanding the associations between variables by…

Demographic Accounting and Model-Building. Education and Development Technical Reports.

ERIC Educational Resources Information Center

Stone, Richard

This report describes and develops a model for coordinating a variety of demographic and social statistics within a single framework. The framework proposed, together with its associated methods of analysis, serves both general and specific functions. The general aim of these functions is to give numerical definition to the pattern of society and…

A Flipped Classroom Model for a Biostatistics Short Course

ERIC Educational Resources Information Center

McLaughlin, Jacqueline E.; Kang, Isabell

2017-01-01

Effective pedagogical strategies are needed to improve statistical literacy within health sciences education. This paper describes the design, implementation, and evaluation of a highly interactive two-week biostatistics short course using the flipped classroom model in the United States. The course was required for all students at the start of a…

Percent Effort vs. Fee-for-Service: A Comparison of Models for Statistical Collaboration

ERIC Educational Resources Information Center

Ittenbach, Richard F.; DeAngelis, Francis W.

2012-01-01

Many statisticians are uncomfortable with discussions about the financial implications of their work. Those who are comfortable may not fully understand the policies and procedures underlying the financial operations of the department. The purpose of the present paper is twofold: first, to describe two predominant models of compensation used by…

AG Channel Measurement and Modeling Results for Over-Water and Hilly Terrain Conditions

NASA Technical Reports Server (NTRS)

Matolak, David W.; Sun, Ruoyu

2015-01-01

This report describes work completed over the past year on our project, entitled "Unmanned Aircraft Systems (UAS) Research: The AG Channel, Robust Waveforms, and Aeronautical Network Simulations." This project is funded under the NASA project "Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS)." In this report we provide the following: an update on project progress; a description of the over-freshwater and hilly terrain initial results on path loss, delay spread, small-scale fading, and correlations; complete path loss models for the over-water AG channels; analysis for obtaining parameter statistics required for development of accurate wideband AG channel models; and analysis of an atypical AG channel in which the aircraft flies out of the ground site antenna main beam. We have modeled the small-scale fading of these channels with Ricean statistics, and have quantified the behavior of the Ricean K-factor. We also provide some results for correlations of signal components, both intra-band and inter-band. An updated literature review, and a summary that also describes future work, are also included.

Discrimination of dynamical system models for biological and chemical processes.

PubMed

Lorenz, Sönke; Diederichs, Elmar; Telgmann, Regina; Schütte, Christof

2007-06-01

In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics.

Analysis of Parasite and Other Skewed Counts

PubMed Central

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

Improving Our Ability to Evaluate Underlying Mechanisms of Behavioral Onset and Other Event Occurrence Outcomes: A Discrete-Time Survival Mediation Model

PubMed Central

Fairchild, Amanda J.; Abara, Winston E.; Gottschall, Amanda C.; Tein, Jenn-Yun; Prinz, Ronald J.

2015-01-01

The purpose of this article is to introduce and describe a statistical model that researchers can use to evaluate underlying mechanisms of behavioral onset and other event occurrence outcomes. Specifically, the article develops a framework for estimating mediation effects with outcomes measured in discrete-time epochs by integrating the statistical mediation model with discrete-time survival analysis. The methodology has the potential to help strengthen health research by targeting prevention and intervention work more effectively as well as by improving our understanding of discretized periods of risk. The model is applied to an existing longitudinal data set to demonstrate its use, and programming code is provided to facilitate its implementation. PMID:24296470

On Fitting Generalized Linear Mixed-effects Models for Binary Responses using Different Statistical Packages

PubMed Central

Zhang, Hui; Lu, Naiji; Feng, Changyong; Thurston, Sally W.; Xia, Yinglin; Tu, Xin M.

2011-01-01

Summary The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice. PMID:21671252

General Blending Models for Data From Mixture Experiments

PubMed Central

Brown, L.; Donev, A. N.; Bissett, A. C.

2015-01-01

We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online. PMID:26681812

Final Report for Dynamic Models for Causal Analysis of Panel Data. Models for Change in Quantitative Variables, Part II Scholastic Models. Part II, Chapter 4.

ERIC Educational Resources Information Center

Hannan, Michael T.

This document is part of a series of chapters described in SO 011 759. Stochastic models for the sociological analysis of change and the change process in quantitative variables are presented. The author lays groundwork for the statistical treatment of simple stochastic differential equations (SDEs) and discusses some of the continuities of…

Statistical Model to Analyze Quantitative Proteomics Data Obtained by 18O/16O Labeling and Linear Ion Trap Mass Spectrometry

PubMed Central

Jorge, Inmaculada; Navarro, Pedro; Martínez-Acedo, Pablo; Núñez, Estefanía; Serrano, Horacio; Alfranca, Arántzazu; Redondo, Juan Miguel; Vázquez, Jesús

2009-01-01

Statistical models for the analysis of protein expression changes by stable isotope labeling are still poorly developed, particularly for data obtained by 16O/18O labeling. Besides large scale test experiments to validate the null hypothesis are lacking. Although the study of mechanisms underlying biological actions promoted by vascular endothelial growth factor (VEGF) on endothelial cells is of considerable interest, quantitative proteomics studies on this subject are scarce and have been performed after exposing cells to the factor for long periods of time. In this work we present the largest quantitative proteomics study to date on the short term effects of VEGF on human umbilical vein endothelial cells by 18O/16O labeling. Current statistical models based on normality and variance homogeneity were found unsuitable to describe the null hypothesis in a large scale test experiment performed on these cells, producing false expression changes. A random effects model was developed including four different sources of variance at the spectrum-fitting, scan, peptide, and protein levels. With the new model the number of outliers at scan and peptide levels was negligible in three large scale experiments, and only one false protein expression change was observed in the test experiment among more than 1000 proteins. The new model allowed the detection of significant protein expression changes upon VEGF stimulation for 4 and 8 h. The consistency of the changes observed at 4 h was confirmed by a replica at a smaller scale and further validated by Western blot analysis of some proteins. Most of the observed changes have not been described previously and are consistent with a pattern of protein expression that dynamically changes over time following the evolution of the angiogenic response. With this statistical model the 18O labeling approach emerges as a very promising and robust alternative to perform quantitative proteomics studies at a depth of several thousand proteins. PMID:19181660

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study.

PubMed

von Stosch, Moritz; Hamelink, Jan-Martijn; Oliveira, Rui

2016-05-01

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.

Analysis of the dependence of extreme rainfalls

NASA Astrophysics Data System (ADS)

Padoan, Simone; Ancey, Christophe; Parlange, Marc

2010-05-01

The aim of spatial analysis is to quantitatively describe the behavior of environmental phenomena such as precipitation levels, wind speed or daily temperatures. A number of generic approaches to spatial modeling have been developed[1], but these are not necessarily ideal for handling extremal aspects given their focus on mean process levels. The areal modelling of the extremes of a natural process observed at points in space is important in environmental statistics; for example, understanding extremal spatial rainfall is crucial in flood protection. In light of recent concerns over climate change, the use of robust mathematical and statistical methods for such analyses has grown in importance. Multivariate extreme value models and the class of maxstable processes [2] have a similar asymptotic motivation to the univariate Generalized Extreme Value (GEV) distribution , but providing a general approach to modeling extreme processes incorporating temporal or spatial dependence. Statistical methods for max-stable processes and data analyses of practical problems are discussed by [3] and [4]. This work illustrates methods to the statistical modelling of spatial extremes and gives examples of their use by means of a real extremal data analysis of Switzerland precipitation levels. [1] Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York. [2] de Haan, L and Ferreria A. (2006). Extreme Value Theory An Introduction. Springer, USA. [3] Padoan, S. A., Ribatet, M and Sisson, S. A. (2009). Likelihood-Based Inference for Max-Stable Processes. Journal of the American Statistical Association, Theory & Methods. In press. [4] Davison, A. C. and Gholamrezaee, M. (2009), Geostatistics of extremes. Journal of the Royal Statistical Society, Series B. To appear.

Evaluating mediation and moderation effects in school psychology: A presentation of methods and review of current practice

PubMed Central

Fairchild, Amanda J.; McQuillin, Samuel D.

2017-01-01

Third variable effects elucidate the relation between two other variables, and can describe why they are related or under what conditions they are related. This article demonstrates methods to analyze two third-variable effects: moderation and mediation. The utility of examining moderation and mediation effects in school psychology is described and current use of the analyses in applied school psychology research is reviewed and evaluated. Proper statistical methods to test the effects are presented, and different effect size measures for the models are provided. Extensions of the basic moderator and mediator models are also described. PMID:20006988

Evaluating mediation and moderation effects in school psychology: a presentation of methods and review of current practice.

PubMed

Fairchild, Amanda J; McQuillin, Samuel D

2010-02-01

Third variable effects elucidate the relation between two other variables, and can describe why they are related or under what conditions they are related. This article demonstrates methods to analyze two third-variable effects: moderation and mediation. The utility of examining moderation and mediation effects in school psychology is described and current use of the analyses in applied school psychology research is reviewed and evaluated. Proper statistical methods to test the effects are presented, and different effect size measures for the models are provided. Extensions of the basic moderator and mediator models are also described.

Statistical description of non-Gaussian samples in the F2 layer of the ionosphere during heliogeophysical disturbances

NASA Astrophysics Data System (ADS)

Sergeenko, N. P.

2017-11-01

An adequate statistical method should be developed in order to predict probabilistically the range of ionospheric parameters. This problem is solved in this paper. The time series of the critical frequency of the layer F2- foF2( t) were subjected to statistical processing. For the obtained samples {δ foF2}, statistical distributions and invariants up to the fourth order are calculated. The analysis shows that the distributions differ from the Gaussian law during the disturbances. At levels of sufficiently small probability distributions, there are arbitrarily large deviations from the model of the normal process. Therefore, it is attempted to describe statistical samples {δ foF2} based on the Poisson model. For the studied samples, the exponential characteristic function is selected under the assumption that time series are a superposition of some deterministic and random processes. Using the Fourier transform, the characteristic function is transformed into a nonholomorphic excessive-asymmetric probability-density function. The statistical distributions of the samples {δ foF2} calculated for the disturbed periods are compared with the obtained model distribution function. According to the Kolmogorov's criterion, the probabilities of the coincidence of a posteriori distributions with the theoretical ones are P 0.7-0.9. The conducted analysis makes it possible to draw a conclusion about the applicability of a model based on the Poisson random process for the statistical description and probabilistic variation estimates during heliogeophysical disturbances of the variations {δ foF2}.

Loop models, modular invariance, and three-dimensional bosonization

NASA Astrophysics Data System (ADS)

Goldman, Hart; Fradkin, Eduardo

2018-05-01

We consider a family of quantum loop models in 2+1 spacetime dimensions with marginally long-ranged and statistical interactions mediated by a U (1 ) gauge field, both purely in 2+1 dimensions and on a surface in a (3+1)-dimensional bulk system. In the absence of fractional spin, these theories have been shown to be self-dual under particle-vortex duality and shifts of the statistical angle of the loops by 2 π , which form a subgroup of the modular group, PSL (2 ,Z ) . We show that careful consideration of fractional spin in these theories completely breaks their statistical periodicity and describe how this occurs, resolving a disagreement with the conformal field theories they appear to approach at criticality. We show explicitly that incorporation of fractional spin leads to loop model dualities which parallel the recent web of (2+1)-dimensional field theory dualities, providing a nontrivial check on its validity.

Statistical mechanics model for the emergence of consensus

NASA Astrophysics Data System (ADS)

Raffaelli, Giacomo; Marsili, Matteo

2005-07-01

The statistical properties of pairwise majority voting over S alternatives are analyzed in an infinite random population. We first compute the probability that the majority is transitive (i.e., that if it prefers A to B to C , then it prefers A to C ) and then study the case of an interacting population. This is described by a constrained multicomponent random field Ising model whose ferromagnetic phase describes the emergence of a strong transitive majority. We derive the phase diagram, which is characterized by a tricritical point and show that, contrary to intuition, it may be more likely for an interacting population to reach consensus on a number S of alternatives when S increases. This effect is due to the constraint imposed by transitivity on voting behavior. Indeed if agents are allowed to express nontransitive votes, the agents’ interaction may decrease considerably the probability of a transitive majority.

Computer Administering of the Psychological Investigations: Set-Relational Representation

NASA Astrophysics Data System (ADS)

Yordzhev, Krasimir

Computer administering of a psychological investigation is the computer representation of the entire procedure of psychological assessments - test construction, test implementation, results evaluation, storage and maintenance of the developed database, its statistical processing, analysis and interpretation. A mathematical description of psychological assessment with the aid of personality tests is discussed in this article. The set theory and the relational algebra are used in this description. A relational model of data, needed to design a computer system for automation of certain psychological assessments is given. Some finite sets and relation on them, which are necessary for creating a personality psychological test, are described. The described model could be used to develop real software for computer administering of any psychological test and there is full automation of the whole process: test construction, test implementation, result evaluation, storage of the developed database, statistical implementation, analysis and interpretation. A software project for computer administering personality psychological tests is suggested.

Complexities and potential pitfalls of clinical study design and data analysis in assisted reproduction.

PubMed

Patounakis, George; Hill, Micah J

2018-06-01

The purpose of the current review is to describe the common pitfalls in design and statistical analysis of reproductive medicine studies. It serves to guide both authors and reviewers toward reducing the incidence of spurious statistical results and erroneous conclusions. The large amount of data gathered in IVF cycles leads to problems with multiplicity, multicollinearity, and over fitting of regression models. Furthermore, the use of the word 'trend' to describe nonsignificant results has increased in recent years. Finally, methods to accurately account for female age in infertility research models are becoming more common and necessary. The pitfalls of study design and analysis reviewed provide a framework for authors and reviewers to approach clinical research in the field of reproductive medicine. By providing a more rigorous approach to study design and analysis, the literature in reproductive medicine will have more reliable conclusions that can stand the test of time.

Fatigue Life Prediction of Fiber-Reinforced Ceramic-Matrix Composites with Different Fiber Preforms at Room and Elevated Temperatures

PubMed Central

Li, Longbiao

2016-01-01

In this paper, the fatigue life of fiber-reinforced ceramic-matrix composites (CMCs) with different fiber preforms, i.e., unidirectional, cross-ply, 2D (two dimensional), 2.5D and 3D CMCs at room and elevated temperatures in air and oxidative environments, has been predicted using the micromechanics approach. An effective coefficient of the fiber volume fraction along the loading direction (ECFL) was introduced to describe the fiber architecture of preforms. The statistical matrix multicracking model and fracture mechanics interface debonding criterion were used to determine the matrix crack spacing and interface debonded length. Under cyclic fatigue loading, the fiber broken fraction was determined by combining the interface wear model and fiber statistical failure model at room temperature, and interface/fiber oxidation model, interface wear model and fiber statistical failure model at elevated temperatures, based on the assumption that the fiber strength is subjected to two-parameter Weibull distribution and the load carried by broken and intact fibers satisfies the Global Load Sharing (GLS) criterion. When the broken fiber fraction approaches the critical value, the composites fatigue fracture. PMID:28773332

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Statistical physics studies of multilayer adsorption isotherm in food materials and pore size distribution

NASA Astrophysics Data System (ADS)

Aouaini, F.; Knani, S.; Ben Yahia, M.; Ben Lamine, A.

2015-08-01

Water sorption isotherms of foodstuffs are very important in different areas of food science engineering such as for design, modeling and optimization of many processes. The equilibrium moisture content is an important parameter in models used to predict changes in the moisture content of a product during storage. A formulation of multilayer model with two energy levels was based on statistical physics and theoretical considerations. Thanks to the grand canonical ensemble in statistical physics. Some physicochemical parameters related to the adsorption process were introduced in the analytical model expression. The data tabulated in literature of water adsorption at different temperatures on: chickpea seeds, lentil seeds, potato and on green peppers were described applying the most popular models applied in food science. We also extend the study to the newest proposed model. It is concluded that among studied models the proposed model seems to be the best for description of data in the whole range of relative humidity. By using our model, we were able to determine the thermodynamic functions. The measurement of desorption isotherms, in particular a gas over a solid porous, allows access to the distribution of pore size PSD.

The microcomputer scientific software series 3: general linear model--analysis of variance.

Treesearch

Harold M. Rauscher

1985-01-01

A BASIC language set of programs, designed for use on microcomputers, is presented. This set of programs will perform the analysis of variance for any statistical model describing either balanced or unbalanced designs. The program computes and displays the degrees of freedom, Type I sum of squares, and the mean square for the overall model, the error, and each factor...

Use of Robust z in Detecting Unstable Items in Item Response Theory Models

ERIC Educational Resources Information Center

Huynh, Huynh; Meyer, Patrick

2010-01-01

The first part of this paper describes the use of the robust z[subscript R] statistic to link test forms using the Rasch (or one-parameter logistic) model. The procedure is then extended to the two-parameter and three-parameter logistic and two-parameter partial credit (2PPC) models. A real set of data was used to illustrate the extension. The…

Statistical power in parallel group point exposure studies with time-to-event outcomes: an empirical comparison of the performance of randomized controlled trials and the inverse probability of treatment weighting (IPTW) approach.

PubMed

Austin, Peter C; Schuster, Tibor; Platt, Robert W

2015-10-15

Estimating statistical power is an important component of the design of both randomized controlled trials (RCTs) and observational studies. Methods for estimating statistical power in RCTs have been well described and can be implemented simply. In observational studies, statistical methods must be used to remove the effects of confounding that can occur due to non-random treatment assignment. Inverse probability of treatment weighting (IPTW) using the propensity score is an attractive method for estimating the effects of treatment using observational data. However, sample size and power calculations have not been adequately described for these methods. We used an extensive series of Monte Carlo simulations to compare the statistical power of an IPTW analysis of an observational study with time-to-event outcomes with that of an analysis of a similarly-structured RCT. We examined the impact of four factors on the statistical power function: number of observed events, prevalence of treatment, the marginal hazard ratio, and the strength of the treatment-selection process. We found that, on average, an IPTW analysis had lower statistical power compared to an analysis of a similarly-structured RCT. The difference in statistical power increased as the magnitude of the treatment-selection model increased. The statistical power of an IPTW analysis tended to be lower than the statistical power of a similarly-structured RCT.

A thermomechanical constitutive model for cemented granular materials with quantifiable internal variables. Part I-Theory

NASA Astrophysics Data System (ADS)

Tengattini, Alessandro; Das, Arghya; Nguyen, Giang D.; Viggiani, Gioacchino; Hall, Stephen A.; Einav, Itai

2014-10-01

This is the first of two papers introducing a novel thermomechanical continuum constitutive model for cemented granular materials. Here, we establish the theoretical foundations of the model, and highlight its novelties. At the limit of no cement, the model is fully consistent with the original Breakage Mechanics model. An essential ingredient of the model is the use of measurable and micro-mechanics based internal variables, describing the evolution of the dominant inelastic processes. This imposes a link between the macroscopic mechanical behavior and the statistically averaged evolution of the microstructure. As a consequence this model requires only a few physically identifiable parameters, including those of the original breakage model and new ones describing the cement: its volume fraction, its critical damage energy and bulk stiffness, and the cohesion.

Probabilities and statistics for backscatter estimates obtained by a scatterometer

NASA Technical Reports Server (NTRS)

Pierson, Willard J., Jr.

1989-01-01

Methods for the recovery of winds near the surface of the ocean from measurements of the normalized radar backscattering cross section must recognize and make use of the statistics (i.e., the sampling variability) of the backscatter measurements. Radar backscatter values from a scatterometer are random variables with expected values given by a model. A model relates backscatter to properties of the waves on the ocean, which are in turn generated by the winds in the atmospheric marine boundary layer. The effective wind speed and direction at a known height for a neutrally stratified atmosphere are the values to be recovered from the model. The probability density function for the backscatter values is a normal probability distribution with the notable feature that the variance is a known function of the expected value. The sources of signal variability, the effects of this variability on the wind speed estimation, and criteria for the acceptance or rejection of models are discussed. A modified maximum likelihood method for estimating wind vectors is described. Ways to make corrections for the kinds of errors found for the Seasat SASS model function are described, and applications to a new scatterometer are given.

A statistical metadata model for clinical trials' data management.

PubMed

Vardaki, Maria; Papageorgiou, Haralambos; Pentaris, Fragkiskos

2009-08-01

We introduce a statistical, process-oriented metadata model to describe the process of medical research data collection, management, results analysis and dissemination. Our approach explicitly provides a structure for pieces of information used in Clinical Study Data Management Systems, enabling a more active role for any associated metadata. Using the object-oriented paradigm, we describe the classes of our model that participate during the design of a clinical trial and the subsequent collection and management of the relevant data. The advantage of our approach is that we focus on presenting the structural inter-relation of these classes when used during datasets manipulation by proposing certain transformations that model the simultaneous processing of both data and metadata. Our solution reduces the possibility of human errors and allows for the tracking of all changes made during datasets lifecycle. The explicit modeling of processing steps improves data quality and assists in the problem of handling data collected in different clinical trials. The case study illustrates the applicability of the proposed framework demonstrating conceptually the simultaneous handling of datasets collected during two randomized clinical studies. Finally, we provide the main considerations for implementing the proposed framework into a modern Metadata-enabled Information System.

Predictive Model for the Design of Zwitterionic Polymer Brushes: A Statistical Design of Experiments Approach.

PubMed

Kumar, Ramya; Lahann, Joerg

2016-07-06

The performance of polymer interfaces in biology is governed by a wide spectrum of interfacial properties. With the ultimate goal of identifying design parameters for stem cell culture coatings, we developed a statistical model that describes the dependence of brush properties on surface-initiated polymerization (SIP) parameters. Employing a design of experiments (DOE) approach, we identified operating boundaries within which four gel architecture regimes can be realized, including a new regime of associated brushes in thin films. Our statistical model can accurately predict the brush thickness and the degree of intermolecular association of poly[{2-(methacryloyloxy) ethyl} dimethyl-(3-sulfopropyl) ammonium hydroxide] (PMEDSAH), a previously reported synthetic substrate for feeder-free and xeno-free culture of human embryonic stem cells. DOE-based multifunctional predictions offer a powerful quantitative framework for designing polymer interfaces. For example, model predictions can be used to decrease the critical thickness at which the wettability transition occurs by simply increasing the catalyst quantity from 1 to 3 mol %.

Are well functioning civil registration and vital statistics systems associated with better health outcomes?

PubMed

Phillips, David E; AbouZahr, Carla; Lopez, Alan D; Mikkelsen, Lene; de Savigny, Don; Lozano, Rafael; Wilmoth, John; Setel, Philip W

2015-10-03

In this Series paper, we examine whether well functioning civil registration and vital statistics (CRVS) systems are associated with improved population health outcomes. We present a conceptual model connecting CRVS to wellbeing, and describe an ecological association between CRVS and health outcomes. The conceptual model posits that the legal identity that civil registration provides to individuals is key to access entitlements and services. Vital statistics produced by CRVS systems provide essential information for public health policy and prevention. These outcomes benefit individuals and societies, including improved health. We use marginal linear models and lag-lead analysis to measure ecological associations between a composite metric of CRVS performance and three health outcomes. Results are consistent with the conceptual model: improved CRVS performance coincides with improved health outcomes worldwide in a temporally consistent manner. Investment to strengthen CRVS systems is not only an important goal for individuals and societies, but also a development imperative that is good for health. Copyright © 2015 Elsevier Ltd. All rights reserved.

On use of the multistage dose-response model for assessing laboratory animal carcinogenicity

PubMed Central

Nitcheva, Daniella; Piegorsch, Walter W.; West, R. Webster

2007-01-01

We explore how well a statistical multistage model describes dose-response patterns in laboratory animal carcinogenicity experiments from a large database of quantal response data. The data are collected from the U.S. EPA’s publicly available IRIS data warehouse and examined statistically to determine how often higher-order values in the multistage predictor yield significant improvements in explanatory power over lower-order values. Our results suggest that the addition of a second-order parameter to the model only improves the fit about 20% of the time, while adding even higher-order terms apparently does not contribute to the fit at all, at least with the study designs we captured in the IRIS database. Also included is an examination of statistical tests for assessing significance of higher-order terms in a multistage dose-response model. It is noted that bootstrap testing methodology appears to offer greater stability for performing the hypothesis tests than a more-common, but possibly unstable, “Wald” test. PMID:17490794

Comments of statistical issue in numerical modeling for underground nuclear test monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicholson, W.L.; Anderson, K.K.

1993-03-01

The Symposium concluded with prepared summaries by four experts in the involved disciplines. These experts made no mention of statistics and/or the statistical content of issues. The first author contributed an extemporaneous statement at the Symposium because there are important issues associated with conducting and evaluating numerical modeling that are familiar to statisticians and often treated successfully by them. This note expands upon these extemporaneous remarks. Statistical ideas may be helpful in resolving some numerical modeling issues. Specifically, we comment first on the role of statistical design/analysis in the quantification process to answer the question ``what do we know aboutmore » the numerical modeling of underground nuclear tests?`` and second on the peculiar nature of uncertainty analysis for situations involving numerical modeling. The simulations described in the workshop, though associated with topic areas, were basically sets of examples. Each simulation was tuned towards agreeing with either empirical evidence or an expert`s opinion of what empirical evidence would be. While the discussions were reasonable, whether the embellishments were correct or a forced fitting of reality is unclear and illustrates that ``simulation is easy.`` We also suggest that these examples of simulation are typical and the questions concerning the legitimacy and the role of knowing the reality are fair, in general, with respect to simulation. The answers will help us understand why ``prediction is difficult.``« less

Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics

USGS Publications Warehouse

Pervez, Md Shahriar; Brown, Jesslyn F.

2010-01-01

Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Categorical data processing for real estate objects valuation using statistical analysis

NASA Astrophysics Data System (ADS)

Parygin, D. S.; Malikov, V. P.; Golubev, A. V.; Sadovnikova, N. P.; Petrova, T. M.; Finogeev, A. G.

2018-05-01

Theoretical and practical approaches to the use of statistical methods for studying various properties of infrastructure objects are analyzed in the paper. Methods of forecasting the value of objects are considered. A method for coding categorical variables describing properties of real estate objects is proposed. The analysis of the results of modeling the price of real estate objects using regression analysis and an algorithm based on a comparative approach is carried out.

Plan Recognition using Statistical Relational Models

DTIC Science & Technology

2014-08-25

arguments. Section 4 describes several variants of MLNs for plan recognition. All MLN mod- els were implemented using Alchemy (Kok et al., 2010), an...For both MLN approaches, we used MC-SAT (Poon and Domingos, 2006) as implemented in the Alchemy system on both Monroe and Linux. Evaluation Metric We...Singla P, Poon H, Lowd D, Wang J, Nath A, Domingos P. The Alchemy System for Statistical Relational AI. Techni- cal Report; Department of Computer Science

Liquid water breakthrough location distances on a gas diffusion layer of polymer electrolyte membrane fuel cells

NASA Astrophysics Data System (ADS)

Yu, Junliang; Froning, Dieter; Reimer, Uwe; Lehnert, Werner

2018-06-01

The lattice Boltzmann method is adopted to simulate the three dimensional dynamic process of liquid water breaking through the gas diffusion layer (GDL) in the polymer electrolyte membrane fuel cell. 22 micro-structures of Toray GDL are built based on a stochastic geometry model. It is found that more than one breakthrough locations are formed randomly on the GDL surface. Breakthrough location distance (BLD) are analyzed statistically in two ways. The distribution is evaluated statistically by the Lilliefors test. It is concluded that the BLD can be described by the normal distribution with certain statistic characteristics. Information of the shortest neighbor breakthrough location distance can be the input modeling setups on the cell-scale simulations in the field of fuel cell simulation.

Modeling the coupled return-spread high frequency dynamics of large tick assets

NASA Astrophysics Data System (ADS)

Curato, Gianbiagio; Lillo, Fabrizio

2015-01-01

Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We present an approach based on the hidden Markov model, also known in econometrics as the Markov switching model, for the dynamics of price changes, where the latent Markov process is described by the transitions between spreads. We then use a finite Markov mixture of logit regressions on past squared price changes to describe temporal dependencies in the dynamics of price changes. The model can thus be seen as a double chain Markov model. We show that the model describes the shape of the price change distribution at different time scales, volatility clustering, and the anomalous decrease of kurtosis. We calibrate our models based on Nasdaq stocks and we show that this model reproduces remarkably well the statistical properties of real data.

Modeling the pharmacokinetics of extended release pharmaceutical systems

NASA Astrophysics Data System (ADS)

di Muria, Michela; Lamberti, Gaetano; Titomanlio, Giuseppe

2009-03-01

The pharmacokinetic (PK) models predict the hematic concentration of drugs after the administration. In compartment modeling, the body is described by a set of interconnected “vessels” or “compartments”; the modeling consisting of transient mass balances. Usually the orally administered drugs were considered as immediately available: this cannot describe the administration of extended-release systems. In this work we added to the traditional compartment models the ability to account for a delay in administration, relating this delay to in vitro data. Firstly, the method was validated, applying the model to the dosage of nicotine by chewing-gum; the model was tuned by in vitro/in vivo data of drugs (divalproex-sodium and diltiazem) with medium-rate release kinetics, then it was applied in describing in vivo evolutions due to the assumption of fast- and slow-release systems. The model reveals itself predictive, the same of a Level A in vitro/in vivo correlation, but being physically based, it is preferable to a purely statistical method.

DESIGN OF EXPOSURE MEASUREMENTS FOR EPIDEMIOLOGIC STUDIES

EPA Science Inventory

This presentation will describe the following items: (1) London daily air pollution and deaths that demonstrate how time series epidemiology can indicate that air pollution caused death; (2) Sophisticated statistical models required to establish this relationship for lower pollut...

Kalman filter to update forest cover estimates

Treesearch

Raymond L. Czaplewski

1990-01-01

The Kalman filter is a statistical estimator that combines a time-series of independent estimates, using a prediction model that describes expected changes in the state of a system over time. An expensive inventory can be updated using model predictions that are adjusted with more recent, but less expensive and precise, monitoring data. The concepts of the Kalman...

Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications

ERIC Educational Resources Information Center

Pabon, Peter; Ternstrom, Sten; Lamarche, Anick

2011-01-01

Purpose: To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. Method: A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the…

Conformal anomaly of some 2-d Z (n) models

NASA Astrophysics Data System (ADS)

William, Peter

1991-01-01

We describe a numerical calculation of the conformal anomaly in the case of some two-dimensional statistical models undergoing a second-order phase transition, utilizing a recently developed method to compute the partition function exactly. This computation is carried out on a massively parallel CM2 machine, using the finite size scaling behaviour of the free energy.

Quantifying and Testing Indirect Effects in Simple Mediation Models when the Constituent Paths Are Nonlinear

ERIC Educational Resources Information Center

Hayes, Andrew F.; Preacher, Kristopher J.

2010-01-01

Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) for estimating indirect effects in models of…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marekova, Elisaveta

Series of relatively large earthquakes in different regions of the Earth are studied. The regions chooses are of a high seismic activity and has a good contemporary network for recording of the seismic events along them. The main purpose of this investigation is the attempt to describe analytically the seismic process in the space and time. We are considering the statistical distributions the distances and the times between consecutive earthquakes (so called pair analysis). Studies conducted on approximating the statistical distribution of the parameters of consecutive seismic events indicate the existence of characteristic functions that describe them best. Such amore » mathematical description allows the distributions of the examined parameters to be compared to other model distributions.« less

A Conway-Maxwell-Poisson (CMP) model to address data dispersion on positron emission tomography.

PubMed

Santarelli, Maria Filomena; Della Latta, Daniele; Scipioni, Michele; Positano, Vincenzo; Landini, Luigi

2016-10-01

Positron emission tomography (PET) in medicine exploits the properties of positron-emitting unstable nuclei. The pairs of γ- rays emitted after annihilation are revealed by coincidence detectors and stored as projections in a sinogram. It is well known that radioactive decay follows a Poisson distribution; however, deviation from Poisson statistics occurs on PET projection data prior to reconstruction due to physical effects, measurement errors, correction of deadtime, scatter, and random coincidences. A model that describes the statistical behavior of measured and corrected PET data can aid in understanding the statistical nature of the data: it is a prerequisite to develop efficient reconstruction and processing methods and to reduce noise. The deviation from Poisson statistics in PET data could be described by the Conway-Maxwell-Poisson (CMP) distribution model, which is characterized by the centring parameter λ and the dispersion parameter ν, the latter quantifying the deviation from a Poisson distribution model. In particular, the parameter ν allows quantifying over-dispersion (ν<1) or under-dispersion (ν>1) of data. A simple and efficient method for λ and ν parameters estimation is introduced and assessed using Monte Carlo simulation for a wide range of activity values. The application of the method to simulated and experimental PET phantom data demonstrated that the CMP distribution parameters could detect deviation from the Poisson distribution both in raw and corrected PET data. It may be usefully implemented in image reconstruction algorithms and quantitative PET data analysis, especially in low counting emission data, as in dynamic PET data, where the method demonstrated the best accuracy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning physical descriptors for materials science by compressed sensing

NASA Astrophysics Data System (ADS)

Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre; Ouyang, Runhai; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias

2017-02-01

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.

An Introduction to Kristof's Theorem for Solving Least-Square Optimization Problems Without Calculus.

PubMed

Waller, Niels

2018-01-01

Kristof's Theorem (Kristof, 1970 ) describes a matrix trace inequality that can be used to solve a wide-class of least-square optimization problems without calculus. Considering its generality, it is surprising that Kristof's Theorem is rarely used in statistics and psychometric applications. The underutilization of this method likely stems, in part, from the mathematical complexity of Kristof's ( 1964 , 1970 ) writings. In this article, I describe the underlying logic of Kristof's Theorem in simple terms by reviewing four key mathematical ideas that are used in the theorem's proof. I then show how Kristof's Theorem can be used to provide novel derivations to two cognate models from statistics and psychometrics. This tutorial includes a glossary of technical terms and an online supplement with R (R Core Team, 2017 ) code to perform the calculations described in the text.

Techniques in teaching statistics : linking research production and research use.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martinez-Moyano, I .; Smith, A.; Univ. of Massachusetts at Boston)

In the spirit of closing the 'research-practice gap,' the authors extend evidence-based principles to statistics instruction in social science graduate education. The authors employ a Delphi method to survey experienced statistics instructors to identify teaching techniques to overcome the challenges inherent in teaching statistics to students enrolled in practitioner-oriented master's degree programs. Among the teaching techniques identi?ed as essential are using real-life examples, requiring data collection exercises, and emphasizing interpretation rather than results. Building on existing research, preliminary interviews, and the ?ndings from the study, the authors develop a model describing antecedents to the strength of the link between researchmore » and practice.« less

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

A Monte Carlo study of Weibull reliability analysis for space shuttle main engine components

NASA Technical Reports Server (NTRS)

Abernethy, K.

1986-01-01

The incorporation of a number of additional capabilities into an existing Weibull analysis computer program and the results of Monte Carlo computer simulation study to evaluate the usefulness of the Weibull methods using samples with a very small number of failures and extensive censoring are discussed. Since the censoring mechanism inherent in the Space Shuttle Main Engine (SSME) data is hard to analyze, it was decided to use a random censoring model, generating censoring times from a uniform probability distribution. Some of the statistical techniques and computer programs that are used in the SSME Weibull analysis are described. The methods documented in were supplemented by adding computer calculations of approximate (using iteractive methods) confidence intervals for several parameters of interest. These calculations are based on a likelihood ratio statistic which is asymptotically a chisquared statistic with one degree of freedom. The assumptions built into the computer simulations are described. The simulation program and the techniques used in it are described there also. Simulation results are tabulated for various combinations of Weibull shape parameters and the numbers of failures in the samples.

A systematic review of Bayesian articles in psychology: The last 25 years.

PubMed

van de Schoot, Rens; Winter, Sonja D; Ryan, Oisín; Zondervan-Zwijnenburg, Mariëlle; Depaoli, Sarah

2017-06-01

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide "big-picture" recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A statistical shape model of the human second cervical vertebra.

PubMed

Clogenson, Marine; Duff, John M; Luethi, Marcel; Levivier, Marc; Meuli, Reto; Baur, Charles; Henein, Simon

2015-07-01

Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Detector noise statistics in the non-linear regime

NASA Technical Reports Server (NTRS)

Shopbell, P. L.; Bland-Hawthorn, J.

1992-01-01

The statistical behavior of an idealized linear detector in the presence of threshold and saturation levels is examined. It is assumed that the noise is governed by the statistical fluctuations in the number of photons emitted by the source during an exposure. Since physical detectors cannot have infinite dynamic range, our model illustrates that all devices have non-linear regimes, particularly at high count rates. The primary effect is a decrease in the statistical variance about the mean signal due to a portion of the expected noise distribution being removed via clipping. Higher order statistical moments are also examined, in particular, skewness and kurtosis. In principle, the expected distortion in the detector noise characteristics can be calibrated using flatfield observations with count rates matched to the observations. For this purpose, some basic statistical methods that utilize Fourier analysis techniques are described.

On fitting generalized linear mixed-effects models for binary responses using different statistical packages.

PubMed

Zhang, Hui; Lu, Naiji; Feng, Changyong; Thurston, Sally W; Xia, Yinglin; Zhu, Liang; Tu, Xin M

2011-09-10

The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Syndromic surveillance models using Web data: the case of scarlet fever in the UK.

PubMed

Samaras, Loukas; García-Barriocanal, Elena; Sicilia, Miguel-Angel

2012-03-01

Recent research has shown the potential of Web queries as a source for syndromic surveillance, and existing studies show that these queries can be used as a basis for estimation and prediction of the development of a syndromic disease, such as influenza, using log linear (logit) statistical models. Two alternative models are applied to the relationship between cases and Web queries in this paper. We examine the applicability of using statistical methods to relate search engine queries with scarlet fever cases in the UK, taking advantage of tools to acquire the appropriate data from Google, and using an alternative statistical method based on gamma distributions. The results show that using logit models, the Pearson correlation factor between Web queries and the data obtained from the official agencies must be over 0.90, otherwise the prediction of the peak and the spread of the distributions gives significant deviations. In this paper, we describe the gamma distribution model and show that we can obtain better results in all cases using gamma transformations, and especially in those with a smaller correlation factor.

Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains.

PubMed

Tataru, Paula; Hobolth, Asger

2011-12-05

Continuous time Markov chains (CTMCs) is a widely used model for describing the evolution of DNA sequences on the nucleotide, amino acid or codon level. The sufficient statistics for CTMCs are the time spent in a state and the number of changes between any two states. In applications past evolutionary events (exact times and types of changes) are unaccessible and the past must be inferred from DNA sequence data observed in the present. We describe and implement three algorithms for computing linear combinations of expected values of the sufficient statistics, conditioned on the end-points of the chain, and compare their performance with respect to accuracy and running time. The first algorithm is based on an eigenvalue decomposition of the rate matrix (EVD), the second on uniformization (UNI), and the third on integrals of matrix exponentials (EXPM). The implementation in R of the algorithms is available at http://www.birc.au.dk/~paula/. We use two different models to analyze the accuracy and eight experiments to investigate the speed of the three algorithms. We find that they have similar accuracy and that EXPM is the slowest method. Furthermore we find that UNI is usually faster than EVD.

Heavy tailed bacterial motor switching statistics define macroscopic transport properties during upstream contamination by E. coli

NASA Astrophysics Data System (ADS)

Figueroa-Morales, N.; Rivera, A.; Altshuler, E.; Darnige, T.; Douarche, C.; Soto, R.; Lindner, A.; Clément, E.

The motility of E. Coli bacteria is described as a run and tumble process. Changes of direction correspond to a switch in the flagellar motor rotation. The run time distribution is described as an exponential decay of characteristic time close to 1s. Remarkably, it has been demonstrated that the generic response for the distribution of run times is not exponential, but a heavy tailed power law decay, which is at odds with the motility findings. We investigate the consequences of the motor statistics in the macroscopic bacterial transport. During upstream contamination processes in very confined channels, we have identified very long contamination tongues. Using a stochastic model considering bacterial dwelling times on the surfaces related to the run times, we are able to reproduce qualitatively and quantitatively the evolution of the contamination profiles when considering the power law run time distribution. However, the model fails to reproduce the qualitative dynamics when the classical exponential run and tumble distribution is considered. Moreover, we have corroborated the existence of a power law run time distribution by means of 3D Lagrangian tracking. We then argue that the macroscopic transport of bacteria is essentially determined by the motor rotation statistics.

Computer-Based Model Calibration and Uncertainty Analysis: Terms and Concepts

DTIC Science & Technology

2015-07-01

uncertainty analyses throughout the lifecycle of planning, designing, and operating of Civil Works flood risk management projects as described in...value 95% of the time. In the frequentist approach to PE, model parameters area regarded as having true values, and their estimate is based on the...in catchment models. 1. Evaluating parameter uncertainty. Water Resources Research 19(5):1151–1172. Lee, P. M. 2012. Bayesian statistics: An

Speckle noise in satellite based lidar systems

NASA Technical Reports Server (NTRS)

Gardner, C. S.

1977-01-01

The lidar system model was described, and the statistics of the signal and noise at the receiver output were derived. Scattering media effects were discussed along with polarization and atmospheric turbulence. The major equations were summarized and evaluated for some typical parameters.

The Use of Programmable Calculators in the Teaching of Economics, Part II

ERIC Educational Resources Information Center

Addis, G. H.

1978-01-01

Describes the use of programmable calculators to perform classroom controlled experiments on economic models. The complete program for exploring the dynamics of the Harrod-Domar equation is given. Some difficulties encountered and statistical uses are mentioned. (BC)

Revised Planning Methodology For Signalized Intersections And Operational Analysis Of Exclusive Left-Turn Lanes, Part-II: Models And Procedures (Final Report)

DOT National Transportation Integrated Search

1996-04-01

THIS REPORT ALSO DESCRIBES THE PROCEDURES FOR DIRECT ESTIMATION OF INTERSECTION CAPACITY WITH SIMULATION, INCLUDING A SET OF RIGOROUS STATISTICAL TESTS FOR SIMULATION PARAMETER CALIBRATION FROM FIELD DATA.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weaver, Brian Phillip

The purpose of this document is to describe the statistical modeling effort for gas concentrations in WIPP storage containers. The concentration (in ppm) of CO 2 in the headspace volume of standard waste box (SWB) 68685 is shown. A Bayesian approach and an adaptive Metropolis-Hastings algorithm were used.

Study of Automobile Market Dynamics : Volume 2. Analysis.

DOT National Transportation Integrated Search

1977-08-01

Volume II describes the work in providing statistical inputs to a computer model by examining the effects of various options on the number of automobiles sold; the distribution of sales among small, medium and large cars; the distribution between aut...

Football fever: goal distributions and non-Gaussian statistics

NASA Astrophysics Data System (ADS)

Bittner, E.; Nußbaumer, A.; Janke, W.; Weigel, M.

2009-02-01

Analyzing football score data with statistical techniques, we investigate how the not purely random, but highly co-operative nature of the game is reflected in averaged properties such as the probability distributions of scored goals for the home and away teams. As it turns out, especially the tails of the distributions are not well described by the Poissonian or binomial model resulting from the assumption of uncorrelated random events. Instead, a good effective description of the data is provided by less basic distributions such as the negative binomial one or the probability densities of extreme value statistics. To understand this behavior from a microscopical point of view, however, no waiting time problem or extremal process need be invoked. Instead, modifying the Bernoulli random process underlying the Poissonian model to include a simple component of self-affirmation seems to describe the data surprisingly well and allows to understand the observed deviation from Gaussian statistics. The phenomenological distributions used before can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments, including data from all past tournaments of the “FIFA World Cup” series, and found the proposed models to be applicable rather universally. In particular, here we analyze the results of the German women’s premier football league and consider the two separate German men’s premier leagues in the East and West during the cold war times as well as the unified league after 1990 to see how scoring in football and the component of self-affirmation depend on cultural and political circumstances.

Introductory life science mathematics and quantitative neuroscience courses.

PubMed

Duffus, Dwight; Olifer, Andrei

2010-01-01

We describe two sets of courses designed to enhance the mathematical, statistical, and computational training of life science undergraduates at Emory College. The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics. The second is an upper-division course in computational neuroscience. We provide a description of each course, detailed syllabi, examples of content, and a brief discussion of the main issues encountered in developing and offering the courses.

Rain rate duration statistics derived from the Mid-Atlantic coast rain gauge network

NASA Technical Reports Server (NTRS)

Goldhirsh, Julius

1993-01-01

A rain gauge network comprised of 10 tipping bucket rain gauges located in the Mid-Atlantic coast of the United States has been in continuous operation since June 1, 1986. Rain rate distributions and estimated slant path fade distributions at 20 GHz and 30 GHz covering the first five year period were derived from the gauge network measurements, and these results were described by Goldhirsh. In this effort, rain rate time duration statistics are presented. The rain duration statistics are of interest for better understanding the physical nature of precipitation and to present a data base which may be used by modelers to convert to slant path fade duration statistics. Such statistics are important for better assessing optimal coding procedures over defined bandwidths.

Filter Tuning Using the Chi-Squared Statistic

NASA Technical Reports Server (NTRS)

Lilly-Salkowski, Tyler B.

2017-01-01

This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The goal of the process is to characterize the filter performance in the metric of covariance realism. The Chi-squared statistic is the value calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance. The process of tuning an Extended Kalman Filter (EKF) for Aqua and Aura support is described, including examination of the measurement errors of available observation types, and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-squared statistic, calculated from EKF solutions, are assessed.

Treatment effects model for assessing disease management: measuring outcomes and strengthening program management.

PubMed

Wendel, Jeanne; Dumitras, Diana

2005-06-01

This paper describes an analytical methodology for obtaining statistically unbiased outcomes estimates for programs in which participation decisions may be correlated with variables that impact outcomes. This methodology is particularly useful for intraorganizational program evaluations conducted for business purposes. In this situation, data is likely to be available for a population of managed care members who are eligible to participate in a disease management (DM) program, with some electing to participate while others eschew the opportunity. The most pragmatic analytical strategy for in-house evaluation of such programs is likely to be the pre-intervention/post-intervention design in which the control group consists of people who were invited to participate in the DM program, but declined the invitation. Regression estimates of program impacts may be statistically biased if factors that impact participation decisions are correlated with outcomes measures. This paper describes an econometric procedure, the Treatment Effects model, developed to produce statistically unbiased estimates of program impacts in this type of situation. Two equations are estimated to (a) estimate the impacts of patient characteristics on decisions to participate in the program, and then (b) use this information to produce a statistically unbiased estimate of the impact of program participation on outcomes. This methodology is well-established in economics and econometrics, but has not been widely applied in the DM outcomes measurement literature; hence, this paper focuses on one illustrative application.

AGR-1 Thermocouple Data Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeff Einerson

2012-05-01

This report documents an effort to analyze measured and simulated data obtained in the Advanced Gas Reactor (AGR) fuel irradiation test program conducted in the INL's Advanced Test Reactor (ATR) to support the Next Generation Nuclear Plant (NGNP) R&D program. The work follows up on a previous study (Pham and Einerson, 2010), in which statistical analysis methods were applied for AGR-1 thermocouple data qualification. The present work exercises the idea that, while recognizing uncertainties inherent in physics and thermal simulations of the AGR-1 test, results of the numerical simulations can be used in combination with the statistical analysis methods tomore » further improve qualification of measured data. Additionally, the combined analysis of measured and simulation data can generate insights about simulation model uncertainty that can be useful for model improvement. This report also describes an experimental control procedure to maintain fuel target temperature in the future AGR tests using regression relationships that include simulation results. The report is organized into four chapters. Chapter 1 introduces the AGR Fuel Development and Qualification program, AGR-1 test configuration and test procedure, overview of AGR-1 measured data, and overview of physics and thermal simulation, including modeling assumptions and uncertainties. A brief summary of statistical analysis methods developed in (Pham and Einerson 2010) for AGR-1 measured data qualification within NGNP Data Management and Analysis System (NDMAS) is also included for completeness. Chapters 2-3 describe and discuss cases, in which the combined use of experimental and simulation data is realized. A set of issues associated with measurement and modeling uncertainties resulted from the combined analysis are identified. This includes demonstration that such a combined analysis led to important insights for reducing uncertainty in presentation of AGR-1 measured data (Chapter 2) and interpretation of simulation results (Chapter 3). The statistics-based simulation-aided experimental control procedure described for the future AGR tests is developed and demonstrated in Chapter 4. The procedure for controlling the target fuel temperature (capsule peak or average) is based on regression functions of thermocouple readings and other relevant parameters and accounting for possible changes in both physical and thermal conditions and in instrument performance.« less

Modeling epidemics on adaptively evolving networks: A data-mining perspective.

PubMed

Kattis, Assimakis A; Holiday, Alexander; Stoica, Ana-Andreea; Kevrekidis, Ioannis G

2016-01-01

The exploration of epidemic dynamics on dynamically evolving ("adaptive") networks poses nontrivial challenges to the modeler, such as the determination of a small number of informative statistics of the detailed network state (that is, a few "good observables") that usefully summarize the overall (macroscopic, systems-level) behavior. Obtaining reduced, small size accurate models in terms of these few statistical observables--that is, trying to coarse-grain the full network epidemic model to a small but useful macroscopic one--is even more daunting. Here we describe a data-based approach to solving the first challenge: the detection of a few informative collective observables of the detailed epidemic dynamics. This is accomplished through Diffusion Maps (DMAPS), a recently developed data-mining technique. We illustrate the approach through simulations of a simple mathematical model of epidemics on a network: a model known to exhibit complex temporal dynamics. We discuss potential extensions of the approach, as well as possible shortcomings.

Specification of ISS Plasma Environment Variability

NASA Technical Reports Server (NTRS)

Minow, Joseph I.; Neergaard, Linda F.; Bui, Them H.; Mikatarian, Ronald R.; Barsamian, H.; Koontz, Steven L.

2004-01-01

Quantifying spacecraft charging risks and associated hazards for the International Space Station (ISS) requires a plasma environment specification for the natural variability of ionospheric temperature (Te) and density (Ne). Empirical ionospheric specification and forecast models such as the International Reference Ionosphere (IRI) model typically only provide long term (seasonal) mean Te and Ne values for the low Earth orbit environment. This paper describes a statistical analysis of historical ionospheric low Earth orbit plasma measurements from the AE-C, AE-D, and DE-2 satellites used to derive a model of deviations of observed data values from IRI-2001 estimates of Ne, Te parameters for each data point to provide a statistical basis for modeling the deviations of the plasma environment from the IRI model output. Application of the deviation model with the IRI-2001 output yields a method for estimating extreme environments for the ISS spacecraft charging analysis.

Statistical damage constitutive model for rocks subjected to cyclic stress and cyclic temperature

NASA Astrophysics Data System (ADS)

Zhou, Shu-Wei; Xia, Cai-Chu; Zhao, Hai-Bin; Mei, Song-Hua; Zhou, Yu

2017-10-01

A constitutive model of rocks subjected to cyclic stress-temperature was proposed. Based on statistical damage theory, the damage constitutive model with Weibull distribution was extended. Influence of model parameters on the stress-strain curve for rock reloading after stress-temperature cycling was then discussed. The proposed model was initially validated by rock tests for cyclic stress-temperature and only cyclic stress. Finally, the total damage evolution induced by stress-temperature cycling and reloading after cycling was explored and discussed. The proposed constitutive model is reasonable and applicable, describing well the stress-strain relationship during stress-temperature cycles and providing a good fit to the test results. Elastic modulus in the reference state and the damage induced by cycling affect the shape of reloading stress-strain curve. Total damage induced by cycling and reloading after cycling exhibits three stages: initial slow increase, mid-term accelerated increase, and final slow increase.

QSAR Study of p56lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

PubMed Central

Fassihi, Afshin; Sabet, Razieh

2008-01-01

Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously. PMID:19325836

Identified state-space prediction model for aero-optical wavefronts

NASA Astrophysics Data System (ADS)

Faghihi, Azin; Tesch, Jonathan; Gibson, Steve

2013-07-01

A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero-optical data.

Self-similarity analysis of eubacteria genome based on weighted graph.

PubMed

Qi, Zhao-Hui; Li, Ling; Zhang, Zhi-Meng; Qi, Xiao-Qin

2011-07-07

We introduce a weighted graph model to investigate the self-similarity characteristics of eubacteria genomes. The regular treating in similarity comparison about genome is to discover the evolution distance among different genomes. Few people focus their attention on the overall statistical characteristics of each gene compared with other genes in the same genome. In our model, each genome is attributed to a weighted graph, whose topology describes the similarity relationship among genes in the same genome. Based on the related weighted graph theory, we extract some quantified statistical variables from the topology, and give the distribution of some variables derived from the largest social structure in the topology. The 23 eubacteria recently studied by Sorimachi and Okayasu are markedly classified into two different groups by their double logarithmic point-plots describing the similarity relationship among genes of the largest social structure in genome. The results show that the proposed model may provide us with some new sights to understand the structures and evolution patterns determined from the complete genomes. Copyright © 2011 Elsevier Ltd. All rights reserved.

Water quality modeling in the systems impact assessment model for the Klamath River basin - Keno, Oregon to Seiad Valley, California

USGS Publications Warehouse

Hanna, R. Blair; Campbell, Sharon G.

2000-01-01

This report describes the water quality model developed for the Klamath River System Impact Assessment Model (SIAM). The Klamath River SIAM is a decision support system developed by the authors and other US Geological Survey (USGS), Midcontinent Ecological Science Center staff to study the effects of basin-wide water management decisions on anadromous fish in the Klamath River. The Army Corps of Engineersa?? HEC5Q water quality modeling software was used to simulate water temperature, dissolved oxygen and conductivity in 100 miles of the Klamath River Basin in Oregon and California. The water quality model simulated three reservoirs and the mainstem Klamath River influenced by the Shasta and Scott River tributaries. Model development, calibration and two validation exercises are described as well as the integration of the water quality model into the SIAM decision support system software. Within SIAM, data are exchanged between the water quantity model (MODSIM), the water quality model (HEC5Q), the salmon population model (SALMOD) and methods for evaluating ecosystem health. The overall predictive ability of the water quality model is described in the context of calibration and validation error statistics. Applications of SIAM and the water quality model are described.

Stochastic Lanchester-type Combat Models I.

DTIC Science & Technology

1979-10-01

necessarily hold when the attrition rates become non- linear in b and/or r. 13 iL 4. OTHER COMBAT MODELS In this section we briefly describe how other...AD-A092 898 FLORIDA STATE UNIV TALLAHASSEE DEPT OF STATISTICS F/6 12/2 STOCHASTIC LANCHESTER-TYPE COMBAT MODELS I.(U) OCT 79 L BILLARD N62271-79-M...COMBAT MODELS I by L. BILLARD October 1979 Approved for public release; distribution unlimited. Prepared for: Naval Postgraduate School Monterey, CA 93940

Satellite temperature monitoring and prediction system

NASA Technical Reports Server (NTRS)

Barnett, U. R.; Martsolf, J. D.; Crosby, F. L.

1980-01-01

The paper describes the Florida Satellite Freeze Forecast System (SFFS) in its current state. All data collection options have been demonstrated, and data collected over a three year period have been stored for future analysis. Presently, specific minimum temperature forecasts are issued routinely from November through March. The procedures for issuing these forecast are discussed. The automated data acquisition and processing system is described, and the physical and statistical models employed are examined.

Ozone data and mission sampling analysis

NASA Technical Reports Server (NTRS)

Robbins, J. L.

1980-01-01

A methodology was developed to analyze discrete data obtained from the global distribution of ozone. Statistical analysis techniques were applied to describe the distribution of data variance in terms of empirical orthogonal functions and components of spherical harmonic models. The effects of uneven data distribution and missing data were considered. Data fill based on the autocorrelation structure of the data is described. Computer coding of the analysis techniques is included.

Work domain constraints for modelling surgical performance.

PubMed

Morineau, Thierry; Riffaud, Laurent; Morandi, Xavier; Villain, Jonathan; Jannin, Pierre

2015-10-01

Three main approaches can be identified for modelling surgical performance: a competency-based approach, a task-based approach, both largely explored in the literature, and a less known work domain-based approach. The work domain-based approach first describes the work domain properties that constrain the agent's actions and shape the performance. This paper presents a work domain-based approach for modelling performance during cervical spine surgery, based on the idea that anatomical structures delineate the surgical performance. This model was evaluated through an analysis of junior and senior surgeons' actions. Twenty-four cervical spine surgeries performed by two junior and two senior surgeons were recorded in real time by an expert surgeon. According to a work domain-based model describing an optimal progression through anatomical structures, the degree of adjustment of each surgical procedure to a statistical polynomial function was assessed. Each surgical procedure showed a significant suitability with the model and regression coefficient values around 0.9. However, the surgeries performed by senior surgeons fitted this model significantly better than those performed by junior surgeons. Analysis of the relative frequencies of actions on anatomical structures showed that some specific anatomical structures discriminate senior from junior performances. The work domain-based modelling approach can provide an overall statistical indicator of surgical performance, but in particular, it can highlight specific points of interest among anatomical structures that the surgeons dwelled on according to their level of expertise.

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

DTIC Science & Technology

2005-08-04

describe a four-band magnetic resonance image (MRI) consisting of 23,712 pixels of a brain with a tumor 2. Because of the size of the dataset, it is not...the Royal Statistical Society, Series B 56, 363–375. Figueiredo, M. A. T. and A. K. Jain (2002). Unsupervised learning of finite mixture models. IEEE...20 5.4 Brain MRI

POOLMS: A computer program for fitting and model selection for two level factorial replication-free experiments

NASA Technical Reports Server (NTRS)

Amling, G. E.; Holms, A. G.

1973-01-01

A computer program is described that performs a statistical multiple-decision procedure called chain pooling. It uses a number of mean squares assigned to error variance that is conditioned on the relative magnitudes of the mean squares. The model selection is done according to user-specified levels of type 1 or type 2 error probabilities.

Integration of Advanced Statistical Analysis Tools and Geophysical Modeling

DTIC Science & Technology

2012-08-01

Carin Duke University Douglas Oldenburg University of British Columbia Stephen Billings Leonard Pasion Laurens Beran Sky Research...data processing for UXO discrimination is the time (or frequency) dependent dipole model (Bell and Barrow (2001), Pasion and Oldenburg (2001), Zhang...described by a bimodal distribution (i.e. two Gaussians, see Pasion (2007)). Data features are nonetheless useful when data quality is not sufficient

"Role Models Are Real People": Speakers and Field Trips for Chicago's American Indian Elementary School Children.

ERIC Educational Resources Information Center

Hill, Lola L.

This two-part document describes the background and development of "Role Models Are Real People," a speakers' program for at-risk American Indian students, grades 6-8, in Chicago. The first part of the document includes the program proposal, outlining dropout statistics and other data showing reason for concern about American Indian…

Accuracy and Variability of Item Parameter Estimates from Marginal Maximum a Posteriori Estimation and Bayesian Inference via Gibbs Samplers

ERIC Educational Resources Information Center

Wu, Yi-Fang

2015-01-01

Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and…

A model for two-dimensional bursty turbulence in magnetized plasmas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Servidio, Sergio; Primavera, Leonardo; Carbone, Vincenzo

2008-01-15

The nonlinear dynamics of two-dimensional electrostatic interchange modes in a magnetized plasma is investigated through a simple model that replaces the instability mechanism due to magnetic field curvature by an external source of vorticity and mass. Simulations in a cylindrical domain, with a spatially localized and randomized source at the center of the domain, reveal the eruption of mushroom-shaped bursts that propagate radially and are absorbed by the boundaries. Burst sizes and the interburst waiting times exhibit power-law statistics, which indicates long-range interburst correlations, similar to what has been found in sandpile models for avalanching systems. It is shown frommore » the simulations that the dynamics can be characterized by a Yaglom relation for the third-order mixed moment involving the particle number density as a passive scalar and the ExB drift velocity, and hence that the burst phenomenology can be described within the framework of turbulence theory. Statistical features are qualitatively in agreement with experiments of intermittent transport at the edge of plasma devices, and suggest that essential features such as transport can be described by this simple model of bursty turbulence.« less

Halo models of HI selected galaxies

NASA Astrophysics Data System (ADS)

Paul, Niladri; Choudhury, Tirthankar Roy; Paranjape, Aseem

2018-06-01

Modelling the distribution of neutral hydrogen (HI) in dark matter halos is important for studying galaxy evolution in the cosmological context. We use a novel approach to infer the HI-dark matter connection at the massive end (m_H{I} > 10^{9.8} M_{⊙}) from radio HI emission surveys, using optical properties of low-redshift galaxies as an intermediary. In particular, we use a previously calibrated optical HOD describing the luminosity- and colour-dependent clustering of SDSS galaxies and describe the HI content using a statistical scaling relation between the optical properties and HI mass. This allows us to compute the abundance and clustering properties of HI-selected galaxies and compare with data from the ALFALFA survey. We apply an MCMC-based statistical analysis to constrain the free parameters related to the scaling relation. The resulting best-fit scaling relation identifies massive HI galaxies primarily with optically faint blue centrals, consistent with expectations from galaxy formation models. We compare the Hi-stellar mass relation predicted by our model with independent observations from matched Hi-optical galaxy samples, finding reasonable agreement. As a further application, we make some preliminary forecasts for future observations of HI and optical galaxies in the expected overlap volume of SKA and Euclid/LSST.

Low order models for uncertainty quantification in acoustic propagation problems

NASA Astrophysics Data System (ADS)

Millet, Christophe

2016-11-01

Long-range sound propagation problems are characterized by both a large number of length scales and a large number of normal modes. In the atmosphere, these modes are confined within waveguides causing the sound to propagate through multiple paths to the receiver. For uncertain atmospheres, the modes are described as random variables. Concise mathematical models and analysis reveal fundamental limitations in classical projection techniques due to different manifestations of the fact that modes that carry small variance can have important effects on the large variance modes. In the present study, we propose a systematic strategy for obtaining statistically accurate low order models. The normal modes are sorted in decreasing Sobol indices using asymptotic expansions, and the relevant modes are extracted using a modified iterative Krylov-based method. The statistics of acoustic signals are computed by decomposing the original pulse into a truncated sum of modal pulses that can be described by a stationary phase method. As the low-order acoustic model preserves the overall structure of waveforms under perturbations of the atmosphere, it can be applied to uncertainty quantification. The result of this study is a new algorithm which applies on the entire phase space of acoustic fields.

The Transfer Function Model as a Tool to Study and Describe Space Weather Phenomena

NASA Technical Reports Server (NTRS)

Porter, Hayden S.; Mayr, Hans G.; Bhartia, P. K. (Technical Monitor)

2001-01-01

The Transfer Function Model (TFM) is a semi-analytical, linear model that is designed especially to describe thermospheric perturbations associated with magnetic storms and substorm. activity. It is a multi-constituent model (N2, O, He H, Ar) that accounts for wind induced diffusion, which significantly affects not only the composition and mass density but also the temperature and wind fields. Because the TFM adopts a semianalytic approach in which the geometry and temporal dependencies of the driving sources are removed through the use of height-integrated Green's functions, it provides physical insight into the essential properties of processes being considered, which are uncluttered by the accidental complexities that arise from particular source geometrie and time dependences. Extending from the ground to 700 km, the TFM eliminates spurious effects due to arbitrarily chosen boundary conditions. A database of transfer functions, computed only once, can be used to synthesize a wide range of spatial and temporal sources dependencies. The response synthesis can be performed quickly in real-time using only limited computing capabilities. These features make the TFM unique among global dynamical models. Given these desirable properties, a version of the TFM has been developed for personal computers (PC) using advanced platform-independent 3D visualization capabilities. We demonstrate the model capabilities with simulations for different auroral sources, including the response of ducted gravity waves modes that propagate around the globe. The thermospheric response is found to depend strongly on the spatial and temporal frequency spectra of the storm. Such varied behavior is difficult to describe in statistical empirical models. To improve the capability of space weather prediction, the TFM thus could be grafted naturally onto existing statistical models using data assimilation.

Statistical appearance models based on probabilistic correspondences.

PubMed

Krüger, Julia; Ehrhardt, Jan; Handels, Heinz

2017-04-01

Model-based image analysis is indispensable in medical image processing. One key aspect of building statistical shape and appearance models is the determination of one-to-one correspondences in the training data set. At the same time, the identification of these correspondences is the most challenging part of such methods. In our earlier work, we developed an alternative method using correspondence probabilities instead of exact one-to-one correspondences for a statistical shape model (Hufnagel et al., 2008). In this work, a new approach for statistical appearance models without one-to-one correspondences is proposed. A sparse image representation is used to build a model that combines point position and appearance information at the same time. Probabilistic correspondences between the derived multi-dimensional feature vectors are used to omit the need for extensive preprocessing of finding landmarks and correspondences as well as to reduce the dependence of the generated model on the landmark positions. Model generation and model fitting can now be expressed by optimizing a single global criterion derived from a maximum a-posteriori (MAP) approach with respect to model parameters that directly affect both shape and appearance of the considered objects inside the images. The proposed approach describes statistical appearance modeling in a concise and flexible mathematical framework. Besides eliminating the demand for costly correspondence determination, the method allows for additional constraints as topological regularity in the modeling process. In the evaluation the model was applied for segmentation and landmark identification in hand X-ray images. The results demonstrate the feasibility of the model to detect hand contours as well as the positions of the joints between finger bones for unseen test images. Further, we evaluated the model on brain data of stroke patients to show the ability of the proposed model to handle partially corrupted data and to demonstrate a possible employment of the correspondence probabilities to indicate these corrupted/pathological areas. Copyright © 2017 Elsevier B.V. All rights reserved.

Time-lapse microscopy and image processing for stem cell research: modeling cell migration

NASA Astrophysics Data System (ADS)

Gustavsson, Tomas; Althoff, Karin; Degerman, Johan; Olsson, Torsten; Thoreson, Ann-Catrin; Thorlin, Thorleif; Eriksson, Peter

2003-05-01

This paper presents hardware and software procedures for automated cell tracking and migration modeling. A time-lapse microscopy system equipped with a computer controllable motorized stage was developed. The performance of this stage was improved by incorporating software algorithms for stage motion displacement compensation and auto focus. The microscope is suitable for in-vitro stem cell studies and allows for multiple cell culture image sequence acquisition. This enables comparative studies concerning rate of cell splits, average cell motion velocity, cell motion as a function of cell sample density and many more. Several cell segmentation procedures are described as well as a cell tracking algorithm. Statistical methods for describing cell migration patterns are presented. In particular, the Hidden Markov Model (HMM) was investigated. Results indicate that if the cell motion can be described as a non-stationary stochastic process, then the HMM can adequately model aspects of its dynamic behavior.

A mathematical model to describe the nonlinear elastic properties of the gastrocnemius tendon of chickens.

PubMed

Foutz, T L

1991-03-01

A phenomenological model was developed to describe the nonlinear elastic behavior of the avian gastrocnemius tendon. Quasistatic uniaxial tensile tests were used to apply a deformation and resulting load on the tendon at a deformation rate of 5 mm/min. Plots of deformation versus load indicated a nonlinear loading response. By calculating engineering stress and engineering strain, the experimental data were normalized for tendon shape. The elastic response was determined from stress-strain curves and was found to vary with engineering strain. The response to the applied engineering strain could best be described by a mathematical model that combined a linear function and a nonlinear function. Three parameters in the model were developed to represent the nonlinear elastic behavior of the tendon, thereby allowing analysis of elasticity without prior knowledge of engineering strain. This procedure reduced the amount of data needed for the statistical analysis of nonlinear elasticity.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poyer, D.A.

In this report, tests of statistical significance of five sets of variables with household energy consumption (at the point of end-use) are described. Five models, in sequence, were empirically estimated and tested for statistical significance by using the Residential Energy Consumption Survey of the US Department of Energy, Energy Information Administration. Each model incorporated additional information, embodied in a set of variables not previously specified in the energy demand system. The variable sets were generally labeled as economic variables, weather variables, household-structure variables, end-use variables, and housing-type variables. The tests of statistical significance showed each of the variable sets tomore » be highly significant in explaining the overall variance in energy consumption. The findings imply that the contemporaneous interaction of different types of variables, and not just one exclusive set of variables, determines the level of household energy consumption.« less

Statistical ensembles for money and debt

NASA Astrophysics Data System (ADS)

Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele

2012-10-01

We build a statistical ensemble representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation. Furthermore, with our formalism we recover and extend some results concerning the temperature of an economic system, previously presented in the literature by considering only the monetary base as a conserved quantity. Finally, we study the statistical ensemble for the Pareto distribution.

Statistical alignment: computational properties, homology testing and goodness-of-fit.

PubMed

Hein, J; Wiuf, C; Knudsen, B; Møller, M B; Wibling, G

2000-09-08

The model of insertions and deletions in biological sequences, first formulated by Thorne, Kishino, and Felsenstein in 1991 (the TKF91 model), provides a basis for performing alignment within a statistical framework. Here we investigate this model.Firstly, we show how to accelerate the statistical alignment algorithms several orders of magnitude. The main innovations are to confine likelihood calculations to a band close to the similarity based alignment, to get good initial guesses of the evolutionary parameters and to apply an efficient numerical optimisation algorithm for finding the maximum likelihood estimate. In addition, the recursions originally presented by Thorne, Kishino and Felsenstein can be simplified. Two proteins, about 1500 amino acids long, can be analysed with this method in less than five seconds on a fast desktop computer, which makes this method practical for actual data analysis.Secondly, we propose a new homology test based on this model, where homology means that an ancestor to a sequence pair can be found finitely far back in time. This test has statistical advantages relative to the traditional shuffle test for proteins.Finally, we describe a goodness-of-fit test, that allows testing the proposed insertion-deletion (indel) process inherent to this model and find that real sequences (here globins) probably experience indels longer than one, contrary to what is assumed by the model. Copyright 2000 Academic Press.

Hunting Solomonoff's Swans: Exploring the Boundary Between Physics and Statistics in Hydrological Modeling

NASA Astrophysics Data System (ADS)

Nearing, G. S.

2014-12-01

Statistical models consistently out-perform conceptual models in the short term, however to account for a nonstationary future (or an unobserved past) scientists prefer to base predictions on unchanging and commutable properties of the universe - i.e., physics. The problem with physically-based hydrology models is, of course, that they aren't really based on physics - they are based on statistical approximations of physical interactions, and we almost uniformly lack an understanding of the entropy associated with these approximations. Thermodynamics is successful precisely because entropy statistics are computable for homogeneous (well-mixed) systems, and ergodic arguments explain the success of Newton's laws to describe systems that are fundamentally quantum in nature. Unfortunately, similar arguments do not hold for systems like watersheds that are heterogeneous at a wide range of scales. Ray Solomonoff formalized the situation in 1968 by showing that given infinite evidence, simultaneously minimizing model complexity and entropy in predictions always leads to the best possible model. The open question in hydrology is about what happens when we don't have infinite evidence - for example, when the future will not look like the past, or when one watershed does not behave like another. How do we isolate stationary and commutable components of watershed behavior? I propose that one possible answer to this dilemma lies in a formal combination of physics and statistics. In this talk I outline my recent analogue (Solomonoff's theorem was digital) of Solomonoff's idea that allows us to quantify the complexity/entropy tradeoff in a way that is intuitive to physical scientists. I show how to formally combine "physical" and statistical methods for model development in a way that allows us to derive the theoretically best possible model given any given physics approximation(s) and available observations. Finally, I apply an analogue of Solomonoff's theorem to evaluate the tradeoff between model complexity and prediction power.

Modeling species-abundance relationships in multi-species collections

USGS Publications Warehouse

Peng, S.; Yin, Z.; Ren, H.; Guo, Q.

2003-01-01

Species-abundance relationship is one of the most fundamental aspects of community ecology. Since Motomura first developed the geometric series model to describe the feature of community structure, ecologists have developed many other models to fit the species-abundance data in communities. These models can be classified into empirical and theoretical ones, including (1) statistical models, i.e., negative binomial distribution (and its extension), log-series distribution (and its extension), geometric distribution, lognormal distribution, Poisson-lognormal distribution, (2) niche models, i.e., geometric series, broken stick, overlapping niche, particulate niche, random assortment, dominance pre-emption, dominance decay, random fraction, weighted random fraction, composite niche, Zipf or Zipf-Mandelbrot model, and (3) dynamic models describing community dynamics and restrictive function of environment on community. These models have different characteristics and fit species-abundance data in various communities or collections. Among them, log-series distribution, lognormal distribution, geometric series, and broken stick model have been most widely used.

Examining the Process of Responding to Circumplex Scales of Interpersonal Values Items: Should Ideal Point Scoring Methods Be Considered?

PubMed

Ling, Ying; Zhang, Minqiang; Locke, Kenneth D; Li, Guangming; Li, Zonglong

2016-01-01

The Circumplex Scales of Interpersonal Values (CSIV) is a 64-item self-report measure of goals from each octant of the interpersonal circumplex. We used item response theory methods to compare whether dominance models or ideal point models best described how people respond to CSIV items. Specifically, we fit a polytomous dominance model called the generalized partial credit model and an ideal point model of similar complexity called the generalized graded unfolding model to the responses of 1,893 college students. The results of both graphical comparisons of item characteristic curves and statistical comparisons of model fit suggested that an ideal point model best describes the process of responding to CSIV items. The different models produced different rank orderings of high-scoring respondents, but overall the models did not differ in their prediction of criterion variables (agentic and communal interpersonal traits and implicit motives).

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

Computer-aided auditing of prescription drug claims.

PubMed

Iyengar, Vijay S; Hermiz, Keith B; Natarajan, Ramesh

2014-09-01

We describe a methodology for identifying and ranking candidate audit targets from a database of prescription drug claims. The relevant audit targets may include various entities such as prescribers, patients and pharmacies, who exhibit certain statistical behavior indicative of potential fraud and abuse over the prescription claims during a specified period of interest. Our overall approach is consistent with related work in statistical methods for detection of fraud and abuse, but has a relative emphasis on three specific aspects: first, based on the assessment of domain experts, certain focus areas are selected and data elements pertinent to the audit analysis in each focus area are identified; second, specialized statistical models are developed to characterize the normalized baseline behavior in each focus area; and third, statistical hypothesis testing is used to identify entities that diverge significantly from their expected behavior according to the relevant baseline model. The application of this overall methodology to a prescription claims database from a large health plan is considered in detail.

Crater-based dating of geological units on Mars: methods and application for the new global geological map

USGS Publications Warehouse

Platz, Thomas; Michael, Gregory; Tanaka, Kenneth L.; Skinner, James A.; Fortezzo, Corey M.

2013-01-01

The new, post-Viking generation of Mars orbital imaging and topographical data provide significant higher-resolution details of surface morphologies, which induced a new effort to photo-geologically map the surface of Mars at 1:20,000,000 scale. Although from unit superposition relations a relative stratigraphical framework can be compiled, it was the ambition of this mapping project to provide absolute unit age constraints through crater statistics. In this study, the crater counting method is described in detail, starting with the selection of image data, type locations (both from the mapper’s and crater counter’s perspectives) and the identification of impact craters. We describe the criteria used to validate and analyse measured crater populations, and to derive and interpret crater model ages. We provide examples of how geological information about the unit’s resurfacing history can be retrieved from crater size–frequency distributions. Three cases illustrate short-, intermediate, and long-term resurfacing histories. In addition, we introduce an interpretation-independent visualisation of the crater resurfacing history that uses the reduction of the crater population in a given size range relative to the expected population given the observed crater density at larger sizes. From a set of potential type locations, 48 areas from 22 globally mapped units were deemed suitable for crater counting. Because resurfacing ages were derived from crater statistics, these secondary ages were used to define the unit age rather than the base age. Using the methods described herein, we modelled ages that are consistent with the interpreted stratigraphy. Our derived model ages allow age assignments to be included in unit names. We discuss the limitations of using the crater dating technique for global-scale geological mapping. Finally, we present recommendations for the documentation and presentation of crater statistics in publications.

Statistical distributions of earthquake numbers: consequence of branching process

NASA Astrophysics Data System (ADS)

Kagan, Yan Y.

2010-03-01

We discuss various statistical distributions of earthquake numbers. Previously, we derived several discrete distributions to describe earthquake numbers for the branching model of earthquake occurrence: these distributions are the Poisson, geometric, logarithmic and the negative binomial (NBD). The theoretical model is the `birth and immigration' population process. The first three distributions above can be considered special cases of the NBD. In particular, a point branching process along the magnitude (or log seismic moment) axis with independent events (immigrants) explains the magnitude/moment-frequency relation and the NBD of earthquake counts in large time/space windows, as well as the dependence of the NBD parameters on the magnitude threshold (magnitude of an earthquake catalogue completeness). We discuss applying these distributions, especially the NBD, to approximate event numbers in earthquake catalogues. There are many different representations of the NBD. Most can be traced either to the Pascal distribution or to the mixture of the Poisson distribution with the gamma law. We discuss advantages and drawbacks of both representations for statistical analysis of earthquake catalogues. We also consider applying the NBD to earthquake forecasts and describe the limits of the application for the given equations. In contrast to the one-parameter Poisson distribution so widely used to describe earthquake occurrence, the NBD has two parameters. The second parameter can be used to characterize clustering or overdispersion of a process. We determine the parameter values and their uncertainties for several local and global catalogues, and their subdivisions in various time intervals, magnitude thresholds, spatial windows, and tectonic categories. The theoretical model of how the clustering parameter depends on the corner (maximum) magnitude can be used to predict future earthquake number distribution in regions where very large earthquakes have not yet occurred.

Sandpile-based model for capturing magnitude distributions and spatiotemporal clustering and separation in regional earthquakes

NASA Astrophysics Data System (ADS)

Batac, Rene C.; Paguirigan, Antonino A., Jr.; Tarun, Anjali B.; Longjas, Anthony G.

2017-04-01

We propose a cellular automata model for earthquake occurrences patterned after the sandpile model of self-organized criticality (SOC). By incorporating a single parameter describing the probability to target the most susceptible site, the model successfully reproduces the statistical signatures of seismicity. The energy distributions closely follow power-law probability density functions (PDFs) with a scaling exponent of around -1. 6, consistent with the expectations of the Gutenberg-Richter (GR) law, for a wide range of the targeted triggering probability values. Additionally, for targeted triggering probabilities within the range 0.004-0.007, we observe spatiotemporal distributions that show bimodal behavior, which is not observed previously for the original sandpile. For this critical range of values for the probability, model statistics show remarkable comparison with long-period empirical data from earthquakes from different seismogenic regions. The proposed model has key advantages, the foremost of which is the fact that it simultaneously captures the energy, space, and time statistics of earthquakes by just introducing a single parameter, while introducing minimal parameters in the simple rules of the sandpile. We believe that the critical targeting probability parameterizes the memory that is inherently present in earthquake-generating regions.

Statistical Models of Fracture Relevant to Nuclear-Grade Graphite: Review and Recommendations

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Bratton, Robert L.

2011-01-01

The nuclear-grade (low-impurity) graphite needed for the fuel element and moderator material for next-generation (Gen IV) reactors displays large scatter in strength and a nonlinear stress-strain response from damage accumulation. This response can be characterized as quasi-brittle. In this expanded review, relevant statistical failure models for various brittle and quasi-brittle material systems are discussed with regard to strength distribution, size effect, multiaxial strength, and damage accumulation. This includes descriptions of the Weibull, Batdorf, and Burchell models as well as models that describe the strength response of composite materials, which involves distributed damage. Results from lattice simulations are included for a physics-based description of material breakdown. Consideration is given to the predicted transition between brittle and quasi-brittle damage behavior versus the density of damage (level of disorder) within the material system. The literature indicates that weakest-link-based failure modeling approaches appear to be reasonably robust in that they can be applied to materials that display distributed damage, provided that the level of disorder in the material is not too large. The Weibull distribution is argued to be the most appropriate statistical distribution to model the stochastic-strength response of graphite.

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

NASA Astrophysics Data System (ADS)

Alexandridis, Konstantinos T.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research.

Covariance approximation for fast and accurate computation of channelized Hotelling observer statistics

NASA Astrophysics Data System (ADS)

Bonetto, P.; Qi, Jinyi; Leahy, R. M.

2000-08-01

Describes a method for computing linear observer statistics for maximum a posteriori (MAP) reconstructions of PET images. The method is based on a theoretical approximation for the mean and covariance of MAP reconstructions. In particular, the authors derive here a closed form for the channelized Hotelling observer (CHO) statistic applied to 2D MAP images. The theoretical analysis models both the Poission statistics of PET data and the inhomogeneity of tracer uptake. The authors show reasonably good correspondence between these theoretical results and Monte Carlo studies. The accuracy and low computational cost of the approximation allow the authors to analyze the observer performance over a wide range of operating conditions and parameter settings for the MAP reconstruction algorithm.

The theoretical and experimental study of a material structure evolution in gigacyclic fatigue regime

NASA Astrophysics Data System (ADS)

Plekhov, Oleg; Naimark, Oleg; Narykova, Maria; Kadomtsev, Andrey; Betekhtin, Vladimir

2015-10-01

The work is devoted to the study of the metal structure evolution under gigacyclic fatigue (VHCF) regime. The study of the mechanical properties of the samples (Armco iron) with different state of life time existing was carried out on the base of the acoustic resonance method. The damage accumulation (porosity of the samples) was studied by the hydrostatic weighing method. A statistical model of damage accumulation was proposed in order to describe the damage accumulation process. The model describes the influence of the sample surface on the location of fatigue crack initiation.

Introductory Life Science Mathematics and Quantitative Neuroscience Courses

PubMed Central

Olifer, Andrei

2010-01-01

We describe two sets of courses designed to enhance the mathematical, statistical, and computational training of life science undergraduates at Emory College. The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics. The second is an upper-division course in computational neuroscience. We provide a description of each course, detailed syllabi, examples of content, and a brief discussion of the main issues encountered in developing and offering the courses. PMID:20810971

Statistical fluctuations of an ocean surface inferred from shoes and ships

NASA Astrophysics Data System (ADS)

Lerche, Ian; Maubeuge, Frédéric

1995-12-01

This paper shows that it is possible to roughly estimate some ocean properties using simple time-dependent statistical models of ocean fluctuations. Based on a real incident, the loss by a vessel of a Nike shoes container in the North Pacific Ocean, a statistical model was tested on data sets consisting of the Nike shoes found by beachcombers a few months later. This statistical treatment of the shoes' motion allows one to infer velocity trends of the Pacific Ocean, together with their fluctuation strengths. The idea is to suppose that there is a mean bulk flow speed that can depend on location on the ocean surface and time. The fluctuations of the surface flow speed are then treated as statistically random. The distribution of shoes is described in space and time using Markov probability processes related to the mean and fluctuating ocean properties. The aim of the exercise is to provide some of the properties of the Pacific Ocean that are otherwise calculated using a sophisticated numerical model, OSCURS, where numerous data are needed. Relevant quantities are sharply estimated, which can be useful to (1) constrain output results from OSCURS computations, and (2) elucidate the behavior patterns of ocean flow characteristics on long time scales.

On the implications of the classical ergodic theorems: analysis of developmental processes has to focus on intra-individual variation.

PubMed

Molenaar, Peter C M

2008-01-01

It is argued that general mathematical-statistical theorems imply that standard statistical analysis techniques of inter-individual variation are invalid to investigate developmental processes. Developmental processes have to be analyzed at the level of individual subjects, using time series data characterizing the patterns of intra-individual variation. It is shown that standard statistical techniques based on the analysis of inter-individual variation appear to be insensitive to the presence of arbitrary large degrees of inter-individual heterogeneity in the population. An important class of nonlinear epigenetic models of neural growth is described which can explain the occurrence of such heterogeneity in brain structures and behavior. Links with models of developmental instability are discussed. A simulation study based on a chaotic growth model illustrates the invalidity of standard analysis of inter-individual variation, whereas time series analysis of intra-individual variation is able to recover the true state of affairs. (c) 2007 Wiley Periodicals, Inc.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

A detailed heterogeneous agent model for a single asset financial market with trading via an order book.

PubMed

Mota Navarro, Roberto; Larralde, Hernán

2017-01-01

We present an agent based model of a single asset financial market that is capable of replicating most of the non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. In our model agents employ strategies inspired on those used in real markets, and a realistic trade mechanism based on a double auction order book. We study the role of the distinct types of trader on the return statistics: specifically, correlation properties (or lack thereof), volatility clustering, heavy tails, and the degree to which the distribution can be described by a log-normal. Further, by introducing the practice of "profit taking", our model is also capable of replicating the stylized fact related to an asymmetry in the distribution of losses and gains.

A detailed heterogeneous agent model for a single asset financial market with trading via an order book

PubMed Central

2017-01-01

We present an agent based model of a single asset financial market that is capable of replicating most of the non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. In our model agents employ strategies inspired on those used in real markets, and a realistic trade mechanism based on a double auction order book. We study the role of the distinct types of trader on the return statistics: specifically, correlation properties (or lack thereof), volatility clustering, heavy tails, and the degree to which the distribution can be described by a log-normal. Further, by introducing the practice of “profit taking”, our model is also capable of replicating the stylized fact related to an asymmetry in the distribution of losses and gains. PMID:28245251

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herbert, J.H.

This brief note describes the probabilistic structure of the Arps/Roberts (A/R) model of petroleum discovery. A model similar to the A/R model is derived from probabilistic propositions demonstrated to be similar to the E. Barouch/G.M. Kaufman (B/K) model, and also demonstrated to be similar to the Drew, Schuenemeyer, and Root (D/S/R) model. This note attempts to elucidate and to simplify some fundamental ideas contained in an unpublished paper by Barouch and Kaufman. This note and its predecessor paper does not attempt to address a wide variety of statistical approaches for estimating petroleum resource availability. Rather, an attempt is made tomore » draw attention to characteristics of certain methods that are commonly used, both formally and informally, to estimate a petroleum resource base for a basin or a nation. Some of these characteristics are statistical, but many are not, except in the broadest sense of the term.« less

Analysis of survival data from telemetry projects

USGS Publications Warehouse

Bunck, C.M.; Winterstein, S.R.; Pollock, K.H.

1985-01-01

Telemetry techniques can be used to study the survival rates of animal populations and are particularly suitable for species or settings for which band recovery models are not. Statistical methods for estimating survival rates and parameters of survival distributions from observations of radio-tagged animals will be described. These methods have been applied to medical and engineering studies and to the study of nest success. Estimates and tests based on discrete models, originally introduced by Mayfield, and on continuous models, both parametric and nonparametric, will be described. Generalizations, including staggered entry of subjects into the study and identification of mortality factors will be considered. Additional discussion topics will include sample size considerations, relocation frequency for subjects, and use of covariates.

Symposium Issue on the Energy Information Administration.

ERIC Educational Resources Information Center

Kent, Calvin A.; And Others

1993-01-01

Describes the Energy Information Administration (EIA), a statistical agency which provides credible, timely, and useful energy information for decision makers in all sectors of society. The 10 articles included in the volume cover survey design, data collection, data integration, data analysis, modeling and forecasting, confidentiality, and…

The Impact of New Technology on Accounting Education.

ERIC Educational Resources Information Center

Shaoul, Jean

The introduction of computers in the Department of Accounting and Finance at Manchester University is described. General background outlining the increasing need for microcomputers in the accounting curriculum (including financial modelling tools and decision support systems such as linear programming, statistical packages, and simulation) is…

A statistical method to estimate low-energy hadronic cross sections

NASA Astrophysics Data System (ADS)

Balassa, Gábor; Kovács, Péter; Wolf, György

2018-02-01

In this article we propose a model based on the Statistical Bootstrap approach to estimate the cross sections of different hadronic reactions up to a few GeV in c.m.s. energy. The method is based on the idea, when two particles collide a so-called fireball is formed, which after a short time period decays statistically into a specific final state. To calculate the probabilities we use a phase space description extended with quark combinatorial factors and the possibility of more than one fireball formation. In a few simple cases the probability of a specific final state can be calculated analytically, where we show that the model is able to reproduce the ratios of the considered cross sections. We also show that the model is able to describe proton-antiproton annihilation at rest. In the latter case we used a numerical method to calculate the more complicated final state probabilities. Additionally, we examined the formation of strange and charmed mesons as well, where we used existing data to fit the relevant model parameters.

Synoptic scale forecast skill and systematic errors in the MASS 2.0 model. [Mesoscale Atmospheric Simulation System

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K. F.

1985-01-01

The synoptic scale performance characteristics of MASS 2.0 are determined by comparing filtered 12-24 hr model forecasts to same-case forecasts made by the National Meteorological Center's synoptic-scale Limited-area Fine Mesh model. Characteristics of the two systems are contrasted, and the analysis methodology used to determine statistical skill scores and systematic errors is described. The overall relative performance of the two models in the sample is documented, and important systematic errors uncovered are presented.

PharmML in Action: an Interoperable Language for Modeling and Simulation

PubMed Central

Bizzotto, R; Smith, G; Yvon, F; Kristensen, NR; Swat, MJ

2017-01-01

PharmML1 is an XML‐based exchange format2, 3, 4 created with a focus on nonlinear mixed‐effect (NLME) models used in pharmacometrics,5, 6 but providing a very general framework that also allows describing mathematical and statistical models such as single‐subject or nonlinear and multivariate regression models. This tutorial provides an overview of the structure of this language, brief suggestions on how to work with it, and use cases demonstrating its power and flexibility. PMID:28575551

Managing fish habitat for flow and temperature extremes ...

EPA Pesticide Factsheets

Summer low flows and stream temperature maxima are key drivers affecting the sustainability of fish populations. Thus, it is critical to understand both the natural templates of spatiotemporal variability, how these are shifting due to anthropogenic influences of development and climate change, and how these impacts can be moderated by natural and constructed green infrastructure. Low flow statistics of New England streams have been characterized using a combination of regression equations to describe long-term averages as a function of indicators of hydrologic regime (rain- versus snow-dominated), precipitation, evapotranspiration or temperature, surface water storage, baseflow recession rates, and impervious cover. Difference equations have been constructed to describe interannual variation in low flow as a function of changing air temperature, precipitation, and ocean-atmospheric teleconnection indices. Spatial statistical network models have been applied to explore fine-scale variability of thermal regimes along stream networks in New England as a function of variables describing natural and altered energy inputs, groundwater contributions, and retention time. Low flows exacerbate temperature impacts by reducing thermal inertia of streams to energy inputs. Based on these models, we can construct scenarios of fish habitat suitability using current and projected future climate and the potential for preservation and restoration of historic habitat regimes th

Estimation of Cell Proliferation Dynamics Using CFSE Data

PubMed Central

Banks, H.T.; Sutton, Karyn L.; Thompson, W. Clayton; Bocharov, Gennady; Roose, Dirk; Schenkel, Tim; Meyerhans, Andreas

2010-01-01

Advances in fluorescent labeling of cells as measured by flow cytometry have allowed for quantitative studies of proliferating populations of cells. The investigations (Luzyanina et al. in J. Math. Biol. 54:57–89, 2007; J. Math. Biol., 2009; Theor. Biol. Med. Model. 4:1–26, 2007) contain a mathematical model with fluorescence intensity as a structure variable to describe the evolution in time of proliferating cells labeled by carboxyfluorescein succinimidyl ester (CFSE). Here, this model and several extensions/modifications are discussed. Suggestions for improvements are presented and analyzed with respect to statistical significance for better agreement between model solutions and experimental data. These investigations suggest that the new decay/label loss and time dependent effective proliferation and death rates do indeed provide improved fits of the model to data. Statistical models for the observed variability/noise in the data are discussed with implications for uncertainty quantification. The resulting new cell dynamics model should prove useful in proliferation assay tracking and modeling, with numerous applications in the biomedical sciences. PMID:20195910

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

SOCR: Statistics Online Computational Resource

PubMed Central

Dinov, Ivo D.

2011-01-01

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student’s intuition and enhance their learning. PMID:21451741

Forecasting volatility with neural regression: a contribution to model adequacy.

PubMed

Refenes, A N; Holt, W T

2001-01-01

Neural nets' usefulness for forecasting is limited by problems of overfitting and the lack of rigorous procedures for model identification, selection and adequacy testing. This paper describes a methodology for neural model misspecification testing. We introduce a generalization of the Durbin-Watson statistic for neural regression and discuss the general issues of misspecification testing using residual analysis. We derive a generalized influence matrix for neural estimators which enables us to evaluate the distribution of the statistic. We deploy Monte Carlo simulation to compare the power of the test for neural and linear regressors. While residual testing is not a sufficient condition for model adequacy, it is nevertheless a necessary condition to demonstrate that the model is a good approximation to the data generating process, particularly as neural-network estimation procedures are susceptible to partial convergence. The work is also an important step toward developing rigorous procedures for neural model identification, selection and adequacy testing which have started to appear in the literature. We demonstrate its applicability in the nontrivial problem of forecasting implied volatility innovations using high-frequency stock index options. Each step of the model building process is validated using statistical tests to verify variable significance and model adequacy with the results confirming the presence of nonlinear relationships in implied volatility innovations.

Solar F10.7 radiation - A short term model for Space Station applications

NASA Technical Reports Server (NTRS)

Vedder, John D.; Tabor, Jill L.

1991-01-01

A new method is described for statistically modeling the F10.7 component of solar radiation for 91-day intervals. The resulting model represents this component of the solar flux as a quasi-exponentially correlated, Weibull distributed random variable, and thereby demonstrates excellent agreement with observed F10.7 data. Values of the F10.7 flux are widely used in models of the earth's upper atmosphere because of its high correlation with density fluctuations due to solar heating effects. Because of the direct relation between atmospheric density and drag, a realistic model of the short term fluctuation of the F10.7 flux is important for the design and operation of Space Station Freedom. The method of modeling this flux described in this report should therefore be useful for a variety of Space Station applications.

[How to fit and interpret multilevel models using SPSS].

PubMed

Pardo, Antonio; Ruiz, Miguel A; San Martín, Rafael

2007-05-01

Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.

Computational Medicine: Translating Models to Clinical Care

PubMed Central

Winslow, Raimond L.; Trayanova, Natalia; Geman, Donald; Miller, Michael I.

2013-01-01

Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health. PMID:23115356

Ranking and validation of spallation models for isotopic production cross sections of heavy residua

NASA Astrophysics Data System (ADS)

Sharma, Sushil K.; Kamys, Bogusław; Goldenbaum, Frank; Filges, Detlef

2017-07-01

The production cross sections of isotopically identified residual nuclei of spallation reactions induced by 136Xe projectiles at 500AMeV on hydrogen target were analyzed in a two-step model. The first stage of the reaction was described by the INCL4.6 model of an intranuclear cascade of nucleon-nucleon and pion-nucleon collisions whereas the second stage was analyzed by means of four different models; ABLA07, GEM2, GEMINI++ and SMM. The quality of the data description was judged quantitatively using two statistical deviation factors; the H-factor and the M-factor. It was found that the present analysis leads to a different ranking of models as compared to that obtained from the qualitative inspection of the data reproduction. The disagreement was caused by sensitivity of the deviation factors to large statistical errors present in some of the data. A new deviation factor, the A factor, was proposed, that is not sensitive to the statistical errors of the cross sections. The quantitative ranking of models performed using the A-factor agreed well with the qualitative analysis of the data. It was concluded that using the deviation factors weighted by statistical errors may lead to erroneous conclusions in the case when the data cover a large range of values. The quality of data reproduction by the theoretical models is discussed. Some systematic deviations of the theoretical predictions from the experimental results are observed.

Advances in analytical chemistry

NASA Technical Reports Server (NTRS)

Arendale, W. F.; Congo, Richard T.; Nielsen, Bruce J.

1991-01-01

Implementation of computer programs based on multivariate statistical algorithms makes possible obtaining reliable information from long data vectors that contain large amounts of extraneous information, for example, noise and/or analytes that we do not wish to control. Three examples are described. Each of these applications requires the use of techniques characteristic of modern analytical chemistry. The first example, using a quantitative or analytical model, describes the determination of the acid dissociation constant for 2,2'-pyridyl thiophene using archived data. The second example describes an investigation to determine the active biocidal species of iodine in aqueous solutions. The third example is taken from a research program directed toward advanced fiber-optic chemical sensors. The second and third examples require heuristic or empirical models.

Derivation of the Statistical Distribution of the Mass Peak Centroids of Mass Spectrometers Employing Analog-to-Digital Converters and Electron Multipliers

DOE PAGES

Ipsen, Andreas

2017-02-03

Here, the mass peak centroid is a quantity that is at the core of mass spectrometry (MS). However, despite its central status in the field, models of its statistical distribution are often chosen quite arbitrarily and without attempts at establishing a proper theoretical justification for their use. Recent work has demonstrated that for mass spectrometers employing analog-to-digital converters (ADCs) and electron multipliers, the statistical distribution of the mass peak intensity can be described via a relatively simple model derived essentially from first principles. Building on this result, the following article derives the corresponding statistical distribution for the mass peak centroidsmore » of such instruments. It is found that for increasing signal strength, the centroid distribution converges to a Gaussian distribution whose mean and variance are determined by physically meaningful parameters and which in turn determine bias and variability of the m/z measurements of the instrument. Through the introduction of the concept of “pulse-peak correlation”, the model also elucidates the complicated relationship between the shape of the voltage pulses produced by the preamplifier and the mean and variance of the centroid distribution. The predictions of the model are validated with empirical data and with Monte Carlo simulations.« less

Derivation of the Statistical Distribution of the Mass Peak Centroids of Mass Spectrometers Employing Analog-to-Digital Converters and Electron Multipliers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ipsen, Andreas

Here, the mass peak centroid is a quantity that is at the core of mass spectrometry (MS). However, despite its central status in the field, models of its statistical distribution are often chosen quite arbitrarily and without attempts at establishing a proper theoretical justification for their use. Recent work has demonstrated that for mass spectrometers employing analog-to-digital converters (ADCs) and electron multipliers, the statistical distribution of the mass peak intensity can be described via a relatively simple model derived essentially from first principles. Building on this result, the following article derives the corresponding statistical distribution for the mass peak centroidsmore » of such instruments. It is found that for increasing signal strength, the centroid distribution converges to a Gaussian distribution whose mean and variance are determined by physically meaningful parameters and which in turn determine bias and variability of the m/z measurements of the instrument. Through the introduction of the concept of “pulse-peak correlation”, the model also elucidates the complicated relationship between the shape of the voltage pulses produced by the preamplifier and the mean and variance of the centroid distribution. The predictions of the model are validated with empirical data and with Monte Carlo simulations.« less

Nonlinear Wave Chaos and the Random Coupling Model

NASA Astrophysics Data System (ADS)

Zhou, Min; Ott, Edward; Antonsen, Thomas M.; Anlage, Steven

The Random Coupling Model (RCM) has been shown to successfully predict the statistical properties of linear wave chaotic cavities in the highly over-moded regime. It is of interest to extend the RCM to strongly nonlinear systems. To introduce nonlinearity, an active nonlinear circuit is connected to two ports of the wave chaotic 1/4-bowtie cavity. The active nonlinear circuit consists of a frequency multiplier, an amplifier and several passive filters. It acts to double the input frequency in the range from 3.5 GHz to 5 GHz, and operates for microwaves going in only one direction. Measurements are taken between two additional ports of the cavity and we measure the statistics of the second harmonic voltage over an ensemble of realizations of the scattering system. We developed an RCM-based model of this system as two chaotic cavities coupled by means of a nonlinear transfer function. The harmonics received at the output are predicted to be the product of three statistical quantities that describe the three elements correspondingly. Statistical results from simulation, RCM-based modeling, and direct experimental measurements will be compared. ONR under Grant No. N000141512134, AFOSR under COE Grant FA9550-15-1-0171,0 and the Maryland Center for Nanophysics and Advanced Materials.

Statistical Analysis of Crystallization Database Links Protein Physico-Chemical Features with Crystallization Mechanisms

PubMed Central

Fusco, Diana; Barnum, Timothy J.; Bruno, Andrew E.; Luft, Joseph R.; Snell, Edward H.; Mukherjee, Sayan; Charbonneau, Patrick

2014-01-01

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis. PMID:24988076

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.

PubMed

Fusco, Diana; Barnum, Timothy J; Bruno, Andrew E; Luft, Joseph R; Snell, Edward H; Mukherjee, Sayan; Charbonneau, Patrick

2014-01-01

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

A MULTIPLE TESTING OF THE ABC METHOD AND THE DEVELOPMENT OF A SECOND-GENERATION MODEL. PART II, TEST RESULTS AND AN ANALYSIS OF RECALL RATIO.

ERIC Educational Resources Information Center

ALTMANN, BERTHOLD

AFTER A BRIEF SUMMARY OF THE TEST PROGRAM (DESCRIBED MORE FULLY IN LI 000 318), THE STATISTICAL RESULTS TABULATED AS OVERALL "ABC (APPROACH BY CONCEPT)-RELEVANCE RATIOS" AND "ABC-RECALL FIGURES" ARE PRESENTED AND REVIEWED. AN ABSTRACT MODEL DEVELOPED IN ACCORDANCE WITH MAX WEBER'S "IDEALTYPUS" ("DIE OBJEKTIVITAET…

Do Assimilated Drifter Velocities Improve Lagrangian Predictability in an Operational Ocean Model?

DTIC Science & Technology

2015-05-01

extended Kalman filter . Molcard et al. (2005) used a statistical method to cor- relate model and drifter velocities. Taillandier et al. (2006) describe the... temperature and salinity observations. Trajectory angular differ- ences are also reduced. 1. Introduction The importance of Lagrangian forecasts was seen... Temperature , salinity, and sea surface height (SSH, measured along-track by satellite altimeters) observa- tions are typically assimilated in

Statistical metrology—measurement and modeling of variation for advanced process development and design rule generation

NASA Astrophysics Data System (ADS)

Boning, Duane S.; Chung, James E.

1998-11-01

Advanced process technology will require more detailed understanding and tighter control of variation in devices and interconnects. The purpose of statistical metrology is to provide methods to measure and characterize variation, to model systematic and random components of that variation, and to understand the impact of variation on both yield and performance of advanced circuits. Of particular concern are spatial or pattern-dependencies within individual chips; such systematic variation within the chip can have a much larger impact on performance than wafer-level random variation. Statistical metrology methods will play an important role in the creation of design rules for advanced technologies. For example, a key issue in multilayer interconnect is the uniformity of interlevel dielectric (ILD) thickness within the chip. For the case of ILD thickness, we describe phases of statistical metrology development and application to understanding and modeling thickness variation arising from chemical-mechanical polishing (CMP). These phases include screening experiments including design of test structures and test masks to gather electrical or optical data, techniques for statistical decomposition and analysis of the data, and approaches to calibrating empirical and physical variation models. These models can be integrated with circuit CAD tools to evaluate different process integration or design rule strategies. One focus for the generation of interconnect design rules are guidelines for the use of "dummy fill" or "metal fill" to improve the uniformity of underlying metal density and thus improve the uniformity of oxide thickness within the die. Trade-offs that can be evaluated via statistical metrology include the improvements to uniformity possible versus the effect of increased capacitance due to additional metal.

Detection of crossover time scales in multifractal detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Ge, Erjia; Leung, Yee

2013-04-01

Fractal is employed in this paper as a scale-based method for the identification of the scaling behavior of time series. Many spatial and temporal processes exhibiting complex multi(mono)-scaling behaviors are fractals. One of the important concepts in fractals is crossover time scale(s) that separates distinct regimes having different fractal scaling behaviors. A common method is multifractal detrended fluctuation analysis (MF-DFA). The detection of crossover time scale(s) is, however, relatively subjective since it has been made without rigorous statistical procedures and has generally been determined by eye balling or subjective observation. Crossover time scales such determined may be spurious and problematic. It may not reflect the genuine underlying scaling behavior of a time series. The purpose of this paper is to propose a statistical procedure to model complex fractal scaling behaviors and reliably identify the crossover time scales under MF-DFA. The scaling-identification regression model, grounded on a solid statistical foundation, is first proposed to describe multi-scaling behaviors of fractals. Through the regression analysis and statistical inference, we can (1) identify the crossover time scales that cannot be detected by eye-balling observation, (2) determine the number and locations of the genuine crossover time scales, (3) give confidence intervals for the crossover time scales, and (4) establish the statistically significant regression model depicting the underlying scaling behavior of a time series. To substantive our argument, the regression model is applied to analyze the multi-scaling behaviors of avian-influenza outbreaks, water consumption, daily mean temperature, and rainfall of Hong Kong. Through the proposed model, we can have a deeper understanding of fractals in general and a statistical approach to identify multi-scaling behavior under MF-DFA in particular.

Modeling nutrient retention at the watershed scale: Does small stream research apply to the whole river network?

NASA Astrophysics Data System (ADS)

Aguilera, Rosana; Marcé, Rafael; Sabater, Sergi

2013-06-01

are conveyed from terrestrial and upstream sources through drainage networks. Streams and rivers contribute to regulate the material exported downstream by means of transformation, storage, and removal of nutrients. It has been recently suggested that the efficiency of process rates relative to available nutrient concentration in streams eventually declines, following an efficiency loss (EL) dynamics. However, most of these predictions are based at the reach scale in pristine streams, failing to describe the role of entire river networks. Models provide the means to study nutrient cycling from the stream network perspective via upscaling to the watershed the key mechanisms occurring at the reach scale. We applied a hybrid process-based and statistical model (SPARROW, Spatially Referenced Regression on Watershed Attributes) as a heuristic approach to describe in-stream nutrient processes in a highly impaired, high stream order watershed (the Llobregat River Basin, NE Spain). The in-stream decay specifications of the model were modified to include a partial saturation effect in uptake efficiency (expressed as a power law) and better capture biological nutrient retention in river systems under high anthropogenic stress. The stream decay coefficients were statistically significant in both nitrate and phosphate models, indicating the potential role of in-stream processing in limiting nutrient export. However, the EL concept did not reliably describe the patterns of nutrient uptake efficiency for the concentration gradient and streamflow values found in the Llobregat River basin, posing in doubt its complete applicability to explain nutrient retention processes in stream networks comprising highly impaired rivers.

About influence of input rate random part of nonstationary queue system on statistical estimates of its macroscopic indicators

NASA Astrophysics Data System (ADS)

Korelin, Ivan A.; Porshnev, Sergey V.

2018-05-01

A model of the non-stationary queuing system (NQS) is described. The input of this model receives a flow of requests with input rate λ = λdet (t) + λrnd (t), where λdet (t) is a deterministic function depending on time; λrnd (t) is a random function. The parameters of functions λdet (t), λrnd (t) were identified on the basis of statistical information on visitor flows collected from various Russian football stadiums. The statistical modeling of NQS is carried out and the average statistical dependences are obtained: the length of the queue of requests waiting for service, the average wait time for the service, the number of visitors entered to the stadium on the time. It is shown that these dependencies can be characterized by the following parameters: the number of visitors who entered at the time of the match; time required to service all incoming visitors; the maximum value; the argument value when the studied dependence reaches its maximum value. The dependences of these parameters on the energy ratio of the deterministic and random component of the input rate are investigated.

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

USGS Publications Warehouse

Barker, Richard J.; Link, William A.

2015-01-01

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

FIRETEC: A transport description of wildfire behavior

DOE Office of Scientific and Technical Information (OSTI.GOV)

Linn, R.R.; Harlow, F.H.

1997-12-01

Wildfires are a threat to human life and property, yet they are an unavoidable part of nature and in some instances they are necessary for the natural maintenance and evolution of forests. Investigators have attempted to describe the behavior (speed, direction, modes of spread) of wildfires for over fifty years. Current models for numerical description are mainly algebraic and based on statistical or empirical ideas. The authors describe, in contrast, a transport model called FIRETEC, which is a self-determining fire behavior model. The use of transport formulations connects the propagation rates to the full conservation equations for energy, momentum, speciesmore » concentrations, mass, and turbulence. In this text, highlights of the model formulation and results are described. The goal of the FIRETEC model is to describe average behavior of the gases and fuels. It represents the essence of the combination of many small-scale processes without resolving each process in complete detail. The FIRETEC model is implemented into a computer code that examines line-fire propagation in a vertical spatial cut parallel to the direction of advancement. With this code the authors are able to examine wind effects, slope effects, and the effects of nonhomogeneous fuel distribution.« less

The use of analysis of variance procedures in biological studies

USGS Publications Warehouse

Williams, B.K.

1987-01-01

The analysis of variance (ANOVA) is widely used in biological studies, yet there remains considerable confusion among researchers about the interpretation of hypotheses being tested. Ambiguities arise when statistical designs are unbalanced, and in particular when not all combinations of design factors are represented in the data. This paper clarifies the relationship among hypothesis testing, statistical modelling and computing procedures in ANOVA for unbalanced data. A simple two-factor fixed effects design is used to illustrate three common parametrizations for ANOVA models, and some associations among these parametrizations are developed. Biologically meaningful hypotheses for main effects and interactions are given in terms of each parametrization, and procedures for testing the hypotheses are described. The standard statistical computing procedures in ANOVA are given along with their corresponding hypotheses. Throughout the development unbalanced designs are assumed and attention is given to problems that arise with missing cells.

Self-consistent assessment of Englert-Schwinger model on atomic properties

NASA Astrophysics Data System (ADS)

Lehtomäki, Jouko; Lopez-Acevedo, Olga

2017-12-01

Our manuscript investigates a self-consistent solution of the statistical atom model proposed by Berthold-Georg Englert and Julian Schwinger (the ES model) and benchmarks it against atomic Kohn-Sham and two orbital-free models of the Thomas-Fermi-Dirac (TFD)-λvW family. Results show that the ES model generally offers the same accuracy as the well-known TFD-1/5 vW model; however, the ES model corrects the failure in the Pauli potential near-nucleus region. We also point to the inability of describing low-Z atoms as the foremost concern in improving the present model.

Self-consistent assessment of Englert-Schwinger model on atomic properties.

PubMed

Lehtomäki, Jouko; Lopez-Acevedo, Olga

2017-12-21

Our manuscript investigates a self-consistent solution of the statistical atom model proposed by Berthold-Georg Englert and Julian Schwinger (the ES model) and benchmarks it against atomic Kohn-Sham and two orbital-free models of the Thomas-Fermi-Dirac (TFD)-λvW family. Results show that the ES model generally offers the same accuracy as the well-known TFD-15vW model; however, the ES model corrects the failure in the Pauli potential near-nucleus region. We also point to the inability of describing low-Z atoms as the foremost concern in improving the present model.

RESTSIM: A Simulation Model That Highlights Decision Making under Conditions of Uncertainty.

ERIC Educational Resources Information Center

Zinkhan, George M.; Taylor, James R.

1983-01-01

Describes RESTSIM, an interactive computer simulation program for graduate and upper-level undergraduate management, marketing, and retailing courses, which introduces naive users to simulation as a decision support technique, and provides a vehicle for studying various statistical procedures for evaluating simulation output. (MBR)

MIX: a computer program to evaluate interaction between chemicals

Treesearch

Jacqueline L. Robertson; Kimberly C. Smith

1989-01-01

A computer program, MIX, was designed to identify pairs of chemicals whose interaction results in a response that departs significantly from the model predicated on the assumption of independent, uncorrelated joint action. This report describes the MIX program, its statistical basis, and instructions for its use.

Applications of Stochastic Analyses for Collaborative Learning and Cognitive Assessment

DTIC Science & Technology

2007-04-01

models (Visser, Maartje, Raijmakers, & Molenaar , 2002). The second part of this paper illustrates two applications of the methods described in the...clustering three-way data sets. Computational Statistics and Data Analysis, 51 (11), 5368–5376. Visser, I., Maartje, E., Raijmakers, E. J., & Molenaar

Yes, the GIGP Really Does Work--And Is Workable!

ERIC Educational Resources Information Center

Burrell, Quentin L.; Fenton, Michael R.

1993-01-01

Discusses the generalized inverse Gaussian-Poisson (GIGP) process for informetric modeling. Negative binomial distribution is discussed, construction of the GIGP process is explained, zero-truncated GIGP is considered, and applications of the process with journals, library circulation statistics, and database index terms are described. (50…

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

ESTIMATE OF METHANE EMISSIONS FROM U.S. LANDFILLS

EPA Science Inventory

The report describes the development of a statistical regression model used for estimating methane (CH4) emissions, which relates landfill gas (LFG) flow rates to waste-in-place data from 105 landfills with LFG recovery projects. (NOTE: CH4 flow rates from landfills with LFG reco...

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index.

PubMed

Shi, Xiangnan; Cao, Libo; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2014-07-18

In this study, we developed a statistical rib cage geometry model accounting for variations by age, sex, stature and body mass index (BMI). Thorax CT scans were obtained from 89 subjects approximately evenly distributed among 8 age groups and both sexes. Threshold-based CT image segmentation was performed to extract the rib geometries, and a total of 464 landmarks on the left side of each subject׳s ribcage were collected to describe the size and shape of the rib cage as well as the cross-sectional geometry of each rib. Principal component analysis and multivariate regression analysis were conducted to predict rib cage geometry as a function of age, sex, stature, and BMI, all of which showed strong effects on rib cage geometry. Except for BMI, all parameters also showed significant effects on rib cross-sectional area using a linear mixed model. This statistical rib cage geometry model can serve as a geometric basis for developing a parametric human thorax finite element model for quantifying effects from different human attributes on thoracic injury risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

SPSS macros to compare any two fitted values from a regression model.

PubMed

Weaver, Bruce; Dubois, Sacha

2012-12-01

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

Predicting future protection of respirator users: Statistical approaches and practical implications.

PubMed

Hu, Chengcheng; Harber, Philip; Su, Jing

2016-01-01

The purpose of this article is to describe a statistical approach for predicting a respirator user's fit factor in the future based upon results from initial tests. A statistical prediction model was developed based upon joint distribution of multiple fit factor measurements over time obtained from linear mixed effect models. The model accounts for within-subject correlation as well as short-term (within one day) and longer-term variability. As an example of applying this approach, model parameters were estimated from a research study in which volunteers were trained by three different modalities to use one of two types of respirators. They underwent two quantitative fit tests at the initial session and two on the same day approximately six months later. The fitted models demonstrated correlation and gave the estimated distribution of future fit test results conditional on past results for an individual worker. This approach can be applied to establishing a criterion value for passing an initial fit test to provide reasonable likelihood that a worker will be adequately protected in the future; and to optimizing the repeat fit factor test intervals individually for each user for cost-effective testing.

Specification of the ISS Plasma Environment Variability

NASA Technical Reports Server (NTRS)

Minow, Joseph I.; Neergaard, Linda F.; Bui, Them H.; Mikatarian, Ronald R.; Barsamian, H.; Koontz, Steven L.

2002-01-01

Quantifying the spacecraft charging risks and corresponding hazards for the International Space Station (ISS) requires a plasma environment specification describing the natural variability of ionospheric temperature (Te) and density (Ne). Empirical ionospheric specification and forecast models such as the International Reference Ionosphere (IRI) model typically only provide estimates of long term (seasonal) mean Te and Ne values for the low Earth orbit environment. Knowledge of the Te and Ne variability as well as the likelihood of extreme deviations from the mean values are required to estimate both the magnitude and frequency of occurrence of potentially hazardous spacecraft charging environments for a given ISS construction stage and flight configuration. This paper describes the statistical analysis of historical ionospheric low Earth orbit plasma measurements used to estimate Ne, Te variability in the ISS flight environment. The statistical variability analysis of Ne and Te enables calculation of the expected frequency of Occurrence of any particular values of Ne and Te, especially those that correspond to possibly hazardous spacecraft charging environments. The database used in the original analysis included measurements from the AE-C, AE-D, and DE-2 satellites. Recent work on the database has added additional satellites to the database and ground based incoherent scatter radar observations as well. Deviations of the data values from the IRI estimated Ne, Te parameters for each data point provide a statistical basis for modeling the deviations of the plasma environment from the IRI model output. This technique, while developed specifically for the Space Station analysis, can also be generalized to provide ionospheric plasma environment risk specification models for low Earth orbit over an altitude range of 200 km through approximately 1000 km.

Lagged correlation networks

NASA Astrophysics Data System (ADS)

Curme, Chester

Technological advances have provided scientists with large high-dimensional datasets that describe the behaviors of complex systems: from the statistics of energy levels in complex quantum systems, to the time-dependent transcription of genes, to price fluctuations among assets in a financial market. In this environment, where it may be difficult to infer the joint distribution of the data, network science has flourished as a way to gain insight into the structure and organization of such systems by focusing on pairwise interactions. This work focuses on a particular setting, in which a system is described by multivariate time series data. We consider time-lagged correlations among elements in this system, in such a way that the measured interactions among elements are asymmetric. Finally, we allow these interactions to be characteristically weak, so that statistical uncertainties may be important to consider when inferring the structure of the system. We introduce a methodology for constructing statistically validated networks to describe such a system, extend the methodology to accommodate interactions with a periodic component, and show how consideration of bipartite community structures in these networks can aid in the construction of robust statistical models. An example of such a system is a financial market, in which high frequency returns data may be used to describe contagion, or the spreading of shocks in price among assets. These data provide the experimental testing ground for our methodology. We study NYSE data from both the present day and one decade ago, examine the time scales over which the validated lagged correlation networks exist, and relate differences in the topological properties of the networks to an increasing economic efficiency. We uncover daily periodicities in the validated interactions, and relate our findings to explanations of the Epps Effect, an empirical phenomenon of financial time series. We also study bipartite community structures in networks composed of market returns and news sentiment signals for 40 countries. We compare the degrees to which markets anticipate news, and news anticipate markets, and use the community structures to construct a recommender system for inputs to prediction models. Finally, we complement this work with novel investigations of the exogenous news items that may drive the financial system using topic models. This includes an analysis of how investors and the general public may interact with these news items using Internet search data, and how the diversity of stories in the news both responds to and influences market movements.

Stochastic modeling of sunshine number data

NASA Astrophysics Data System (ADS)

Brabec, Marek; Paulescu, Marius; Badescu, Viorel

2013-11-01

In this paper, we will present a unified statistical modeling framework for estimation and forecasting sunshine number (SSN) data. Sunshine number has been proposed earlier to describe sunshine time series in qualitative terms (Theor Appl Climatol 72 (2002) 127-136) and since then, it was shown to be useful not only for theoretical purposes but also for practical considerations, e.g. those related to the development of photovoltaic energy production. Statistical modeling and prediction of SSN as a binary time series has been challenging problem, however. Our statistical model for SSN time series is based on an underlying stochastic process formulation of Markov chain type. We will show how its transition probabilities can be efficiently estimated within logistic regression framework. In fact, our logistic Markovian model can be relatively easily fitted via maximum likelihood approach. This is optimal in many respects and it also enables us to use formalized statistical inference theory to obtain not only the point estimates of transition probabilities and their functions of interest, but also related uncertainties, as well as to test of various hypotheses of practical interest, etc. It is straightforward to deal with non-homogeneous transition probabilities in this framework. Very importantly from both physical and practical points of view, logistic Markov model class allows us to test hypotheses about how SSN dependents on various external covariates (e.g. elevation angle, solar time, etc.) and about details of the dynamic model (order and functional shape of the Markov kernel, etc.). Therefore, using generalized additive model approach (GAM), we can fit and compare models of various complexity which insist on keeping physical interpretation of the statistical model and its parts. After introducing the Markovian model and general approach for identification of its parameters, we will illustrate its use and performance on high resolution SSN data from the Solar Radiation Monitoring Station of the West University of Timisoara.

Stochastic modeling of sunshine number data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brabec, Marek, E-mail: mbrabec@cs.cas.cz; Paulescu, Marius; Badescu, Viorel

2013-11-13

In this paper, we will present a unified statistical modeling framework for estimation and forecasting sunshine number (SSN) data. Sunshine number has been proposed earlier to describe sunshine time series in qualitative terms (Theor Appl Climatol 72 (2002) 127-136) and since then, it was shown to be useful not only for theoretical purposes but also for practical considerations, e.g. those related to the development of photovoltaic energy production. Statistical modeling and prediction of SSN as a binary time series has been challenging problem, however. Our statistical model for SSN time series is based on an underlying stochastic process formulation ofmore » Markov chain type. We will show how its transition probabilities can be efficiently estimated within logistic regression framework. In fact, our logistic Markovian model can be relatively easily fitted via maximum likelihood approach. This is optimal in many respects and it also enables us to use formalized statistical inference theory to obtain not only the point estimates of transition probabilities and their functions of interest, but also related uncertainties, as well as to test of various hypotheses of practical interest, etc. It is straightforward to deal with non-homogeneous transition probabilities in this framework. Very importantly from both physical and practical points of view, logistic Markov model class allows us to test hypotheses about how SSN dependents on various external covariates (e.g. elevation angle, solar time, etc.) and about details of the dynamic model (order and functional shape of the Markov kernel, etc.). Therefore, using generalized additive model approach (GAM), we can fit and compare models of various complexity which insist on keeping physical interpretation of the statistical model and its parts. After introducing the Markovian model and general approach for identification of its parameters, we will illustrate its use and performance on high resolution SSN data from the Solar Radiation Monitoring Station of the West University of Timisoara.« less

Probabilistic models for reactive behaviour in heterogeneous condensed phase media

NASA Astrophysics Data System (ADS)

Baer, M. R.; Gartling, D. K.; DesJardin, P. E.

2012-02-01

This work presents statistically-based models to describe reactive behaviour in heterogeneous energetic materials. Mesoscale effects are incorporated in continuum-level reactive flow descriptions using probability density functions (pdfs) that are associated with thermodynamic and mechanical states. A generalised approach is presented that includes multimaterial behaviour by treating the volume fraction as a random kinematic variable. Model simplifications are then sought to reduce the complexity of the description without compromising the statistical approach. Reactive behaviour is first considered for non-deformable media having a random temperature field as an initial state. A pdf transport relationship is derived and an approximate moment approach is incorporated in finite element analysis to model an example application whereby a heated fragment impacts a reactive heterogeneous material which leads to a delayed cook-off event. Modelling is then extended to include deformation effects associated with shock loading of a heterogeneous medium whereby random variables of strain, strain-rate and temperature are considered. A demonstrative mesoscale simulation of a non-ideal explosive is discussed that illustrates the joint statistical nature of the strain and temperature fields during shock loading to motivate the probabilistic approach. This modelling is derived in a Lagrangian framework that can be incorporated in continuum-level shock physics analysis. Future work will consider particle-based methods for a numerical implementation of this modelling approach.

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 1: Methodology and applications

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

Virtual sensor models for real-time applications

NASA Astrophysics Data System (ADS)

Hirsenkorn, Nils; Hanke, Timo; Rauch, Andreas; Dehlink, Bernhard; Rasshofer, Ralph; Biebl, Erwin

2016-09-01

Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensor-model, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.

Structural equation modeling: building and evaluating causal models: Chapter 8

USGS Publications Warehouse

Grace, James B.; Scheiner, Samuel M.; Schoolmaster, Donald R.

2015-01-01

Scientists frequently wish to study hypotheses about causal relationships, rather than just statistical associations. This chapter addresses the question of how scientists might approach this ambitious task. Here we describe structural equation modeling (SEM), a general modeling framework for the study of causal hypotheses. Our goals are to (a) concisely describe the methodology, (b) illustrate its utility for investigating ecological systems, and (c) provide guidance for its application. Throughout our presentation, we rely on a study of the effects of human activities on wetland ecosystems to make our description of methodology more tangible. We begin by presenting the fundamental principles of SEM, including both its distinguishing characteristics and the requirements for modeling hypotheses about causal networks. We then illustrate SEM procedures and offer guidelines for conducting SEM analyses. Our focus in this presentation is on basic modeling objectives and core techniques. Pointers to additional modeling options are also given.

Modeling Selection and Extinction Mechanisms of Biological Systems

NASA Astrophysics Data System (ADS)

Amirjanov, Adil

In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.

Statistical mechanical estimation of the free energy of formation of E. coli biomass for use with macroscopic bioreactor balances.

PubMed

Grosz, R; Stephanopoulos, G

1983-09-01

The need for the determination of the free energy of formation of biomass in bioreactor second law balances is well established. A statistical mechanical method for the calculation of the free energy of formation of E. coli biomass is introduced. In this method, biomass is modelled to consist of a system of biopolymer networks. The partition function of this system is proposed to consist of acoustic and optical modes of vibration. Acoustic modes are described by Tarasov's model, the parameters of which are evaluated with the aid of low-temperature calorimetric data for the crystalline protein bovine chymotrypsinogen A. The optical modes are described by considering the low-temperature thermodynamic properties of biological monomer crystals such as amino acid crystals. Upper and lower bounds are placed on the entropy to establish the maximum error associated with the statistical method. The upper bound is determined by endowing the monomers in biomass with ideal gas properties. The lower bound is obtained by limiting the monomers to complete immobility. On this basis, the free energy of formation is fixed to within 10%. Proposals are made with regard to experimental verification of the calculated value and extension of the calculation to other types of biomass.

Online and offline tools for head movement compensation in MEG.

PubMed

Stolk, Arjen; Todorovic, Ana; Schoffelen, Jan-Mathijs; Oostenveld, Robert

2013-03-01

Magnetoencephalography (MEG) is measured above the head, which makes it sensitive to variations of the head position with respect to the sensors. Head movements blur the topography of the neuronal sources of the MEG signal, increase localization errors, and reduce statistical sensitivity. Here we describe two novel and readily applicable methods that compensate for the detrimental effects of head motion on the statistical sensitivity of MEG experiments. First, we introduce an online procedure that continuously monitors head position. Second, we describe an offline analysis method that takes into account the head position time-series. We quantify the performance of these methods in the context of three different experimental settings, involving somatosensory, visual and auditory stimuli, assessing both individual and group-level statistics. The online head localization procedure allowed for optimal repositioning of the subjects over multiple sessions, resulting in a 28% reduction of the variance in dipole position and an improvement of up to 15% in statistical sensitivity. Offline incorporation of the head position time-series into the general linear model resulted in improvements of group-level statistical sensitivity between 15% and 29%. These tools can substantially reduce the influence of head movement within and between sessions, increasing the sensitivity of many cognitive neuroscience experiments. Copyright © 2012 Elsevier Inc. All rights reserved.

Modelling 1-minute directional observations of the global irradiance.

NASA Astrophysics Data System (ADS)

Thejll, Peter; Pagh Nielsen, Kristian; Andersen, Elsa; Furbo, Simon

2016-04-01

Direct and diffuse irradiances from the sky has been collected at 1-minute intervals for about a year from the experimental station at the Technical University of Denmark for the IEA project "Solar Resource Assessment and Forecasting". These data were gathered by pyrheliometers tracking the Sun, as well as with apertured pyranometers gathering 1/8th and 1/16th of the light from the sky in 45 degree azimuthal ranges pointed around the compass. The data are gathered in order to develop detailed models of the potentially available solar energy and its variations at high temporal resolution in order to gain a more detailed understanding of the solar resource. This is important for a better understanding of the sub-grid scale cloud variation that cannot be resolved with climate and weather models. It is also important for optimizing the operation of active solar energy systems such as photovoltaic plants and thermal solar collector arrays, and for passive solar energy and lighting to buildings. We present regression-based modelling of the observed data, and focus, here, on the statistical properties of the model fits. Using models based on the one hand on what is found in the literature and on physical expectations, and on the other hand on purely statistical models, we find solutions that can explain up to 90% of the variance in global radiation. The models leaning on physical insights include terms for the direct solar radiation, a term for the circum-solar radiation, a diffuse term and a term for the horizon brightening/darkening. The purely statistical model is found using data- and formula-validation approaches picking model expressions from a general catalogue of possible formulae. The method allows nesting of expressions, and the results found are dependent on and heavily constrained by the cross-validation carried out on statistically independent testing and training data-sets. Slightly better fits -- in terms of variance explained -- is found using the purely statistical fitting/searching approach. We describe the methods applied, results found, and discuss the different potentials of the physics- and statistics-only based model-searches.

Quantum statistics in complex networks

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra

The Barabasi-Albert (BA) model for a complex network shows a characteristic power law connectivity distribution typical of scale free systems. The Ising model on the BA network shows that the ferromagnetic phase transition temperature depends logarithmically on its size. We have introduced a fitness parameter for the BA network which describes the different abilities of nodes to compete for links. This model predicts the formation of a scale free network where each node increases its connectivity in time as a power-law with an exponent depending on its fitness. This model includes the fact that the node connectivity and growth rate do not depend on the node age alone and it reproduces non trivial correlation properties of the Internet. We have proposed a model of bosonic networks by a generalization of the BA model where the properties of quantum statistics can be applied. We have introduced a fitness eta i = e-bei where the temperature T = 1/ b is determined by the noise in the system and the energy ei accounts for qualitative differences of each node for acquiring links. The results of this work show that a power law network with exponent gamma = 2 can give a Bose condensation where a single node grabs a finite fraction of all the links. In order to address the connection with self-organized processes we have introduced a model for a growing Cayley tree that generalizes the dynamics of invasion percolation. At each node we associate a parameter ei (called energy) such that the probability to grow for each node is given by pii ∝ ebei where T = 1/ b is a statistical parameter of the system determined by the noise called the temperature. This model has been solved analytically with a similar mathematical technique as the bosonic scale-free networks and it shows the self organization of the low energy nodes at the interface. In the thermodynamic limit the Fermi distribution describes the probability of the energy distribution at the interface.

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

PubMed Central

Malosetti, Marcos; Ribaut, Jean-Marcel; van Eeuwijk, Fred A.

2013-01-01

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.” PMID:23487515

Linear fitting of multi-threshold counting data with a pixel-array detector for spectral X-ray imaging

PubMed Central

Muir, Ryan D.; Pogranichney, Nicholas R.; Muir, J. Lewis; Sullivan, Shane Z.; Battaile, Kevin P.; Mulichak, Anne M.; Toth, Scott J.; Keefe, Lisa J.; Simpson, Garth J.

2014-01-01

Experiments and modeling are described to perform spectral fitting of multi-threshold counting measurements on a pixel-array detector. An analytical model was developed for describing the probability density function of detected voltage in X-ray photon-counting arrays, utilizing fractional photon counting to account for edge/corner effects from voltage plumes that spread across multiple pixels. Each pixel was mathematically calibrated by fitting the detected voltage distributions to the model at both 13.5 keV and 15.0 keV X-ray energies. The model and established pixel responses were then exploited to statistically recover images of X-ray intensity as a function of X-ray energy in a simulated multi-wavelength and multi-counting threshold experiment. PMID:25178010

Linear fitting of multi-threshold counting data with a pixel-array detector for spectral X-ray imaging.

PubMed

Muir, Ryan D; Pogranichney, Nicholas R; Muir, J Lewis; Sullivan, Shane Z; Battaile, Kevin P; Mulichak, Anne M; Toth, Scott J; Keefe, Lisa J; Simpson, Garth J

2014-09-01

Experiments and modeling are described to perform spectral fitting of multi-threshold counting measurements on a pixel-array detector. An analytical model was developed for describing the probability density function of detected voltage in X-ray photon-counting arrays, utilizing fractional photon counting to account for edge/corner effects from voltage plumes that spread across multiple pixels. Each pixel was mathematically calibrated by fitting the detected voltage distributions to the model at both 13.5 keV and 15.0 keV X-ray energies. The model and established pixel responses were then exploited to statistically recover images of X-ray intensity as a function of X-ray energy in a simulated multi-wavelength and multi-counting threshold experiment.

A κ-generalized statistical mechanics approach to income analysis

NASA Astrophysics Data System (ADS)

Clementi, F.; Gallegati, M.; Kaniadakis, G.

2009-02-01

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.

Irradiation-hyperthermia in canine hemangiopericytomas: large-animal model for therapeutic response.

PubMed

Richardson, R C; Anderson, V L; Voorhees, W D; Blevins, W E; Inskeep, T K; Janas, W; Shupe, R E; Babbs, C F

1984-11-01

Results of irradiation-hyperthermia treatment in 11 dogs with naturally occurring hemangiopericytoma were reported. Similarities of canine and human hemangiopericytomas were described. Orthovoltage X-irradiation followed by microwave-induced hyperthermia resulted in a 91% objective response rate. A statistical procedure was given to evaluate quantitatively the clinical behavior of locally invasive, nonmetastatic tumors in dogs that were undergoing therapy for control of local disease. The procedure used a small sample size and demonstrated distribution of the data on a scaled response as well as transformation of the data through classical parametric and nonparametric statistical methods. These statistical methods set confidence limits on the population mean and placed tolerance limits on a population percentage. Application of the statistical methods to human and animal clinical trials was apparent.

Cosmological Constraints from Fourier Phase Statistics

NASA Astrophysics Data System (ADS)

Ali, Kamran; Obreschkow, Danail; Howlett, Cullan; Bonvin, Camille; Llinares, Claudio; Oliveira Franco, Felipe; Power, Chris

2018-06-01

Most statistical inference from cosmic large-scale structure relies on two-point statistics, i.e. on the galaxy-galaxy correlation function (2PCF) or the power spectrum. These statistics capture the full information encoded in the Fourier amplitudes of the galaxy density field but do not describe the Fourier phases of the field. Here, we quantify the information contained in the line correlation function (LCF), a three-point Fourier phase correlation function. Using cosmological simulations, we estimate the Fisher information (at redshift z = 0) of the 2PCF, LCF and their combination, regarding the cosmological parameters of the standard ΛCDM model, as well as a Warm Dark Matter (WDM) model and the f(R) and Symmetron modified gravity models. The galaxy bias is accounted for at the level of a linear bias. The relative information of the 2PCF and the LCF depends on the survey volume, sampling density (shot noise) and the bias uncertainty. For a volume of 1h^{-3}Gpc^3, sampled with points of mean density \\bar{n} = 2× 10^{-3} h3 Mpc^{-3} and a bias uncertainty of 13%, the LCF improves the parameter constraints by about 20% in the ΛCDM cosmology and potentially even more in alternative models. Finally, since a linear bias only affects the Fourier amplitudes (2PCF), but not the phases (LCF), the combination of the 2PCF and the LCF can be used to break the degeneracy between the linear bias and σ8, present in 2-point statistics.

The reservoir model: a differential equation model of psychological regulation.

PubMed

Deboeck, Pascal R; Bergeman, C S

2013-06-01

Differential equation models can be used to describe the relationships between the current state of a system of constructs (e.g., stress) and how those constructs are changing (e.g., based on variable-like experiences). The following article describes a differential equation model based on the concept of a reservoir. With a physical reservoir, such as one for water, the level of the liquid in the reservoir at any time depends on the contributions to the reservoir (inputs) and the amount of liquid removed from the reservoir (outputs). This reservoir model might be useful for constructs such as stress, where events might "add up" over time (e.g., life stressors, inputs), but individuals simultaneously take action to "blow off steam" (e.g., engage coping resources, outputs). The reservoir model can provide descriptive statistics of the inputs that contribute to the "height" (level) of a construct and a parameter that describes a person's ability to dissipate the construct. After discussing the model, we describe a method of fitting the model as a structural equation model using latent differential equation modeling and latent distribution modeling. A simulation study is presented to examine recovery of the input distribution and output parameter. The model is then applied to the daily self-reports of negative affect and stress from a sample of older adults from the Notre Dame Longitudinal Study on Aging. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

The Reservoir Model: A Differential Equation Model of Psychological Regulation

PubMed Central

Deboeck, Pascal R.; Bergeman, C. S.

2017-01-01

Differential equation models can be used to describe the relationships between the current state of a system of constructs (e.g., stress) and how those constructs are changing (e.g., based on variable-like experiences). The following article describes a differential equation model based on the concept of a reservoir. With a physical reservoir, such as one for water, the level of the liquid in the reservoir at any time depends on the contributions to the reservoir (inputs) and the amount of liquid removed from the reservoir (outputs). This reservoir model might be useful for constructs such as stress, where events might “add up” over time (e.g., life stressors, inputs), but individuals simultaneously take action to “blow off steam” (e.g., engage coping resources, outputs). The reservoir model can provide descriptive statistics of the inputs that contribute to the “height” (level) of a construct and a parameter that describes a person's ability to dissipate the construct. After discussing the model, we describe a method of fitting the model as a structural equation model using latent differential equation modeling and latent distribution modeling. A simulation study is presented to examine recovery of the input distribution and output parameter. The model is then applied to the daily self-reports of negative affect and stress from a sample of older adults from the Notre Dame Longitudinal Study on Aging. PMID:23527605

Novel approaches to the study of particle dark matter in astrophysics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Argüelles, C. R., E-mail: carlos.arguelles@icranet.org; Ruffini, R., E-mail: ruffini@icra.it; Rueda, J. A., E-mail: jorge.rueda@icra.it

A deep understanding of the role of the dark matter in the different astrophysical scenarios of the local Universe such as galaxies, represent a crucial step to describe in a more consistent way the role of dark matter in cosmology. This kind of studies requires the interconnection between particle physics within and beyond the Standard Model, and fundamental physics such as thermodynamics and statistics, within a fully relativistic treatment of Gravity. After giving a comprehensive summary of the different types of dark matter and their role in astrophysics, we discuss the recent efforts in describing the distribution of dark mattermore » in the center and halo of galaxies from first principles such as gravitational interactions, quantum statistics and particle physics; and its implications with the observations.« less

Compromise decision support problems for hierarchical design involving uncertainty

NASA Astrophysics Data System (ADS)

Vadde, S.; Allen, J. K.; Mistree, F.

1994-08-01

In this paper an extension to the traditional compromise Decision Support Problem (DSP) formulation is presented. Bayesian statistics is used in the formulation to model uncertainties associated with the information being used. In an earlier paper a compromise DSP that accounts for uncertainty using fuzzy set theory was introduced. The Bayesian Decision Support Problem is described in this paper. The method for hierarchical design is demonstrated by using this formulation to design a portal frame. The results are discussed and comparisons are made with those obtained using the fuzzy DSP. Finally, the efficacy of incorporating Bayesian statistics into the traditional compromise DSP formulation is discussed and some pending research issues are described. Our emphasis in this paper is on the method rather than the results per se.

Crossed beam (E--VRT) energy transfer experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hertel, I.V.; Hofmann, H.; Rost, K.A.

A molecular crossed beam apparatus which has been developed to perform electronic-to-vibrational, rotational, translational (E--V,R,T) energy transfer studies is described. Its capabilities are illustrated on the basis of a number of energy transfer spectra obtained for collision systems of the type Na*+Mol(..nu..,j) ..-->..Na+Mol (..nu..',j') where Na* represents a laser excited sodium atom and Mol a diatomic or polyatomic molecule. Because of the lack of reliable dynamic theories on quenching processes, statistical approaches such as the ''linearly forced harmonic oscillator'' and ''prior distributions'' have been used to model the experimental spectra. The agreement is found to be satisfactory, so even suchmore » simple statistics may be useful to describe (E--V,R,T) energy transfer processes in collision systems with small molecules.« less

An optimization model to agroindustrial sector in antioquia (Colombia, South America)

NASA Astrophysics Data System (ADS)

Fernandez, J.

2015-06-01

This paper develops a proposal of a general optimization model for the flower industry, which is defined by using discrete simulation and nonlinear optimization, whose mathematical models have been solved by using ProModel simulation tools and Gams optimization. It defines the operations that constitute the production and marketing of the sector, statistically validated data taken directly from each operation through field work, the discrete simulation model of the operations and the linear optimization model of the entire industry chain are raised. The model is solved with the tools described above and presents the results validated in a case study.

Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

PubMed

Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

2014-01-01

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

Spacing distribution functions for 1D point island model with irreversible attachment

NASA Astrophysics Data System (ADS)

Gonzalez, Diego; Einstein, Theodore; Pimpinelli, Alberto

2011-03-01

We study the configurational structure of the point island model for epitaxial growth in one dimension. In particular, we calculate the island gap and capture zone distributions. Our model is based on an approximate description of nucleation inside the gaps. Nucleation is described by the joint probability density p xy n (x,y), which represents the probability density to have nucleation at position x within a gap of size y. Our proposed functional form for p xy n (x,y) describes excellently the statistical behavior of the system. We compare our analytical model with extensive numerical simulations. Our model retains the most relevant physical properties of the system. This work was supported by the NSF-MRSEC at the University of Maryland, Grant No. DMR 05-20471, with ancillary support from the Center for Nanophysics and Advanced Materials (CNAM).

Temporal Characteristics of Electron Flux Events at Geosynchronous Orbit

NASA Astrophysics Data System (ADS)

Olson, D. K.; Larsen, B.; Henderson, M. G.

2017-12-01

Geosynchronous satellites such as the LANL-GEO fleet are exposed to hazardous conditions when they encounter regions of hot, intense plasma such as that from the plasma sheet. These conditions can lead to the build-up of charge on the surface of a spacecraft, with undesired, and often dangerous, side effects. Observation of electron flux levels at geosynchronous orbit (GEO) with multiple satellites provides a unique view of plasma sheet access to that region. Flux "events", or periods when fluxes are elevated continuously above the LANL-GEO spacecraft charging threshold, can be characterized by duration in two dimensions: a spatial dimension of local time, describing the duration of an event from the perspective of a single spacecraft, and a temporal dimension describing the duration in time in which high energy plasma sheet particles have access to geosynchronous orbit. We examine the statistical properties of the temporal duration of 8 keV electron flux events at geosynchronous orbit over a twelve-year period. These results, coupled with the spatial duration characteristics, provide the key information needed to formulate a statistical model for forecasting the electron flux conditions at GEO that are correlated with LANL-GEO surface charging. Forecasting models are an essential component to understanding space weather and mitigating the dangers of surface charging on our satellites. We also examine the correlation of flux event durations with solar wind parameters and geomagnetic indices, identifying the data needed to improve upon a statistical forecasting model

Statistical estimation via convex optimization for trending and performance monitoring

NASA Astrophysics Data System (ADS)

Samar, Sikandar

This thesis presents an optimization-based statistical estimation approach to find unknown trends in noisy data. A Bayesian framework is used to explicitly take into account prior information about the trends via trend models and constraints. The main focus is on convex formulation of the Bayesian estimation problem, which allows efficient computation of (globally) optimal estimates. There are two main parts of this thesis. The first part formulates trend estimation in systems described by known detailed models as a convex optimization problem. Statistically optimal estimates are then obtained by maximizing a concave log-likelihood function subject to convex constraints. We consider the problem of increasing problem dimension as more measurements become available, and introduce a moving horizon framework to enable recursive estimation of the unknown trend by solving a fixed size convex optimization problem at each horizon. We also present a distributed estimation framework, based on the dual decomposition method, for a system formed by a network of complex sensors with local (convex) estimation. Two specific applications of the convex optimization-based Bayesian estimation approach are described in the second part of the thesis. Batch estimation for parametric diagnostics in a flight control simulation of a space launch vehicle is shown to detect incipient fault trends despite the natural masking properties of feedback in the guidance and control loops. Moving horizon approach is used to estimate time varying fault parameters in a detailed nonlinear simulation model of an unmanned aerial vehicle. An excellent performance is demonstrated in the presence of winds and turbulence.

Ordered phase and non-equilibrium fluctuation in stock market

NASA Astrophysics Data System (ADS)

Maskawa, Jun-ichi

2002-08-01

We analyze the statistics of daily price change of stock market in the framework of a statistical physics model for the collective fluctuation of stock portfolio. In this model the time series of price changes are coded into the sequences of up and down spins, and the Hamiltonian of the system is expressed by spin-spin interactions as in spin glass models of disordered magnetic systems. Through the analysis of Dow-Jones industrial portfolio consisting of 30 stock issues by this model, we find a non-equilibrium fluctuation mode on the point slightly below the boundary between ordered and disordered phases. The remaining 29 modes are still in disordered phase and well described by Gibbs distribution. The variance of the fluctuation is outlined by the theoretical curve and peculiarly large in the non-equilibrium mode compared with those in the other modes remaining in ordinary phase.

Simulation program for estimating statistical power of Cox's proportional hazards model assuming no specific distribution for the survival time.

PubMed

Akazawa, K; Nakamura, T; Moriguchi, S; Shimada, M; Nose, Y

1991-07-01

Small sample properties of the maximum partial likelihood estimates for Cox's proportional hazards model depend on the sample size, the true values of regression coefficients, covariate structure, censoring pattern and possibly baseline hazard functions. Therefore, it would be difficult to construct a formula or table to calculate the exact power of a statistical test for the treatment effect in any specific clinical trial. The simulation program, written in SAS/IML, described in this paper uses Monte-Carlo methods to provide estimates of the exact power for Cox's proportional hazards model. For illustrative purposes, the program was applied to real data obtained from a clinical trial performed in Japan. Since the program does not assume any specific function for the baseline hazard, it is, in principle, applicable to any censored survival data as long as they follow Cox's proportional hazards model.

Nonequilibrium critical behavior of model statistical systems and methods for the description of its features

NASA Astrophysics Data System (ADS)

Prudnikov, V. V.; Prudnikov, P. V.; Mamonova, M. V.

2017-11-01

This paper reviews features in critical behavior of far-from-equilibrium macroscopic systems and presents current methods of describing them by referring to some model statistical systems such as the three-dimensional Ising model and the two-dimensional XY model. The paper examines the critical relaxation of homogeneous and structurally disordered systems subjected to abnormally strong fluctuation effects involved in ordering processes in solids at second-order phase transitions. Interest in such systems is due to the aging properties and fluctuation-dissipation theorem violations predicted for and observed in systems slowly evolving from a nonequilibrium initial state. It is shown that these features of nonequilibrium behavior show up in the magnetic properties of magnetic superstructures consisting of alternating nanoscale-thick magnetic and nonmagnetic layers and can be observed not only near the film’s critical ferromagnetic ordering temperature Tc, but also over the wide temperature range T ⩽ Tc.

Adsorption of diclofenac and nimesulide on activated carbon: Statistical physics modeling and effect of adsorbate size

NASA Astrophysics Data System (ADS)

Sellaoui, Lotfi; Mechi, Nesrine; Lima, Éder Cláudio; Dotto, Guilherme Luiz; Ben Lamine, Abdelmottaleb

2017-10-01

Based on statistical physics elements, the equilibrium adsorption of diclofenac (DFC) and nimesulide (NM) on activated carbon was analyzed by a multilayer model with saturation. The paper aimed to describe experimentally and theoretically the adsorption process and study the effect of adsorbate size using the model parameters. From numerical simulation, the number of molecules per site showed that the adsorbate molecules (DFC and NM) were mostly anchored in both sides of the pore walls. The receptor sites density increase suggested that additional sites appeared during the process, to participate in DFC and NM adsorption. The description of the adsorption energy behavior indicated that the process was physisorption. Finally, by a model parameters correlation, the size effect of the adsorbate was deduced indicating that the molecule dimension has a negligible effect on the DFC and NM adsorption.

A Three-Dimensional Statistical Average Skull: Application of Biometric Morphing in Generating Missing Anatomy.

PubMed

Teshima, Tara Lynn; Patel, Vaibhav; Mainprize, James G; Edwards, Glenn; Antonyshyn, Oleh M

2015-07-01

The utilization of three-dimensional modeling technology in craniomaxillofacial surgery has grown exponentially during the last decade. Future development, however, is hindered by the lack of a normative three-dimensional anatomic dataset and a statistical mean three-dimensional virtual model. The purpose of this study is to develop and validate a protocol to generate a statistical three-dimensional virtual model based on a normative dataset of adult skulls. Two hundred adult skull CT images were reviewed. The average three-dimensional skull was computed by processing each CT image in the series using thin-plate spline geometric morphometric protocol. Our statistical average three-dimensional skull was validated by reconstructing patient-specific topography in cranial defects. The experiment was repeated 4 times. In each case, computer-generated cranioplasties were compared directly to the original intact skull. The errors describing the difference between the prediction and the original were calculated. A normative database of 33 adult human skulls was collected. Using 21 anthropometric landmark points, a protocol for three-dimensional skull landmarking and data reduction was developed and a statistical average three-dimensional skull was generated. Our results show the root mean square error (RMSE) for restoration of a known defect using the native best match skull, our statistical average skull, and worst match skull was 0.58, 0.74, and 4.4  mm, respectively. The ability to statistically average craniofacial surface topography will be a valuable instrument for deriving missing anatomy in complex craniofacial defects and deficiencies as well as in evaluating morphologic results of surgery.

The International Provision and Supply of Publications.

ERIC Educational Resources Information Center

Line, Maurice B.; And Others

As part of a Universal Availability of Publications (UAP) program, this report describes the current situation in international interlending and possible future models. From a review of literature and statistics previously collected, and a 1979 study of 15 international supply centers, it is concluded that international loan demand is increasing…

Introducing Simulation via the Theory of Records

ERIC Educational Resources Information Center

Johnson, Arvid C.

2011-01-01

While spreadsheet simulation can be a useful method by which to help students to understand some of the more advanced concepts in an introductory statistics course, introducing the simulation methodology at the same time as these concepts can result in student cognitive overload. This article describes a spreadsheet model that has been…

Circulation Clusters--An Empirical Approach to Decentralization of Academic Libraries.

ERIC Educational Resources Information Center

McGrath, William E.

1986-01-01

Discusses the issue of centralization or decentralization of academic library collections, and describes a statistical analysis of book circulation at the University of Southwestern Louisiana that yielded subject area clusters as a compromise solution to the problem. Applications of the cluster model for all types of library catalogs are…

Multi-Parameter Linear Least-Squares Fitting to Poisson Data One Count at a Time

NASA Technical Reports Server (NTRS)

Wheaton, W.; Dunklee, A.; Jacobson, A.; Ling, J.; Mahoney, W.; Radocinski, R.

1993-01-01

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multi-component linear model, with underlying physical count rates or fluxes which are to be estimated from the data.

Assessing the Growth of Gifted Students

ERIC Educational Resources Information Center

McCoach, D. Betsy; Rambo, Karen E.; Welsh, Megan

2013-01-01

This Methodological Brief gives an overview of statistical methods used to gauge academic growth and discusses issues surrounding the measurement of growth in gifted populations. To illustrate some of these issues, we describe a growth model that examines differences in summer lag between gifted and nongifted students. We also provide…

Statistical Analysis of PDF's for Na Released by Photons from Solid Surfaces

NASA Astrophysics Data System (ADS)

Gamborino, D.; Wurz, P.

2018-05-01

We analyse the adequacy of three model speed PDF's previously used to describe the desorption of Na from a solid surface either by ESD or PSD. We found that the Maxwell PDF is too wide compared to measurements and non-thermal PDF's are better suited.

Two Applications of Simulation in the Educational Environment. Tech Memo.

ERIC Educational Resources Information Center

Thomas, David B.

Two educational computer simulations are described in this paper. One of the simulations is STATSIM, a series of exercises applicable to statistical instruction. The content of the other simulation is comprised of mathematical learning models. Student involvement, the interactive nature of the simulations, and terminal display of materials are…

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

A Computer Program for the Generation of ARIMA Data

ERIC Educational Resources Information Center

Green, Samuel B.; Noles, Keith O.

1977-01-01

The autoregressive integrated moving averages model (ARIMA) has been applied to time series data in psychological and educational research. A program is described that generates ARIMA data of a known order. The program enables researchers to explore statistical properties of ARIMA data and simulate systems producing time dependent observations.…

Standard Operating Procedures for Collecting Data from Local Education Agencies.

ERIC Educational Resources Information Center

McElreath, Nancy R., Ed.

A systematic approach to planning and presenting the data collection activities of a State Department of Education is described. The Information Communication System, a model communication system used by the state of New Jersey, conveys narrative and statistical information relating to a school district's students, teachers, finances, facilities…

Quantum Statistical Mechanics on a Quantum Computer

NASA Astrophysics Data System (ADS)

Raedt, H. D.; Hams, A. H.; Michielsen, K.; Miyashita, S.; Saito, K.

We describe a quantum algorithm to compute the density of states and thermal equilibrium properties of quantum many-body systems. We present results obtained by running this algorithm on a software implementation of a 21-qubit quantum computer for the case of an antiferromagnetic Heisenberg model on triangular lattices of different size.

Counting statistics for genetic switches based on effective interaction approximation

NASA Astrophysics Data System (ADS)

Ohkubo, Jun

2012-09-01

Applicability of counting statistics for a system with an infinite number of states is investigated. The counting statistics has been studied a lot for a system with a finite number of states. While it is possible to use the scheme in order to count specific transitions in a system with an infinite number of states in principle, we have non-closed equations in general. A simple genetic switch can be described by a master equation with an infinite number of states, and we use the counting statistics in order to count the number of transitions from inactive to active states in the gene. To avoid having the non-closed equations, an effective interaction approximation is employed. As a result, it is shown that the switching problem can be treated as a simple two-state model approximately, which immediately indicates that the switching obeys non-Poisson statistics.

Model of aircraft passenger acceptance

NASA Technical Reports Server (NTRS)

Jacobson, I. D.

1978-01-01

A technique developed to evaluate the passenger response to a transportation system environment is described. Reactions to motion, noise, temperature, seating, ventilation, sudden jolts and descents are modeled. Statistics are presented for the age, sex, occupation, and income distributions of the candidates analyzed. Values are noted for the relative importance of system variables such as time savings, on-time arrival, convenience, comfort, safety, the ability to read and write, and onboard services.

An Extensible NetLogo Model for Visualizing Message Routing Protocols

DTIC Science & Technology

2017-08-01

the hard sciences to the social sciences to computer-generated art. NetLogo represents the world as a set of...describe the model is shown here; for the supporting methods , refer to the source code. Approved for public release; distribution is unlimited. 4 iv...if ticks - last-inject > time-to-inject [inject] if run# > #runs [stop] end Next, we present some basic statistics collected for the

Widening Disparity and its Suppression in a Stochastic Replicator Model

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu

2016-04-01

Winner-take-all phenomena are observed in various competitive systems. We find similar phenomena in replicator models with randomly fluctuating growth rates. The disparity between winners and losers increases indefinitely, even if all elements are statistically equivalent. A lognormal distribution describes well the nonstationary time evolution. If a nonlinear load corresponding to progressive taxation is introduced, a stationary distribution is obtained and disparity widening is suppressed.

An innovative statistical approach for analysing non-continuous variables in environmental monitoring: assessing temporal trends of TBT pollution.

PubMed

Santos, José António; Galante-Oliveira, Susana; Barroso, Carlos

2011-03-01

The current work presents an innovative statistical approach to model ordinal variables in environmental monitoring studies. An ordinal variable has values that can only be compared as "less", "equal" or "greater" and it is not possible to have information about the size of the difference between two particular values. The example of ordinal variable under this study is the vas deferens sequence (VDS) used in imposex (superimposition of male sexual characters onto prosobranch females) field assessment programmes for monitoring tributyltin (TBT) pollution. The statistical methodology presented here is the ordered logit regression model. It assumes that the VDS is an ordinal variable whose values match up a process of imposex development that can be considered continuous in both biological and statistical senses and can be described by a latent non-observable continuous variable. This model was applied to the case study of Nucella lapillus imposex monitoring surveys conducted in the Portuguese coast between 2003 and 2008 to evaluate the temporal evolution of TBT pollution in this country. In order to produce more reliable conclusions, the proposed model includes covariates that may influence the imposex response besides TBT (e.g. the shell size). The model also provides an analysis of the environmental risk associated to TBT pollution by estimating the probability of the occurrence of females with VDS ≥ 2 in each year, according to OSPAR criteria. We consider that the proposed application of this statistical methodology has a great potential in environmental monitoring whenever there is the need to model variables that can only be assessed through an ordinal scale of values.

Using mark-recapture distance sampling methods on line transect surveys

USGS Publications Warehouse

Burt, Louise M.; Borchers, David L.; Jenkins, Kurt J.; Marques, Tigao A

2014-01-01

Synthesis and applications. Mark–recapture DS is a widely used method for estimating animal density and abundance when detection of animals at distance zero is not certain. Two observer configurations and three statistical models are described, and it is important to choose the most appropriate model for the observer configuration and target species in question. By way of making the methods more accessible to practicing ecologists, we describe the key ideas underlying MRDS methods, the sometimes subtle differences between them, and we illustrate these by applying different kinds of MRDS method to surveys of two different target species using different survey configurations.

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

PubMed

Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H

2016-10-01

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blume-Kohout, Robin J; Scholten, Travis L.

Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) shouldmore » not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.« less

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Impact of Temperature and Non-Gaussian Statistics on Electron Transfer in Donor-Bridge-Acceptor Molecules.

PubMed

Waskasi, Morteza M; Newton, Marshall D; Matyushov, Dmitry V

2017-03-30

A combination of experimental data and theoretical analysis provides evidence of a bell-shaped kinetics of electron transfer in the Arrhenius coordinates ln k vs 1/T. This kinetic law is a temperature analogue of the familiar Marcus bell-shaped dependence based on ln k vs the reaction free energy. These results were obtained for reactions of intramolecular charge shift between the donor and acceptor separated by a rigid spacer studied experimentally by Miller and co-workers. The non-Arrhenius kinetic law is a direct consequence of the solvent reorganization energy and reaction driving force changing approximately as hyperbolic functions with temperature. The reorganization energy decreases and the driving force increases when temperature is increased. The point of equality between them marks the maximum of the activationless reaction rate. Reaching the consistency between the kinetic and thermodynamic experimental data requires the non-Gaussian statistics of the donor-acceptor energy gap described by the Q-model of electron transfer. The theoretical formalism combines the vibrational envelope of quantum vibronic transitions with the Q-model describing the classical component of the Franck-Condon factor and a microscopic solvation model of the solvent reorganization energy and the reaction free energy.

Quantum description of light propagation in generalized media

NASA Astrophysics Data System (ADS)

Häyrynen, Teppo; Oksanen, Jani

2016-02-01

Linear quantum input-output relation based models are widely applied to describe the light propagation in a lossy medium. The details of the interaction and the associated added noise depend on whether the device is configured to operate as an amplifier or an attenuator. Using the traveling wave (TW) approach, we generalize the linear material model to simultaneously account for both the emission and absorption processes and to have point-wise defined noise field statistics and intensity dependent interaction strengths. Thus, our approach describes the quantum input-output relations of linear media with net attenuation, amplification or transparency without pre-selection of the operation point. The TW approach is then applied to investigate materials at thermal equilibrium, inverted materials, the transparency limit where losses are compensated, and the saturating amplifiers. We also apply the approach to investigate media in nonuniform states which can be e.g. consequences of a temperature gradient over the medium or a position dependent inversion of the amplifier. Furthermore, by using the generalized model we investigate devices with intensity dependent interactions and show how an initial thermal field transforms to a field having coherent statistics due to gain saturation.

Chern-Simons Term: Theory and Applications.

NASA Astrophysics Data System (ADS)

Gupta, Kumar Sankar

1992-01-01

We investigate the quantization and applications of Chern-Simons theories to several systems of interest. Elementary canonical methods are employed for the quantization of abelian and nonabelian Chern-Simons actions using ideas from gauge theories and quantum gravity. When the spatial slice is a disc, it yields quantum states at the edge of the disc carrying a representation of the Kac-Moody algebra. We next include sources in this model and their quantum states are shown to be those of a conformal family. Vertex operators for both abelian and nonabelian sources are constructed. The regularized abelian Wilson line is proved to be a vertex operator. The spin-statistics theorem is established for Chern-Simons dynamics using purely geometrical techniques. Chern-Simons action is associated with exotic spin and statistics in 2 + 1 dimensions. We study several systems in which the Chern-Simons action affects the spin and statistics. The first class of systems we study consist of G/H models. The solitons of these models are shown to obey anyonic statistics in the presence of a Chern-Simons term. The second system deals with the effect of the Chern -Simons term in a model for high temperature superconductivity. The coefficient of the Chern-Simons term is shown to be quantized, one of its possible values giving fermionic statistics to the solitons of this model. Finally, we study a system of spinning particles interacting with 2 + 1 gravity, the latter being described by an ISO(2,1) Chern-Simons term. An effective action for the particles is obtained by integrating out the gauge fields. Next we construct operators which exchange the particles. They are shown to satisfy the braid relations. There are ambiguities in the quantization of this system which can be exploited to give anyonic statistics to the particles. We also point out that at the level of the first quantized theory, the usual spin-statistics relation need not apply to these particles.

Occupational Injury and Illness Surveillance: Conceptual Filters Explain Underreporting

PubMed Central

Azaroff, Lenore S.; Levenstein, Charles; Wegman, David H.

2002-01-01

Occupational health surveillance data are key to effective intervention. However, the US Bureau of Labor Statistics survey significantly underestimates the incidence of work-related injuries and illnesses. Researchers supplement these statistics with data from other systems not designed for surveillance. The authors apply the filter model of Webb et al. to underreporting by the Bureau of Labor Statistics, workers’ compensation wage-replacement documents, physician reporting systems, and medical records of treatment charged to workers’ compensation. Mechanisms are described for the loss of cases at successive steps of documentation. Empirical findings indicate that workers repeatedly risk adverse consequences for attempting to complete these steps, while systems for ensuring their completion are weak or absent. PMID:12197968

Statistical procedures for analyzing mental health services data.

PubMed

Elhai, Jon D; Calhoun, Patrick S; Ford, Julian D

2008-08-15

In mental health services research, analyzing service utilization data often poses serious problems, given the presence of substantially skewed data distributions. This article presents a non-technical introduction to statistical methods specifically designed to handle the complexly distributed datasets that represent mental health service use, including Poisson, negative binomial, zero-inflated, and zero-truncated regression models. A flowchart is provided to assist the investigator in selecting the most appropriate method. Finally, a dataset of mental health service use reported by medical patients is described, and a comparison of results across several different statistical methods is presented. Implications of matching data analytic techniques appropriately with the often complexly distributed datasets of mental health services utilization variables are discussed.

Generating survival times to simulate Cox proportional hazards models with time-varying covariates.

PubMed

Austin, Peter C

2012-12-20

Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data-generating process: one must be able to simulate data from a specified statistical model. We describe data-generating processes for the Cox proportional hazards model with time-varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time-varying covariates: first, a dichotomous time-varying covariate that can change at most once from untreated to treated (e.g., organ transplant); second, a continuous time-varying covariate such as cumulative exposure at a constant dose to radiation or to a pharmaceutical agent used for a chronic condition; third, a dichotomous time-varying covariate with a subject being able to move repeatedly between treatment states (e.g., current compliance or use of a medication). In each setting, we derive closed-form expressions that allow one to simulate survival times so that survival times are related to a vector of fixed or time-invariant covariates and to a single time-varying covariate. We illustrate the utility of our closed-form expressions for simulating event times by using Monte Carlo simulations to estimate the statistical power to detect as statistically significant the effect of different types of binary time-varying covariates. This is compared with the statistical power to detect as statistically significant a binary time-invariant covariate. Copyright © 2012 John Wiley & Sons, Ltd.

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS

PubMed Central

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2013-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models. PMID:23935262

Statistical Irreversible Thermodynamics in the Framework of Zubarev's Nonequilibrium Statistical Operator Method

NASA Astrophysics Data System (ADS)

Luzzi, R.; Vasconcellos, A. R.; Ramos, J. G.; Rodrigues, C. G.

2018-01-01

We describe the formalism of statistical irreversible thermodynamics constructed based on Zubarev's nonequilibrium statistical operator (NSO) method, which is a powerful and universal tool for investigating the most varied physical phenomena. We present brief overviews of the statistical ensemble formalism and statistical irreversible thermodynamics. The first can be constructed either based on a heuristic approach or in the framework of information theory in the Jeffreys-Jaynes scheme of scientific inference; Zubarev and his school used both approaches in formulating the NSO method. We describe the main characteristics of statistical irreversible thermodynamics and discuss some particular considerations of several authors. We briefly describe how Rosenfeld, Bohr, and Prigogine proposed to derive a thermodynamic uncertainty principle.

Sample Skewness as a Statistical Measurement of Neuronal Tuning Sharpness

PubMed Central

Samonds, Jason M.; Potetz, Brian R.; Lee, Tai Sing

2014-01-01

We propose using the statistical measurement of the sample skewness of the distribution of mean firing rates of a tuning curve to quantify sharpness of tuning. For some features, like binocular disparity, tuning curves are best described by relatively complex and sometimes diverse functions, making it difficult to quantify sharpness with a single function and parameter. Skewness provides a robust nonparametric measure of tuning curve sharpness that is invariant with respect to the mean and variance of the tuning curve and is straightforward to apply to a wide range of tuning, including simple orientation tuning curves and complex object tuning curves that often cannot even be described parametrically. Because skewness does not depend on a specific model or function of tuning, it is especially appealing to cases of sharpening where recurrent interactions among neurons produce sharper tuning curves that deviate in a complex manner from the feedforward function of tuning. Since tuning curves for all neurons are not typically well described by a single parametric function, this model independence additionally allows skewness to be applied to all recorded neurons, maximizing the statistical power of a set of data. We also compare skewness with other nonparametric measures of tuning curve sharpness and selectivity. Compared to these other nonparametric measures tested, skewness is best used for capturing the sharpness of multimodal tuning curves defined by narrow peaks (maximum) and broad valleys (minima). Finally, we provide a more formal definition of sharpness using a shape-based information gain measure and derive and show that skewness is correlated with this definition. PMID:24555451

Comment on 'Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: feasibility studies for range verification'.

PubMed

Sitek, Arkadiusz

2016-12-21

The origin ensemble (OE) algorithm is a new method used for image reconstruction from nuclear tomographic data. The main advantage of this algorithm is the ease of implementation for complex tomographic models and the sound statistical theory. In this comment, the author provides the basics of the statistical interpretation of OE and gives suggestions for the improvement of the algorithm in the application to prompt gamma imaging as described in Polf et al (2015 Phys. Med. Biol. 60 7085).

Comment on ‘Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: feasibility studies for range verification’

NASA Astrophysics Data System (ADS)

Sitek, Arkadiusz

2016-12-01

The origin ensemble (OE) algorithm is a new method used for image reconstruction from nuclear tomographic data. The main advantage of this algorithm is the ease of implementation for complex tomographic models and the sound statistical theory. In this comment, the author provides the basics of the statistical interpretation of OE and gives suggestions for the improvement of the algorithm in the application to prompt gamma imaging as described in Polf et al (2015 Phys. Med. Biol. 60 7085).

Properties of a memory network in psychology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wedemann, Roseli S.; Donangelo, Raul; Carvalho, Luis A. V. de

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

Properties of a memory network in psychology

NASA Astrophysics Data System (ADS)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2007-12-01

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

Impact of systematic uncertainties for the CP violation measurement in superbeam experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meloni, Davide

We present a three-flavour fit to the recent ν{sub µ} → ν{sub e} T2K oscillation data with different models for the neutrino-nucleus cross section. We show that, even for a limited statistics, the allowed regions and best fit points in the (θ{sub 13}, δ{sub CP}) plane are affected if, instead of using the Fermi Gas model to describe the quasielastic cross section, we employ a model including the multinucleon emission channel [1].

Statistical and temporal irradiance fluctuations modeling for a ground-to-geostationary satellite optical link.

PubMed

Camboulives, A-R; Velluet, M-T; Poulenard, S; Saint-Antonin, L; Michau, V

2018-02-01

An optical communication link performance between the ground and a geostationary satellite can be impaired by scintillation, beam wandering, and beam spreading due to its propagation through atmospheric turbulence. These effects on the link performance can be mitigated by tracking and error correction codes coupled with interleaving. Precise numerical tools capable of describing the irradiance fluctuations statistically and of creating an irradiance time series are needed to characterize the benefits of these techniques and optimize them. The wave optics propagation methods have proven their capability of modeling the effects of atmospheric turbulence on a beam, but these are known to be computationally intensive. We present an analytical-numerical model which provides good results on the probability density functions of irradiance fluctuations as well as a time series with an important saving of time and computational resources.

Computing maximum-likelihood estimates for parameters of the National Descriptive Model of Mercury in Fish

USGS Publications Warehouse

Donato, David I.

2012-01-01

This report presents the mathematical expressions and the computational techniques required to compute maximum-likelihood estimates for the parameters of the National Descriptive Model of Mercury in Fish (NDMMF), a statistical model used to predict the concentration of methylmercury in fish tissue. The expressions and techniques reported here were prepared to support the development of custom software capable of computing NDMMF parameter estimates more quickly and using less computer memory than is currently possible with available general-purpose statistical software. Computation of maximum-likelihood estimates for the NDMMF by numerical solution of a system of simultaneous equations through repeated Newton-Raphson iterations is described. This report explains the derivation of the mathematical expressions required for computational parameter estimation in sufficient detail to facilitate future derivations for any revised versions of the NDMMF that may be developed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewis, John R.; Brooks, Dusty Marie

In pressurized water reactors, the prevention, detection, and repair of cracks within dissimilar metal welds is essential to ensure proper plant functionality and safety. Weld residual stresses, which are difficult to model and cannot be directly measured, contribute to the formation and growth of cracks due to primary water stress corrosion cracking. Additionally, the uncertainty in weld residual stress measurements and modeling predictions is not well understood, further complicating the prediction of crack evolution. The purpose of this document is to develop methodology to quantify the uncertainty associated with weld residual stress that can be applied to modeling predictions andmore » experimental measurements. Ultimately, the results can be used to assess the current state of uncertainty and to build confidence in both modeling and experimental procedures. The methodology consists of statistically modeling the variation in the weld residual stress profiles using functional data analysis techniques. Uncertainty is quantified using statistical bounds (e.g. confidence and tolerance bounds) constructed with a semi-parametric bootstrap procedure. Such bounds describe the range in which quantities of interest, such as means, are expected to lie as evidenced by the data. The methodology is extended to provide direct comparisons between experimental measurements and modeling predictions by constructing statistical confidence bounds for the average difference between the two quantities. The statistical bounds on the average difference can be used to assess the level of agreement between measurements and predictions. The methodology is applied to experimental measurements of residual stress obtained using two strain relief measurement methods and predictions from seven finite element models developed by different organizations during a round robin study.« less

Empirical Reference Distributions for Networks of Different Size

PubMed Central

Smith, Anna; Calder, Catherine A.; Browning, Christopher R.

2016-01-01

Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics, i.e. graph-level measures of network structural features, across multiple networks that differ in size. Although “normalized” versions of some network statistics exist, we demonstrate via simulation why direct comparison is often inappropriate. We consider normalizing network statistics relative to a simple fully parameterized reference distribution and demonstrate via simulation how this is an improvement over direct comparison, but still sometimes problematic. We propose a new adjustment method based on a reference distribution constructed as a mixture model of random graphs which reflect the dependence structure exhibited in the observed networks. We show that using simple Bernoulli models as mixture components in this reference distribution can provide adjusted network statistics that are relatively comparable across different network sizes but still describe interesting features of networks, and that this can be accomplished at relatively low computational expense. Finally, we apply this methodology to a collection of ecological networks derived from the Los Angeles Family and Neighborhood Survey activity location data. PMID:27721556

Topological characterization of antireflective and hydrophobic rough surfaces: are random process theory and fractal modeling applicable?

NASA Astrophysics Data System (ADS)

Borri, Claudia; Paggi, Marco

2015-02-01

The random process theory (RPT) has been widely applied to predict the joint probability distribution functions (PDFs) of asperity heights and curvatures of rough surfaces. A check of the predictions of RPT against the actual statistics of numerically generated random fractal surfaces and of real rough surfaces has been only partially undertaken. The present experimental and numerical study provides a deep critical comparison on this matter, providing some insight into the capabilities and limitations in applying RPT and fractal modeling to antireflective and hydrophobic rough surfaces, two important types of textured surfaces. A multi-resolution experimental campaign using a confocal profilometer with different lenses is carried out and a comprehensive software for the statistical description of rough surfaces is developed. It is found that the topology of the analyzed textured surfaces cannot be fully described according to RPT and fractal modeling. The following complexities emerge: (i) the presence of cut-offs or bi-fractality in the power-law power-spectral density (PSD) functions; (ii) a more pronounced shift of the PSD by changing resolution as compared to what was expected from fractal modeling; (iii) inaccuracy of the RPT in describing the joint PDFs of asperity heights and curvatures of textured surfaces; (iv) lack of resolution-invariance of joint PDFs of textured surfaces in case of special surface treatments, not accounted for by fractal modeling.

The Thomas–Fermi quark model: Non-relativistic aspects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Quan, E-mail: quan_liu@baylor.edu; Wilcox, Walter, E-mail: walter_wilcox@baylor.edu

The first numerical investigation of non-relativistic aspects of the Thomas–Fermi (TF) statistical multi-quark model is given. We begin with a review of the traditional TF model without an explicit spin interaction and find that the spin splittings are too small in this approach. An explicit spin interaction is then introduced which entails the definition of a generalized spin “flavor”. We investigate baryonic states in this approach which can be described with two inequivalent wave functions; such states can however apply to multiple degenerate flavors. We find that the model requires a spatial separation of quark flavors, even if completely degenerate.more » Although the TF model is designed to investigate the possibility of many-quark states, we find surprisingly that it may be used to fit the low energy spectrum of almost all ground state octet and decuplet baryons. The charge radii of such states are determined and compared with lattice calculations and other models. The low energy fit obtained allows us to extrapolate to the six-quark doubly strange H-dibaryon state, flavor symmetric strange states of higher quark content and possible six quark nucleon–nucleon resonances. The emphasis here is on the systematics revealed in this approach. We view our model as a versatile and convenient tool for quickly assessing the characteristics of new, possibly bound, particle states of higher quark number content. -- Highlights: • First application of the statistical Thomas–Fermi quark model to baryonic systems. • Novel aspects: spin as generalized flavor; spatial separation of quark flavor phases. • The model is statistical, but the low energy baryonic spectrum is successfully fit. • Numerical applications include the H-dibaryon, strange states and nucleon resonances. • The statistical point of view does not encourage the idea of bound many-quark baryons.« less

A Statistical Model of the Fluctuations in the Geomagnetic Field from Paleosecular Variation to Reversal

PubMed

Camps; Prevot

1996-08-09

The statistical characteristics of the local magnetic field of Earth during paleosecular variation, excursions, and reversals are described on the basis of a database that gathers the cleaned mean direction and average remanent intensity of 2741 lava flows that have erupted over the last 20 million years. A model consisting of a normally distributed axial dipole component plus an independent isotropic set of vectors with a Maxwellian distribution that simulates secular variation fits the range of geomagnetic fluctuations, in terms of both direction and intensity. This result suggests that the magnitude of secular variation vectors is independent of the magnitude of Earth's axial dipole moment and that the amplitude of secular variation is unchanged during reversals.

Quantum statistics of Raman scattering model with Stokes mode generation

NASA Technical Reports Server (NTRS)

Tanatar, Bilal; Shumovsky, Alexander S.

1994-01-01

The model describing three coupled quantum oscillators with decay of Rayleigh mode into the Stokes and vibration (phonon) modes is examined. Due to the Manley-Rowe relations the problem of exact eigenvalues and eigenstates is reduced to the calculation of new orthogonal polynomials defined both by the difference and differential equations. The quantum statistical properties are examined in the case when initially: the Stokes mode is in the vacuum state; the Rayleigh mode is in the number state; and the vibration mode is in the number of or squeezed states. The collapses and revivals are obtained for different initial conditions as well as the change in time the sub-Poisson distribution by the super-Poisson distribution and vice versa.

Statistical analyses to support guidelines for marine avian sampling. Final report

USGS Publications Warehouse

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 distribution to describe counts of a given species in a particular region and season. 4. Using a large database of historical at-sea seabird survey data, we applied this technique to identify appropriate statistical distributions for modeling a variety of species, allowing the distribution to vary by season. For each species and season, we used the selected distribution to calculate and map retrospective statistical power to detect hotspots and coldspots, and map pvalues from Monte Carlo significance tests of hotspots and coldspots, in discrete lease blocks designated by the U.S. Department of Interior, Bureau of Ocean Energy Management (BOEM). 5. Because our definition of hotspots and coldspots does not explicitly include variability over time, we examine the relationship between the temporal scale of sampling and the proportion of variance captured in time series of key environmental correlates of marine bird abundance, as well as available marine bird abundance time series, and use these analyses to develop recommendations for the temporal distribution of sampling to adequately represent both shortterm and long-term variability. We conclude by presenting a schematic “decision tree” showing how this power analysis approach would fit in a general framework for avian survey design, and discuss implications of model assumptions and results. We discuss avenues for future development of this work, and recommendations for practical implementation in the context of siting and wildlife assessment for offshore renewable energy development projects.

An investigation into pilot and system response to critical in-flight events, volume 1

NASA Technical Reports Server (NTRS)

Rockwell, T. H.; Giffin, W. C.

1981-01-01

The scope of a critical in-flight event (CIFE) with emphasis on pilot management of available resources is described. Detailed scenarios for both full mission simulation and written testing of pilot responses to CIFE's, and statistical relationships among pilot characteristics and observed responses are developed. A model developed to described pilot response to CIFE and an analysis of professional fight crews compliance with specified operating procedures and the relationships with in-flight errors are included.

Temporal asymmetry in precipitation time series and its influence on flow simulations in combined sewer systems

NASA Astrophysics Data System (ADS)

Müller, Thomas; Schütze, Manfred; Bárdossy, András

2017-09-01

A property of natural processes is temporal irreversibility. However, this property cannot be reflected by most statistics used to describe precipitation time series and, consequently, is not considered in most precipitation models. In this paper, a new statistic, the asymmetry measure, is introduced and applied to precipitation enabling to detect and quantify irreversibility. It is used to analyze two different data sets of Singapore and Germany. The data of both locations show a significant asymmetry for high temporal resolutions. The asymmetry is more pronounced for Singapore where the climate is dominated by convective precipitation events. The impact of irreversibility on applications is analyzed on two different hydrological sewer system models. The results show that the effect of the irreversibility can lead to biases in combined sewer overflow statistics. This bias is in the same order as the effect that can be achieved by real time control of sewer systems. Consequently, wrong conclusion can be drawn if synthetic time series are used for sewer systems if asymmetry is present, but not considered in precipitation modeling.

Understanding baseball team standings and streaks

NASA Astrophysics Data System (ADS)

Sire, C.; Redner, S.

2009-02-01

Can one understand the statistics of wins and losses of baseball teams? Are their consecutive-game winning and losing streaks self-reinforcing or can they be described statistically? We apply the Bradley-Terry model, which incorporates the heterogeneity of team strengths in a minimalist way, to answer these questions. Excellent agreement is found between the predictions of the Bradley-Terry model and the rank dependence of the average number team wins and losses in major-league baseball over the past century when the distribution of team strengths is taken to be uniformly distributed over a finite range. Using this uniform strength distribution, we also find very good agreement between model predictions and the observed distribution of consecutive-game team winning and losing streaks over the last half-century; however, the agreement is less good for the previous half-century. The behavior of the last half-century supports the hypothesis that long streaks are primarily statistical in origin with little self-reinforcing component. The data further show that the past half-century of baseball has been more competitive than the preceding half-century.

Statistical estimation of femur micro-architecture using optimal shape and density predictors.

PubMed

Lekadir, Karim; Hazrati-Marangalou, Javad; Hoogendoorn, Corné; Taylor, Zeike; van Rietbergen, Bert; Frangi, Alejandro F

2015-02-26

The personalization of trabecular micro-architecture has been recently shown to be important in patient-specific biomechanical models of the femur. However, high-resolution in vivo imaging of bone micro-architecture using existing modalities is still infeasible in practice due to the associated acquisition times, costs, and X-ray radiation exposure. In this study, we describe a statistical approach for the prediction of the femur micro-architecture based on the more easily extracted subject-specific bone shape and mineral density information. To this end, a training sample of ex vivo micro-CT images is used to learn the existing statistical relationships within the low and high resolution image data. More specifically, optimal bone shape and mineral density features are selected based on their predictive power and used within a partial least square regression model to estimate the unknown trabecular micro-architecture within the anatomical models of new subjects. The experimental results demonstrate the accuracy of the proposed approach, with average errors of 0.07 for both the degree of anisotropy and tensor norms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Climate change or climate cycles? Snowpack trends in the Olympic and Cascade Mountains, Washington, USA.

PubMed

Barry, Dwight; McDonald, Shea

2013-01-01

Climate change could significantly influence seasonal streamflow and water availability in the snowpack-fed watersheds of Washington, USA. Descriptions of snowpack decline often use linear ordinary least squares (OLS) models to quantify this change. However, the region's precipitation is known to be related to climate cycles. If snowpack decline is more closely related to these cycles, an OLS model cannot account for this effect, and thus both descriptions of trends and estimates of decline could be inaccurate. We used intervention analysis to determine whether snow water equivalent (SWE) in 25 long-term snow courses within the Olympic and Cascade Mountains are more accurately described by OLS (to represent gradual change), stationary (to represent no change), or step-stationary (to represent climate cycling) models. We used Bayesian information-theoretic methods to determine these models' relative likelihood, and we found 90 models that could plausibly describe the statistical structure of the 25 snow courses' time series. Posterior model probabilities of the 29 "most plausible" models ranged from 0.33 to 0.91 (mean = 0.58, s = 0.15). The majority of these time series (55%) were best represented as step-stationary models with a single breakpoint at 1976/77, coinciding with a major shift in the Pacific Decadal Oscillation. However, estimates of SWE decline differed by as much as 35% between statistically plausible models of a single time series. This ambiguity is a critical problem for water management policy. Approaches such as intervention analysis should become part of the basic analytical toolkit for snowpack or other climatic time series data.

Selecting the "Best" Factor Structure and Moving Measurement Validation Forward: An Illustration.

PubMed

Schmitt, Thomas A; Sass, Daniel A; Chappelle, Wayne; Thompson, William

2018-04-09

Despite the broad literature base on factor analysis best practices, research seeking to evaluate a measure's psychometric properties frequently fails to consider or follow these recommendations. This leads to incorrect factor structures, numerous and often overly complex competing factor models and, perhaps most harmful, biased model results. Our goal is to demonstrate a practical and actionable process for factor analysis through (a) an overview of six statistical and psychometric issues and approaches to be aware of, investigate, and report when engaging in factor structure validation, along with a flowchart for recommended procedures to understand latent factor structures; (b) demonstrating these issues to provide a summary of the updated Posttraumatic Stress Disorder Checklist (PCL-5) factor models and a rationale for validation; and (c) conducting a comprehensive statistical and psychometric validation of the PCL-5 factor structure to demonstrate all the issues we described earlier. Considering previous research, the PCL-5 was evaluated using a sample of 1,403 U.S. Air Force remotely piloted aircraft operators with high levels of battlefield exposure. Previously proposed PCL-5 factor structures were not supported by the data, but instead a bifactor model is arguably more statistically appropriate.

Seven-parameter statistical model for BRDF in the UV band.

PubMed

Bai, Lu; Wu, Zhensen; Zou, Xiren; Cao, Yunhua

2012-05-21

A new semi-empirical seven-parameter BRDF model is developed in the UV band using experimentally measured data. The model is based on the five-parameter model of Wu and the fourteen-parameter model of Renhorn and Boreman. Surface scatter, bulk scatter and retro-reflection scatter are considered. An optimizing modeling method, the artificial immune network genetic algorithm, is used to fit the BRDF measurement data over a wide range of incident angles. The calculation time and accuracy of the five- and seven-parameter models are compared. After fixing the seven parameters, the model can well describe scattering data in the UV band.

Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal.

PubMed

Haneuse, Sebastien; Lee, Kyu Ha

2016-05-01

Hospital readmission is a key marker of quality of health care. Notwithstanding its widespread use, however, it remains controversial in part because statistical methods used to analyze readmission, primarily logistic regression and related models, may not appropriately account for patients who die before experiencing a readmission event within the time frame of interest. Toward resolving this, we describe and illustrate the semi-competing risks framework, which refers to the general setting where scientific interest lies with some nonterminal event (eg, readmission), the occurrence of which is subject to a terminal event (eg, death). Although several statistical analysis methods have been proposed for semi-competing risks data, we describe in detail the use of illness-death models primarily because of their relation to well-known methods for survival analysis and the availability of software. We also describe and consider in detail several existing approaches that could, in principle, be used to analyze semi-competing risks data, including composite end point and competing risks analyses. Throughout we illustrate the ideas and methods using data on N=49 763 Medicare beneficiaries hospitalized between 2011 and 2013 with a principle discharge diagnosis of heart failure. © 2016 American Heart Association, Inc.

Teasing apart the effects of natural and constructed green ...

EPA Pesticide Factsheets

Summer low flows and stream temperature maxima are key drivers affecting the sustainability of fish populations. Thus, it is critical to understand both the natural templates of spatiotemporal variability, how these are shifting due to anthropogenic influences of development and climate change, and how these impacts can be moderated by natural and constructed green infrastructure. Low flow statistics of New England streams have been characterized using a combination of regression equations to describe long-term averages as a function of indicators of hydrologic regime (rain- versus snow-dominated), precipitation, evapotranspiration or temperature, surface water storage, baseflow recession rates, and impervious cover. Difference equations have been constructed to describe interannual variation in low flow as a function of changing air temperature, precipitation, and ocean-atmospheric teleconnection indices. Spatial statistical network models have been applied to explore fine-scale variability of thermal regimes along stream networks in New England as a function of variables describing natural and altered energy inputs, groundwater contributions, and retention time. Low flows exacerbate temperature impacts by reducing thermal inertia of streams to energy inputs. Based on these models, we can construct scenarios of fish habitat suitability using current and projected future climate and the potential for preservation and restoration of historic habitat regimes th

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest is Non-Terminal

PubMed Central

Haneuse, Sebastien; Lee, Kyu Ha

2016-01-01

Hospital readmission is a key marker of quality of health care. Notwithstanding its widespread use, however, it remains controversial in part because statistical methods used to analyze readmission, primarily logistic regression and related models, may not appropriately account for patients who die prior to experiencing a readmission event within the timeframe of interest. Towards resolving this, we describe and illustrate the semi-competing risks framework, which refers to the general setting where scientific interest lies with some non-terminal event (e.g. readmission), the occurrence of which is subject to a terminal event (e.g. death). Although a number of statistical analysis methods have been proposed for semi-competing risks data, we describe in detail the use of illness-death models primarily because of their relation to well-known methods for survival analysis and the availability of software. We also describe and consider in detail a number of existing approaches that could, in principle, be used to analyze semi-competing risks data including composite endpoint and competing risks analyses. Throughout we illustrate the ideas and methods using data on N=49,763 Medicare beneficiaries hospitalized between 2011–2013 with a principle discharge diagnosis of heart failure. PMID:27072677

Boosting Bayesian parameter inference of stochastic differential equation models with methods from statistical physics

NASA Astrophysics Data System (ADS)

Albert, Carlo; Ulzega, Simone; Stoop, Ruedi

2016-04-01

Measured time-series of both precipitation and runoff are known to exhibit highly non-trivial statistical properties. For making reliable probabilistic predictions in hydrology, it is therefore desirable to have stochastic models with output distributions that share these properties. When parameters of such models have to be inferred from data, we also need to quantify the associated parametric uncertainty. For non-trivial stochastic models, however, this latter step is typically very demanding, both conceptually and numerically, and always never done in hydrology. Here, we demonstrate that methods developed in statistical physics make a large class of stochastic differential equation (SDE) models amenable to a full-fledged Bayesian parameter inference. For concreteness we demonstrate these methods by means of a simple yet non-trivial toy SDE model. We consider a natural catchment that can be described by a linear reservoir, at the scale of observation. All the neglected processes are assumed to happen at much shorter time-scales and are therefore modeled with a Gaussian white noise term, the standard deviation of which is assumed to scale linearly with the system state (water volume in the catchment). Even for constant input, the outputs of this simple non-linear SDE model show a wealth of desirable statistical properties, such as fat-tailed distributions and long-range correlations. Standard algorithms for Bayesian inference fail, for models of this kind, because their likelihood functions are extremely high-dimensional intractable integrals over all possible model realizations. The use of Kalman filters is illegitimate due to the non-linearity of the model. Particle filters could be used but become increasingly inefficient with growing number of data points. Hamiltonian Monte Carlo algorithms allow us to translate this inference problem to the problem of simulating the dynamics of a statistical mechanics system and give us access to most sophisticated methods that have been developed in the statistical physics community over the last few decades. We demonstrate that such methods, along with automated differentiation algorithms, allow us to perform a full-fledged Bayesian inference, for a large class of SDE models, in a highly efficient and largely automatized manner. Furthermore, our algorithm is highly parallelizable. For our toy model, discretized with a few hundred points, a full Bayesian inference can be performed in a matter of seconds on a standard PC.

Scattering and transport statistics at the metal-insulator transition: A numerical study of the power-law banded random-matrix model

NASA Astrophysics Data System (ADS)

Méndez-Bermúdez, J. A.; Gopar, Victor A.; Varga, Imre

2010-09-01

We study numerically scattering and transport statistical properties of the one-dimensional Anderson model at the metal-insulator transition described by the power-law banded random matrix (PBRM) model at criticality. Within a scattering approach to electronic transport, we concentrate on the case of a small number of single-channel attached leads. We observe a smooth crossover from localized to delocalized behavior in the average-scattering matrix elements, the conductance probability distribution, the variance of the conductance, and the shot noise power by varying b (the effective bandwidth of the PBRM model) from small (b≪1) to large (b>1) values. We contrast our results with analytic random matrix theory predictions which are expected to be recovered in the limit b→∞ . We also compare our results for the PBRM model with those for the three-dimensional (3D) Anderson model at criticality, finding that the PBRM model with bɛ[0.2,0.4] reproduces well the scattering and transport properties of the 3D Anderson model.

Statistical analysis of cascading failures in power grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertkov, Michael; Pfitzner, Rene; Turitsyn, Konstantin

2010-12-01

We introduce a new microscopic model of cascading failures in transmission power grids. This model accounts for automatic response of the grid to load fluctuations that take place on the scale of minutes, when optimum power flow adjustments and load shedding controls are unavailable. We describe extreme events, caused by load fluctuations, which cause cascading failures of loads, generators and lines. Our model is quasi-static in the causal, discrete time and sequential resolution of individual failures. The model, in its simplest realization based on the Directed Current description of the power flow problem, is tested on three standard IEEE systemsmore » consisting of 30, 39 and 118 buses. Our statistical analysis suggests a straightforward classification of cascading and islanding phases in terms of the ratios between average number of removed loads, generators and links. The analysis also demonstrates sensitivity to variations in line capacities. Future research challenges in modeling and control of cascading outages over real-world power networks are discussed.« less

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

Vibroacoustic Response of the NASA ACTS Spacecraft Antenna to Launch Acoustic Excitation

NASA Technical Reports Server (NTRS)

Larko, Jeffrey M.; Cotoni, Vincent

2008-01-01

The Advanced Communications Technology Satellite was an experimental NASA satellite launched from the Space Shuttle Discovery. As part of the ground test program, the satellite s large, parabolic reflector antennas were exposed to a reverberant acoustic loading to simulate the launch acoustics in the Shuttle payload bay. This paper describes the modelling and analysis of the dynamic response of these large, composite spacecraft antenna structure subjected to a diffuse acoustic field excitation. Due to the broad frequency range of the excitation, different models were created to make predictions in the various frequency regimes of interest: a statistical energy analysis (SEA) model to capture the high frequency response and a hybrid finite element-statistical energy (hybrid FE-SEA) model for the low to mid-frequency responses. The strengths and limitations of each of the analytical techniques are discussed. The predictions are then compared to the measured acoustic test data and to a boundary element (BEM) model to evaluate the performance of the hybrid techniques.

Inferring Models of Bacterial Dynamics toward Point Sources

PubMed Central

Jashnsaz, Hossein; Nguyen, Tyler; Petrache, Horia I.; Pressé, Steve

2015-01-01

Experiments have shown that bacteria can be sensitive to small variations in chemoattractant (CA) concentrations. Motivated by these findings, our focus here is on a regime rarely studied in experiments: bacteria tracking point CA sources (such as food patches or even prey). In tracking point sources, the CA detected by bacteria may show very large spatiotemporal fluctuations which vary with distance from the source. We present a general statistical model to describe how bacteria locate point sources of food on the basis of stochastic event detection, rather than CA gradient information. We show how all model parameters can be directly inferred from single cell tracking data even in the limit of high detection noise. Once parameterized, our model recapitulates bacterial behavior around point sources such as the “volcano effect”. In addition, while the search by bacteria for point sources such as prey may appear random, our model identifies key statistical signatures of a targeted search for a point source given any arbitrary source configuration. PMID:26466373

Statistical modeling of software reliability

NASA Technical Reports Server (NTRS)

Miller, Douglas R.

1992-01-01

This working paper discusses the statistical simulation part of a controlled software development experiment being conducted under the direction of the System Validation Methods Branch, Information Systems Division, NASA Langley Research Center. The experiment uses guidance and control software (GCS) aboard a fictitious planetary landing spacecraft: real-time control software operating on a transient mission. Software execution is simulated to study the statistical aspects of reliability and other failure characteristics of the software during development, testing, and random usage. Quantification of software reliability is a major goal. Various reliability concepts are discussed. Experiments are described for performing simulations and collecting appropriate simulated software performance and failure data. This data is then used to make statistical inferences about the quality of the software development and verification processes as well as inferences about the reliability of software versions and reliability growth under random testing and debugging.

Fractional properties of geophysical field variability on the example of hydrochemical parameters

NASA Astrophysics Data System (ADS)

Shevtsov, Boris; Shevtsova, Olga

2017-10-01

Using the properties of compound Poisson process and its fractional generalizations, statistical models of geophysical fields variability are considered on an example of hydrochemical parameters system. These models are universal to describe objects of different nature and allow us to explain various pulsing regime. Manifestations of non-conservatism in hydrochemical parameters system and the advantages of the system approach in the description of geophysical fields variability are discussed.

Battery Calendar Life Estimator Manual Modeling and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jon P. Christophersen; Ira Bloom; Ed Thomas

2012-10-01

The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.

Battery Life Estimator Manual Linear Modeling and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jon P. Christophersen; Ira Bloom; Ed Thomas

2009-08-01

The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.

Semiclassical matrix model for quantum chaotic transport with time-reversal symmetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Novaes, Marcel, E-mail: marcel.novaes@gmail.com

2015-10-15

We show that the semiclassical approach to chaotic quantum transport in the presence of time-reversal symmetry can be described by a matrix model. In other words, we construct a matrix integral whose perturbative expansion satisfies the semiclassical diagrammatic rules for the calculation of transport statistics. One of the virtues of this approach is that it leads very naturally to the semiclassical derivation of universal predictions from random matrix theory.

Composite Load Spectra for Select Space Propulsion Structural Components

NASA Technical Reports Server (NTRS)

Ho, Hing W.; Newell, James F.

1994-01-01

Generic load models are described with multiple levels of progressive sophistication to simulate the composite (combined) load spectra (CLS) that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades and liquid oxygen (LOX) posts. These generic (coupled) models combine the deterministic models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients are then determined using advanced probabilistic simulation methods with and without strategically selected experimental data. The entire simulation process is included in a CLS computer code. Applications of the computer code to various components in conjunction with the PSAM (Probabilistic Structural Analysis Method) to perform probabilistic load evaluation and life prediction evaluations are also described to illustrate the effectiveness of the coupled model approach.

The construction of meaning.

PubMed

Kintsch, Walter; Mangalath, Praful

2011-04-01

We argue that word meanings are not stored in a mental lexicon but are generated in the context of working memory from long-term memory traces that record our experience with words. Current statistical models of semantics, such as latent semantic analysis and the Topic model, describe what is stored in long-term memory. The CI-2 model describes how this information is used to construct sentence meanings. This model is a dual-memory model, in that it distinguishes between a gist level and an explicit level. It also incorporates syntactic information about how words are used, derived from dependency grammar. The construction of meaning is conceptualized as feature sampling from the explicit memory traces, with the constraint that the sampling must be contextually relevant both semantically and syntactically. Semantic relevance is achieved by sampling topically relevant features; local syntactic constraints as expressed by dependency relations ensure syntactic relevance. Copyright © 2010 Cognitive Science Society, Inc.

Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems

NASA Astrophysics Data System (ADS)

Raksincharoensak, Pongsathorn; Khaisongkram, Wathanyoo; Nagai, Masao; Shimosaka, Masamichi; Mori, Taketoshi; Sato, Tomomasa

2010-12-01

This paper describes the modelling of naturalistic driving behaviour in real-world traffic scenarios, based on driving data collected via an experimental automobile equipped with a continuous sensing drive recorder. This paper focuses on the longitudinal driving situations which are classified into five categories - car following, braking, free following, decelerating and stopping - and are referred to as driving states. Here, the model is assumed to be represented by a state flow diagram. Statistical machine learning of driver-vehicle-environment system model based on driving database is conducted by a discriminative modelling approach called boosting sequential labelling method.

Underwater Sound Propagation Modeling Methods for Predicting Marine Animal Exposure.

PubMed

Hamm, Craig A; McCammon, Diana F; Taillefer, Martin L

2016-01-01

The offshore exploration and production (E&P) industry requires comprehensive and accurate ocean acoustic models for determining the exposure of marine life to the high levels of sound used in seismic surveys and other E&P activities. This paper reviews the types of acoustic models most useful for predicting the propagation of undersea noise sources and describes current exposure models. The severe problems caused by model sensitivity to the uncertainty in the environment are highlighted to support the conclusion that it is vital that risk assessments include transmission loss estimates with statistical measures of confidence.

Data mining and statistical inference in selective laser melting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamath, Chandrika

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

Data mining and statistical inference in selective laser melting

DOE PAGES

Kamath, Chandrika

2016-01-11

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

A three-dimensional refractive index model for simulation of optical wave propagation in atmospheric turbulence

NASA Astrophysics Data System (ADS)

Paramonov, P. V.; Vorontsov, A. M.; Kunitsyn, V. E.

2015-10-01

Numerical modeling of optical wave propagation in atmospheric turbulence is traditionally performed with using the so-called "split"-operator method, when the influence of the propagation medium's refractive index inhomogeneities is accounted for only within a system of infinitely narrow layers (phase screens) where phase is distorted. Commonly, under certain assumptions, such phase screens are considered as mutually statistically uncorrelated. However, in several important applications including laser target tracking, remote sensing, and atmospheric imaging, accurate optical field propagation modeling assumes upper limitations on interscreen spacing. The latter situation can be observed, for instance, in the presence of large-scale turbulent inhomogeneities or in deep turbulence conditions, where interscreen distances become comparable with turbulence outer scale and, hence, corresponding phase screens cannot be statistically uncorrelated. In this paper, we discuss correlated phase screens. The statistical characteristics of screens are calculated based on a representation of turbulent fluctuations of three-dimensional (3D) refractive index random field as a set of sequentially correlated 3D layers displaced in the wave propagation direction. The statistical characteristics of refractive index fluctuations are described in terms of the von Karman power spectrum density. In the representation of these 3D layers by corresponding phase screens, the geometrical optics approximation is used.

Examining the Stationarity Assumption for Statistically Downscaled Climate Projections of Precipitation

NASA Astrophysics Data System (ADS)

Wootten, A.; Dixon, K. W.; Lanzante, J. R.; Mcpherson, R. A.

2017-12-01

Empirical statistical downscaling (ESD) approaches attempt to refine global climate model (GCM) information via statistical relationships between observations and GCM simulations. The aim of such downscaling efforts is to create added-value climate projections by adding finer spatial detail and reducing biases. The results of statistical downscaling exercises are often used in impact assessments under the assumption that past performance provides an indicator of future results. Given prior research describing the danger of this assumption with regards to temperature, this study expands the perfect model experimental design from previous case studies to test the stationarity assumption with respect to precipitation. Assuming stationarity implies the performance of ESD methods are similar between the future projections and historical training. Case study results from four quantile-mapping based ESD methods demonstrate violations of the stationarity assumption for both central tendency and extremes of precipitation. These violations vary geographically and seasonally. For the four ESD methods tested the greatest challenges for downscaling of daily total precipitation projections occur in regions with limited precipitation and for extremes of precipitation along Southeast coastal regions. We conclude with a discussion of future expansion of the perfect model experimental design and the implications for improving ESD methods and providing guidance on the use of ESD techniques for impact assessments and decision-support.

Leveraging non-targeted metabolite profiling via statistical genomics

USDA-ARS?s Scientific Manuscript database

One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a networ...

Prediction of Solution Properties of Flexible-Chain Polymers: A Computer Simulation Undergraduate Experiment

ERIC Educational Resources Information Center

de la Torre, Jose Garcia; Cifre, Jose G. Hernandez; Martinez, M. Carmen Lopez

2008-01-01

This paper describes a computational exercise at undergraduate level that demonstrates the employment of Monte Carlo simulation to study the conformational statistics of flexible polymer chains, and to predict solution properties. Three simple chain models, including excluded volume interactions, have been implemented in a public-domain computer…

Attenuation of landscape signals through the coastal zone: A basin-wide analysis for the US Great Lakes shoreline, circa 2002-2010

EPA Science Inventory

We compare statistical models developed to describe a) the relationship between watershed properties and Great Lakes coastal wetlands with b) the relationship developed between watershed properties and the Great Lakes nearshore. Using landscape metrics from the GLEI project (Dan...

TESTING LANDSCAPE INDICATORS FOR STREAM CONDITION RELATED TO PESTICIDES AND NUTRIENTS: LANDSCAPE INDICATORS FOR PESTICIDES STUDY FOR MID-ATLANTIC COASTAL STREAMS (LIPS-MACS)

EPA Science Inventory

This research plan for the Landscape Indicators for Pesticides Study ? Mid-Atlantic Coastal Streams (LIPS-MACS) describes the rational and approach of developing a research project to evaluate statistical landscape indicator models for freshwater streams in the Mid-Atlantic Coas...

A Computational Study of the Energy Dissipation Through an Acrylic Target Impacted by Various Size FSP

DTIC Science & Technology

2009-06-01

data, and then returns an array that describes the line. This function, when compared to the LOGEST statistical function of the Microsoft Excel, which...threats continues to grow, the ability to predict materials performances using advanced modeling tools increases. The current paper has demonstrated

Enrollment Simulation and Planning. Strategies & Solutions Series, No. 3.

ERIC Educational Resources Information Center

McIntyre, Chuck

Enrollment simulation and planning (ESP) is centered on the use of statistical models to describe how and why college enrollments fluctuate. College planners may use this approach with confidence to simulate any number of plausible future scenarios. Planners can then set a variety of possible college actions against these scenarios, and examine…

Adaptive variation in Pinus ponderosa from Intermountain regions. II. Middle Columbia River system

Treesearch

Gerald Rehfeldt

1986-01-01

Seedling populations were grown and compared in common environments. Statistical analyses detected genetic differences between populations for numerous traits reflecting growth potential and periodicity of shoot elongation. Multiple regression models described an adaptive landscape in which populations from low elevations have a high growth potential while those from...

A Multidimensional Scaling Approach to Dimensionality Assessment for Measurement Instruments Modeled by Multidimensional Item Response Theory

ERIC Educational Resources Information Center

Toro, Maritsa

2011-01-01

The statistical assessment of dimensionality provides evidence of the underlying constructs measured by a survey or test instrument. This study focuses on educational measurement, specifically tests comprised of items described as multidimensional. That is, items that require examinee proficiency in multiple content areas and/or multiple cognitive…

A Framework for Authenticity in the Mathematics and Statistics Classroom

ERIC Educational Resources Information Center

Garrett, Lauretta; Huang, Li; Charleton, Maria Calhoun

2016-01-01

Authenticity is a term commonly used in reference to pedagogical and curricular qualities of mathematics teaching and learning, but its use lacks a coherent framework. The work of researchers in engineering education provides such a framework. Authentic qualities of mathematics teaching and learning are fit within a model described by Strobel,…

Adjusting Your Gaze

ERIC Educational Resources Information Center

Webber-Thrush, Diane

2010-01-01

Peter Wylie is a man of many contradictions: a statistician and a storyteller, an introvert who loves an audience, and a self-described data geek with a passion for his work and the people it helps. Wylie is one of the pioneers of predictive modeling, the statistical analysis that uses data to drive educational institutions and nonprofits toward…

The Information Function for the One-Parameter Logistic Model: Is it Reliability?

ERIC Educational Resources Information Center

Doran, Harold C.

2005-01-01

The information function is an important statistic in item response theory (IRT) applications. Although the information function is often described as the IRT version of reliability, it differs from the classical notion of reliability from a critical perspective: replication. This article first explores the information function for the…

Truncated Lévy walks and an emerging market economic index

NASA Astrophysics Data System (ADS)

Miranda, L. Couto; Riera, R.

2001-08-01

In this paper, we perform a statistical analysis of the major stock index in Latin America, the São Paulo Stock Exchange Index in Brazil (IBOVESPA). Database contains daily records for the 15-year period 1986-2000. We find that the time evolution of the index of share prices is well described by an Exponentially Truncated Lévy Flight (ETLF) characterized by a Lévy exponent α≃1.6-1.7 and a cutoff exponent λ≃1.7. The ETLF statistics accounts for the observed short-term large fluctuations of the financial data time series and describes the long-term convergence to the Gaussian regime. We derive the characteristic crossover time scale Nc dependence on α and λ according to this model as well as the volatility dependence on α, λ and Nc. We find an uncorrelated behaviour of the historical data and Nc≃20 trading days which are in numerical agreement with the analytical results. This dynamic model provides a framework within which it is possible to develop an efficient risk management and option pricing practice for emerging economies.

Power analysis to detect treatment effect in longitudinal studies with heterogeneous errors and incomplete data.

PubMed

Vallejo, Guillermo; Ato, Manuel; Fernández García, Paula; Livacic Rojas, Pablo E; Tuero Herrero, Ellián

2016-08-01

 S. Usami (2014) describes a method to realistically determine sample size in longitudinal research using a multilevel model. The present research extends the aforementioned work to situations where it is likely that the assumption of homogeneity of the errors across groups is not met and the error term does not follow a scaled identity covariance structure.   For this purpose, we followed a procedure based on transforming the variance components of the linear growth model and the parameter related to the treatment effect into specific and easily understandable indices. At the same time, we provide the appropriate statistical machinery for researchers to use when data loss is unavoidable, and changes in the expected value of the observed responses are not linear.   The empirical powers based on unknown variance components were virtually the same as the theoretical powers derived from the use of statistically processed indexes.   The main conclusion of the study is the accuracy of the proposed method to calculate sample size in the described situations with the stipulated power criteria.

Potential for the dynamics of pedestrians in a socially interacting group

NASA Astrophysics Data System (ADS)

Zanlungo, Francesco; Ikeda, Tetsushi; Kanda, Takayuki

2014-01-01

We introduce a simple potential to describe the dynamics of the relative motion of two pedestrians socially interacting in a walking group. We show that the proposed potential, based on basic empirical observations and theoretical considerations, can qualitatively describe the statistical properties of pedestrian behavior. In detail, we show that the two-dimensional probability distribution of the relative distance is determined by the proposed potential through a Boltzmann distribution. After calibrating the parameters of the model on the two-pedestrian group data, we apply the model to three-pedestrian groups, showing that it describes qualitatively and quantitatively well their behavior. In particular, the model predicts that three-pedestrian groups walk in a V-shaped formation and provides accurate values for the position of the three pedestrians. Furthermore, the model correctly predicts the average walking velocity of three-person groups based on the velocity of two-person ones. Possible extensions to larger groups, along with alternative explanations of the social dynamics that may be implied by our model, are discussed at the end of the paper.

Drawing Nomograms with R: applications to categorical outcome and survival data.

PubMed

Zhang, Zhongheng; Kattan, Michael W

2017-05-01

Outcome prediction is a major task in clinical medicine. The standard approach to this work is to collect a variety of predictors and build a model of appropriate type. The model is a mathematical equation that connects the outcome of interest with the predictors. A new patient with given clinical characteristics can be predicted for outcome with this model. However, the equation describing the relationship between predictors and outcome is often complex and the computation requires software for practical use. There is another method called nomogram which is a graphical calculating device allowing an approximate graphical computation of a mathematical function. In this article, we describe how to draw nomograms for various outcomes with nomogram() function. Binary outcome is fit by logistic regression model and the outcome of interest is the probability of the event of interest. Ordinal outcome variable is also discussed. Survival analysis can be fit with parametric model to fully describe the distributions of survival time. Statistics such as the median survival time, survival probability up to a specific time point are taken as the outcome of interest.

Voronoi Cell Patterns: theoretical model and application to submonolayer growth

NASA Astrophysics Data System (ADS)

González, Diego Luis; Einstein, T. L.

2012-02-01

We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We apply our model to describe the Voronoi cell patterns of island nucleation for critical island sizes i=0,1,2,3. Experimental results for the Voronoi cells of InAs/GaAs quantum dots are also described by our model.