Acceleration transforms and statistical kinetic models

International Nuclear Information System (INIS)

LuValle, M.J.; Welsher, T.L.; Svoboda, K.

1988-01-01

For a restricted class of problems a mathematical model of microscopic degradation processes, statistical kinetics, is developed and linked through acceleration transforms to the information which can be obtained from a system in which the only observable sign of degradation is sudden and catastrophic failure. The acceleration transforms were developed in accelerated life testing applications as a tool for extrapolating from the observable results of an accelerated life test to the dynamics of the underlying degradation processes. A particular concern of a physicist attempting to interpreted the results of an analysis based on acceleration transforms is determining the physical species involved in the degradation process. These species may be (a) relatively abundant or (b) relatively rare. The main results of this paper are a theorem showing that for an important subclass of statistical kinetic models, acceleration transforms cannot be used to distinguish between cases a and b, and an example showing that in some cases falling outside the restrictions of the theorem, cases a and b can be distinguished by their acceleration transforms

Statistical modelling with quantile functions

CERN Document Server

Gilchrist, Warren

2000-01-01

Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of quantiles in their work. Until now, however, no one put all the ideas together to form what turns out to be a general approach to statistics.Statistical Modelling with Quantile Functions does just that. It systematically examines the entire process of statistical modelling, starting with using the quantile function to define continuous distributions. The author shows that by using this approach, it becomes possible to develop complex distributional models from simple components. A modelling kit can be developed that applies to the whole model - deterministic and stochastic components - and this kit operates by adding, multiplying, and transforming distributions rather than data.Statistical Modelling with Quantile Functions adds a new dimension to the practice of stati...

PVeStA: A Parallel Statistical Model Checking and Quantitative Analysis Tool

KAUST Repository

AlTurki, Musab

2011-01-01

Statistical model checking is an attractive formal analysis method for probabilistic systems such as, for example, cyber-physical systems which are often probabilistic in nature. This paper is about drastically increasing the scalability of statistical model checking, and making such scalability of analysis available to tools like Maude, where probabilistic systems can be specified at a high level as probabilistic rewrite theories. It presents PVeStA, an extension and parallelization of the VeStA statistical model checking tool [10]. PVeStA supports statistical model checking of probabilistic real-time systems specified as either: (i) discrete or continuous Markov Chains; or (ii) probabilistic rewrite theories in Maude. Furthermore, the properties that it can model check can be expressed in either: (i) PCTL/CSL, or (ii) the QuaTEx quantitative temporal logic. As our experiments show, the performance gains obtained from parallelization can be very high. © 2011 Springer-Verlag.

A Statistical Programme Assignment Model

DEFF Research Database (Denmark)

Rosholm, Michael; Staghøj, Jonas; Svarer, Michael

When treatment effects of active labour market programmes are heterogeneous in an observable way  across the population, the allocation of the unemployed into different programmes becomes a particularly  important issue. In this paper, we present a statistical model designed to improve the present...... duration of unemployment spells may result if a statistical programme assignment model is introduced. We discuss several issues regarding the  plementation of such a system, especially the interplay between the statistical model and  case workers....

Statistical model analysis of fast-neutron-induced fission of U isotopes

International Nuclear Information System (INIS)

Lestone, J.P.; Gavron, A.

1994-01-01

We have obtained the first experimental evidence of the washing out of the collective level density enhancement associated with the amma deformation of the triaxial first barrier in the U isotope cross sections at neutron energies up to ∼20 MeV, with a statistical model which uses level densities obtained from Nilsson model singles particle levels, we find that it is necessary to (1) wash out the triaxial level density enhancement at an excitation energy of ∼7 MeV above the triaxial barriers with a width of ∼1 MeV, and (2) incorporate the effects of preequilibrium emission. These results imply a γ deformation of the first barriers in the range 10 degree--20 degree. Above an incoming neutron energy of ∼20 MeV where insufficient data exist to constrain optical model potentials, our statistical model U(n,f) cross sections increasingly overestimate the experimental data. A satisfactory reproduction of all the available U(n,f) cross sections above ∼20 MeV is obtained by scaling our calculated compound nucleus formation cross sections. This scaling factor falls from 1.0 at ∼20 MeV to 0.82 at 100 MeV

Statistical identification of the confidence limits of open loop transfer functions obtained by MAR analysis

International Nuclear Information System (INIS)

Antonopoulos-Domis, M.; Mourtzanos, K.

1996-01-01

Estimators of the confidence limits of open loop transfer functions via Multivariate Auto-Regressive (MAR) modelling are not available in the literature. The statistics of open loop transfer functions obtained by MAR modelling are investigated via numerical experiments. A system of known open loop transfer functions is simulated digitally and excited by random number series. The digital signals of the simulated system are then MAR modelled and the open loop transfer functions are estimated. Performing a large number of realizations, mean values and variances of the open loop transfer functions are estimated. It is found that if the record length N of each realization is long enough then the estimates of open loop transfer functions follow normal distribution. The variance of the open loop transfer functions is proportional to 1/N. For MAR processes the asymptotic covariance matrix of the estimate of open loop transfer functions was found in agreement with theoretical prediction. (author)

Residuals and the Residual-Based Statistic for Testing Goodness of Fit of Structural Equation Models

Science.gov (United States)

Foldnes, Njal; Foss, Tron; Olsson, Ulf Henning

2012-01-01

The residuals obtained from fitting a structural equation model are crucial ingredients in obtaining chi-square goodness-of-fit statistics for the model. The authors present a didactic discussion of the residuals, obtaining a geometrical interpretation by recognizing the residuals as the result of oblique projections. This sheds light on the…

A simple statistical model for geomagnetic reversals

Science.gov (United States)

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.

Diffeomorphic Statistical Deformation Models

DEFF Research Database (Denmark)

Hansen, Michael Sass; Hansen, Mads/Fogtman; Larsen, Rasmus

2007-01-01

In this paper we present a new method for constructing diffeomorphic statistical deformation models in arbitrary dimensional images with a nonlinear generative model and a linear parameter space. Our deformation model is a modified version of the diffeomorphic model introduced by Cootes et al....... The modifications ensure that no boundary restriction has to be enforced on the parameter space to prevent folds or tears in the deformation field. For straightforward statistical analysis, principal component analysis and sparse methods, we assume that the parameters for a class of deformations lie on a linear...... with ground truth in form of manual expert annotations, and compared to Cootes's model. We anticipate applications in unconstrained diffeomorphic synthesis of images, e.g. for tracking, segmentation, registration or classification purposes....

Exclusion statistics and integrable models

International Nuclear Information System (INIS)

Mashkevich, S.

1998-01-01

The definition of exclusion statistics, as given by Haldane, allows for a statistical interaction between distinguishable particles (multi-species statistics). The thermodynamic quantities for such statistics ca be evaluated exactly. The explicit expressions for the cluster coefficients are presented. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models. The interesting questions of generalizing this correspondence onto the higher-dimensional and the multi-species cases remain essentially open

Obtaining Internet Flow Statistics by Volunteer-Based System

DEFF Research Database (Denmark)

Pedersen, Jens Myrup; Bujlow, Tomasz

2012-01-01

In this paper we demonstrate how the Volunteer Based System for Research on the Internet, developed at Aalborg University, can be used for creating statistics of Internet usage. Since the data is collected on individual machines, the statistics can be made on the basis of both individual users......, and average flow durations. The paper is concluded with a discussion on what further statistics can be made, and the further development of the system....

Optimizing refiner operation with statistical modelling

Energy Technology Data Exchange (ETDEWEB)

Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)

1997-02-01

The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.

Statistical modeling of geopressured geothermal reservoirs

Science.gov (United States)

Ansari, Esmail; Hughes, Richard; White, Christopher D.

2017-06-01

Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.

Online Statistical Modeling (Regression Analysis) for Independent Responses

Science.gov (United States)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

A Statistical Graphical Model of the California Reservoir System

Science.gov (United States)

Taeb, A.; Reager, J. T.; Turmon, M.; Chandrasekaran, V.

2017-11-01

The recent California drought has highlighted the potential vulnerability of the state's water management infrastructure to multiyear dry intervals. Due to the high complexity of the network, dynamic storage changes in California reservoirs on a state-wide scale have previously been difficult to model using either traditional statistical or physical approaches. Indeed, although there is a significant line of research on exploring models for single (or a small number of) reservoirs, these approaches are not amenable to a system-wide modeling of the California reservoir network due to the spatial and hydrological heterogeneities of the system. In this work, we develop a state-wide statistical graphical model to characterize the dependencies among a collection of 55 major California reservoirs across the state; this model is defined with respect to a graph in which the nodes index reservoirs and the edges specify the relationships or dependencies between reservoirs. We obtain and validate this model in a data-driven manner based on reservoir volumes over the period 2003-2016. A key feature of our framework is a quantification of the effects of external phenomena that influence the entire reservoir network. We further characterize the degree to which physical factors (e.g., state-wide Palmer Drought Severity Index (PDSI), average temperature, snow pack) and economic factors (e.g., consumer price index, number of agricultural workers) explain these external influences. As a consequence of this analysis, we obtain a system-wide health diagnosis of the reservoir network as a function of PDSI.

Absorbing systematic effects to obtain a better background model in a search for new physics

International Nuclear Information System (INIS)

Caron, S; Horner, S; Sundermann, J E; Cowan, G; Gross, E

2009-01-01

This paper presents a novel approach to estimate the Standard Model backgrounds based on modifying Monte Carlo predictions within their systematic uncertainties. The improved background model is obtained by altering the original predictions with successively more complex correction functions in signal-free control selections. Statistical tests indicate when sufficient compatibility with data is reached. In this way, systematic effects are absorbed into the new background model. The same correction is then applied on the Monte Carlo prediction in the signal region. Comparing this method to other background estimation techniques shows improvements with respect to statistical and systematic uncertainties. The proposed method can also be applied in other fields beyond high energy physics.

Probing NWP model deficiencies by statistical postprocessing

DEFF Research Database (Denmark)

Rosgaard, Martin Haubjerg; Nielsen, Henrik Aalborg; Nielsen, Torben S.

2016-01-01

The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational....... Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites....

Structural reliability in context of statistical uncertainties and modelling discrepancies

International Nuclear Information System (INIS)

Pendola, Maurice

2000-01-01

Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr

Statistical mechanics of directed models of polymers in the square lattice

International Nuclear Information System (INIS)

Rensburg, E J Janse van

2003-01-01

Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce partition functions and free energies, and then investigate these using the general framework of critical phenomena. Generating function and statistical mechanics approaches are closely related. For example, questions regarding the limiting free energy may be approached by considering the radius of convergence of a generating function, and the scaling properties of thermodynamic quantities are related to the asymptotic properties of the generating function. In this review the methods for obtaining generating functions and determining free energies in directed lattice path models of linear polymers is presented. These methods include decomposition methods leading to functional recursions, as well as the Temperley method (that is implemented by creating a combinatorial object, one slice at a time). A constant term formulation of the generating function will also be reviewed. The thermodynamic features and critical behaviour in models of directed paths may be

Statistical meandering wake model and its application to yaw-angle optimisation of wind farms

DEFF Research Database (Denmark)

Thøgersen, Emil; Tranberg, Bo; Herp, Jürgen

2017-01-01

deterministic models to a statistical meandering wake model (SMWM), where a random directional deflection is assigned to a narrow wake in such a way that on average it resembles a broad Jensen wake. In a second step, the model is further generalised to wind-farm level, where the deflections of the multiple...... wakes are treated as independently and identically distributed random variables. When carefully calibrated to the Nysted wind farm, the ensemble average of the statistical model produces the same wind-direction dependence of the power efficiency as obtained from the standard Jensen model. Upon using...... the JWM to perform a yaw-angle optimisation of wind-farm power output, we find an optimisation gain of 6.7% for the Nysted wind farm when compared to zero yaw angles and averaged over all wind directions. When applying the obtained JWM-based optimised yaw angles to the SMWM, the ensemble-averaged gain...

A statistical model of structure functions and quantum chromodynamics

International Nuclear Information System (INIS)

Mac, E.; Ugaz, E.; Universidad Nacional de Ingenieria, Lima

1989-01-01

We consider a model for the x-dependence of the quark distributions in the proton. Within the context of simple statistical assumptions, we obtain the parton densities in the infinite momentum frame. In a second step lowest order QCD corrections are incorporated to these distributions. Crude, but reasonable, agreement with experiment is found for the F 2 , valence and q, anti q distributions for x> or approx.0.2. (orig.)

Statistical Model of Extreme Shear

DEFF Research Database (Denmark)

Hansen, Kurt Schaldemose; Larsen, Gunner Chr.

2005-01-01

In order to continue cost-optimisation of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate...

Statistical Compression for Climate Model Output

Science.gov (United States)

Hammerling, D.; Guinness, J.; Soh, Y. J.

2017-12-01

Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus is it important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset-one year of daily mean temperature data-particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.

Automated statistical modeling of analytical measurement systems

International Nuclear Information System (INIS)

Jacobson, J.J.

1992-01-01

The statistical modeling of analytical measurement systems at the Idaho Chemical Processing Plant (ICPP) has been completely automated through computer software. The statistical modeling of analytical measurement systems is one part of a complete quality control program used by the Remote Analytical Laboratory (RAL) at the ICPP. The quality control program is an integration of automated data input, measurement system calibration, database management, and statistical process control. The quality control program and statistical modeling program meet the guidelines set forth by the American Society for Testing Materials and American National Standards Institute. A statistical model is a set of mathematical equations describing any systematic bias inherent in a measurement system and the precision of a measurement system. A statistical model is developed from data generated from the analysis of control standards. Control standards are samples which are made up at precise known levels by an independent laboratory and submitted to the RAL. The RAL analysts who process control standards do not know the values of those control standards. The object behind statistical modeling is to describe real process samples in terms of their bias and precision and, to verify that a measurement system is operating satisfactorily. The processing of control standards gives us this ability

An efficient approach to transient turbulent dispersion modeling by CFD-statistical analysis of a many-puff system

International Nuclear Information System (INIS)

Ching, W-H; K H Leung, Michael; Leung, Dennis Y C

2009-01-01

Transient turbulent dispersion phenomena can be found in various practical problems, such as the accidental release of toxic chemical vapor and the airborne transmission of infectious droplets. Computational fluid dynamics (CFD) is an effective tool for analyzing such transient dispersion behaviors. However, the transient CFD analysis is often computationally expensive and time consuming. In the present study, a computationally efficient CFD-statistical hybrid modeling method has been developed for studying transient turbulent dispersion. In this method, the source emission is represented by emissions of many infinitesimal puffs. Statistical analysis is performed to obtain first the statistical properties of the puff trajectories and subsequently the most probable distribution of the puff trajectories that represent the macroscopic dispersion behaviors. In two case studies of ambient dispersion, the numerical modeling results obtained agree reasonably well with both experimental measurements and conventional k-ε modeling results published in the literature. More importantly, the proposed many-puff CFD-statistical hybrid modeling method effectively reduces the computational time by two orders of magnitude.

Statistical modelling for ship propulsion efficiency

DEFF Research Database (Denmark)

Petersen, Jóan Petur; Jacobsen, Daniel J.; Winther, Ole

2012-01-01

This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks...

A statistical-dynamical modeling approach for the simulation of local paleo proxy records using GCM output

Energy Technology Data Exchange (ETDEWEB)

Reichert, B.K.; Bengtsson, L. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Aakesson, O. [Sveriges Meteorologiska och Hydrologiska Inst., Norrkoeping (Sweden)

1998-08-01

Recent proxy data obtained from ice core measurements, dendrochronology and valley glaciers provide important information on the evolution of the regional or local climate. General circulation models integrated over a long period of time could help to understand the (external and internal) forcing mechanisms of natural climate variability. For a systematic interpretation of in situ paleo proxy records, a combined method of dynamical and statistical modeling is proposed. Local 'paleo records' can be simulated from GCM output by first undertaking a model-consistent statistical downscaling and then using a process-based forward modeling approach to obtain the behavior of valley glaciers and the growth of trees under specific conditions. The simulated records can be compared to actual proxy records in order to investigate whether e.g. the response of glaciers to climatic change can be reproduced by models and to what extent climate variability obtained from proxy records (with the main focus on the last millennium) can be represented. For statistical downscaling to local weather conditions, a multiple linear forward regression model is used. Daily sets of observed weather station data and various large-scale predictors at 7 pressure levels obtained from ECMWF reanalyses are used for development of the model. Daily data give the closest and most robust relationships due to the strong dependence on individual synoptic-scale patterns. For some local variables, the performance of the model can be further increased by developing seasonal specific statistical relationships. The model is validated using both independent and restricted predictor data sets. The model is applied to a long integration of a mixed layer GCM experiment simulating pre-industrial climate variability. The dynamical-statistical local GCM output within a region around Nigardsbreen glacier, Norway is compared to nearby observed station data for the period 1868-1993. Patterns of observed

Sensometrics: Thurstonian and Statistical Models

DEFF Research Database (Denmark)

Christensen, Rune Haubo Bojesen

. sensR is a package for sensory discrimination testing with Thurstonian models and ordinal supports analysis of ordinal data with cumulative link (mixed) models. While sensR is closely connected to the sensometrics field, the ordinal package has developed into a generic statistical package applicable......This thesis is concerned with the development and bridging of Thurstonian and statistical models for sensory discrimination testing as applied in the scientific discipline of sensometrics. In sensory discrimination testing sensory differences between products are detected and quantified by the use...... and sensory discrimination testing in particular in a series of papers by advancing Thurstonian models for a range of sensory discrimination protocols in addition to facilitating their application by providing software for fitting these models. The main focus is on identifying Thurstonian models...

Comparison of neutron capture cross sections obtained from two Hauser-Feshbach statistical models on a short-lived nucleus using experimentally constrained input

Science.gov (United States)

Lewis, Rebecca; Liddick, Sean; Spyrou, Artemis; Crider, Benjamin; Dombos, Alexander; Naqvi, Farheen; Prokop, Christopher; Quinn, Stephen; Larsen, Ann-Cecilie; Crespo Campo, Lucia; Guttormsen, Magne; Renstrom, Therese; Siem, Sunniva; Bleuel, Darren; Couture, Aaron; Mosby, Shea; Perdikakis, George

2017-09-01

A majority of the abundance of the elements above iron are produced by neutron capture reactions, and, in explosive stellar processes, many of these reactions take place on unstable nuclei. Direct neutron capture experiments can only be performed on stable and long-lived nuclei, requiring indirect methods for the remaining isotopes. Statistical neutron capture can be described using the nuclear level density (NLD), the γ strength function (γSF), and an optical model. The NLD and γSF can be obtained using the β-Oslo method. The NLD and γSF were recently determined for 74Zn using the β-Oslo method, and were used in both TALYS and CoH to calculate the 73Zn(n, γ)74Zn neutron capture cross section. The cross sections calculated in TALYS and CoH are expected to be identical if the inputs for both codes are the same, however, after a thorough investigation into the inputs for the 73Zn(n, γ)74Zn reaction there is still a factor of two discrepancy between the two codes.

Statistical modelling for social researchers principles and practice

CERN Document Server

Tarling, Roger

2008-01-01

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-l...

Topology for statistical modeling of petascale data.

Energy Technology Data Exchange (ETDEWEB)

Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)

2011-07-01

This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.

Bayesian models: A statistical primer for ecologists

Science.gov (United States)

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

Statistical Model-Based Face Pose Estimation

Institute of Scientific and Technical Information of China (English)

GE Xinliang; YANG Jie; LI Feng; WANG Huahua

2007-01-01

A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.

Schedulability of Herschel revisited using statistical model checking

DEFF Research Database (Denmark)

David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel

2015-01-01

-approximation technique. We can safely conclude that the system is schedulable for varying values of BCET. For the cases where deadlines are violated, we use polyhedra to try to confirm the witnesses. Our alternative method to confirm non-schedulability uses statistical model-checking (SMC) to generate counter...... and blocking times of tasks. Consequently, the method may falsely declare deadline violations that will never occur during execution. This paper is a continuation of previous work of the authors in applying extended timed automata model checking (using the tool UPPAAL) to obtain more exact schedulability...... analysis, here in the presence of non-deterministic computation times of tasks given by intervals [BCET,WCET]. Computation intervals with preemptive schedulers make the schedulability analysis of the resulting task model undecidable. Our contribution is to propose a combination of model checking techniques...

Damages detection in cylindrical metallic specimens by means of statistical baseline models and updated daily temperature profiles

Science.gov (United States)

Villamizar-Mejia, Rodolfo; Mujica-Delgado, Luis-Eduardo; Ruiz-Ordóñez, Magda-Liliana; Camacho-Navarro, Jhonatan; Moreno-Beltrán, Gustavo

2017-05-01

In previous works, damage detection of metallic specimens exposed to temperature changes has been achieved by using a statistical baseline model based on Principal Component Analysis (PCA), piezodiagnostics principle and taking into account temperature effect by augmenting the baseline model or by using several baseline models according to the current temperature. In this paper a new approach is presented, where damage detection is based in a new index that combine Q and T2 statistical indices with current temperature measurements. Experimental tests were achieved in a carbon-steel pipe of 1m length and 1.5 inches diameter, instrumented with piezodevices acting as actuators or sensors. A PCA baseline model was obtained to a temperature of 21º and then T2 and Q statistical indices were obtained for a 24h temperature profile. Also, mass adding at different points of pipe between sensor and actuator was used as damage. By using the combined index the temperature contribution can be separated and a better differentiation of damages respect to undamaged cases can be graphically obtained.

Statistical intercomparison of global climate models: A common principal component approach with application to GCM data

International Nuclear Information System (INIS)

Sengupta, S.K.; Boyle, J.S.

1993-05-01

Variables describing atmospheric circulation and other climate parameters derived from various GCMs and obtained from observations can be represented on a spatio-temporal grid (lattice) structure. The primary objective of this paper is to explore existing as well as some new statistical methods to analyze such data structures for the purpose of model diagnostics and intercomparison from a statistical perspective. Among the several statistical methods considered here, a new method based on common principal components appears most promising for the purpose of intercomparison of spatio-temporal data structures arising in the task of model/model and model/data intercomparison. A complete strategy for such an intercomparison is outlined. The strategy includes two steps. First, the commonality of spatial structures in two (or more) fields is captured in the common principal vectors. Second, the corresponding principal components obtained as time series are then compared on the basis of similarities in their temporal evolution

Statistical Modelling of Resonant Cross Section Structure in URR, Model of the Characteristic Function

International Nuclear Information System (INIS)

Koyumdjieva, N.

2006-01-01

A statistical model for the resonant cross section structure in the Unresolved Resonance Region has been developed in the framework of the R-matrix formalism in Reich Moore approach with effective accounting of the resonance parameters fluctuations. The model uses only the average resonance parameters and can be effectively applied for analyses of cross sections functional, averaged over many resonances. Those are cross section moments, transmission and self-indication functions measured through thick sample. In this statistical model the resonant cross sections structure is accepted to be periodic and the R-matrix is a function of ε=E/D with period 0≤ε≤N; R nc (ε)=π/2√(S n *S c )1/NΣ(i=1,N)(β in *β ic *ctg[π(ε i - = ε-iS i )/N]; Here S n ,S c ,S i is respectively neutron strength function, strength function for fission or inelastic channel and strength function for radiative capture, N is the number of resonances (ε i ,β i ) that obey the statistic of Porter-Thomas and Wigner's one. The simple case of this statistical model concerns the resonant cross section structure for non-fissile nuclei under the threshold for inelastic scattering - the model of the characteristic function with HARFOR program. In the above model some improvements of calculation of the phases and logarithmic derivatives of neutron channels have been done. In the parameterization we use the free parameter R l ∞ , which accounts the influence of long-distant resonances. The above scheme for statistical modelling of the resonant cross section structure has been applied for evaluation of experimental data for total, capture and inelastic cross sections for 232 Th in the URR (4-150) keV and also the transmission and self-indication functions in (4-175) keV. The set of evaluated average resonance parameters have been obtained. The evaluated average resonance parameters in the URR are consistent with those in the Resolved Resonance Region (CRP for Th-U cycle, Vienna, 2006

The application of convolution-based statistical model on the electrical breakdown time delay distributions in neon

International Nuclear Information System (INIS)

Maluckov, Cedomir A.; Karamarkovic, Jugoslav P.; Radovic, Miodrag K.; Pejovic, Momcilo M.

2004-01-01

The convolution-based model of the electrical breakdown time delay distribution is applied for statistical analysis of experimental results obtained in neon-filled diode tube at 6.5 mbar. At first, the numerical breakdown time delay density distributions are obtained by stochastic modeling as the sum of two independent random variables, the electrical breakdown statistical time delay with exponential, and discharge formative time with Gaussian distribution. Then, the single characteristic breakdown time delay distribution is obtained as the convolution of these two random variables with previously determined parameters. These distributions show good correspondence with the experimental distributions, obtained on the basis of 1000 successive and independent measurements. The shape of distributions is investigated, and corresponding skewness and kurtosis are plotted, in order to follow the transition from Gaussian to exponential distribution

Statistical modelling of transcript profiles of differentially regulated genes

Directory of Open Access Journals (Sweden)

Sergeant Martin J

2008-07-01

allowed 11% of the Escherichia coli features to be fitted by an exponential function, and 25% of the Rattus norvegicus features could be described by the critical exponential model, all with statistical significance of p Conclusion The statistical non-linear regression approaches presented in this study provide detailed biologically oriented descriptions of individual gene expression profiles, using biologically variable data to generate a set of defining parameters. These approaches have application to the modelling and greater interpretation of profiles obtained across a wide range of platforms, such as microarrays. Through careful choice of appropriate model forms, such statistical regression approaches allow an improved comparison of gene expression profiles, and may provide an approach for the greater understanding of common regulatory mechanisms between genes.

Analysis of Statistical Distributions Used for Modeling Reliability and Failure Rate of Temperature Alarm Circuit

International Nuclear Information System (INIS)

EI-Shanshoury, G.I.

2011-01-01

Several statistical distributions are used to model various reliability and maintainability parameters. The applied distribution depends on the' nature of the data being analyzed. The presented paper deals with analysis of some statistical distributions used in reliability to reach the best fit of distribution analysis. The calculations rely on circuit quantity parameters obtained by using Relex 2009 computer program. The statistical analysis of ten different distributions indicated that Weibull distribution gives the best fit distribution for modeling the reliability of the data set of Temperature Alarm Circuit (TAC). However, the Exponential distribution is found to be the best fit distribution for modeling the failure rate

Small nodule detectability evaluation using a generalized scan-statistic model

International Nuclear Information System (INIS)

Popescu, Lucretiu M; Lewitt, Robert M

2006-01-01

In this paper is investigated the use of the scan statistic for evaluating the detectability of small nodules in medical images. The scan-statistic method is often used in applications in which random fields must be searched for abnormal local features. Several results of the detection with localization theory are reviewed and a generalization is presented using the noise nodule distribution obtained by scanning arbitrary areas. One benefit of the noise nodule model is that it enables determination of the scan-statistic distribution by using only a few image samples in a way suitable both for simulation and experimental setups. Also, based on the noise nodule model, the case of multiple targets per image is addressed and an image abnormality test using the likelihood ratio and an alternative test using multiple decision thresholds are derived. The results obtained reveal that in the case of low contrast nodules or multiple nodules the usual test strategy based on a single decision threshold underperforms compared with the alternative tests. That is a consequence of the fact that not only the contrast or the size, but also the number of suspicious nodules is a clue indicating the image abnormality. In the case of the likelihood ratio test, the multiple clues are unified in a single decision variable. Other tests that process multiple clues differently do not necessarily produce a unique ROC curve, as shown in examples using a test involving two decision thresholds. We present examples with two-dimensional time-of-flight (TOF) and non-TOF PET image sets analysed using the scan statistic for different search areas, as well as the fixed position observer

Simple statistical model for branched aggregates

DEFF Research Database (Denmark)

Lemarchand, Claire; Hansen, Jesper Schmidt

2015-01-01

, given that it already has bonds with others. The model is applied here to asphaltene nanoaggregates observed in molecular dynamics simulations of Cooee bitumen. The variation with temperature of the probabilities deduced from this model is discussed in terms of statistical mechanics arguments....... The relevance of the statistical model in the case of asphaltene nanoaggregates is checked by comparing the predicted value of the probability for one molecule to have exactly i bonds with the same probability directly measured in the molecular dynamics simulations. The agreement is satisfactory......We propose a statistical model that can reproduce the size distribution of any branched aggregate, including amylopectin, dendrimers, molecular clusters of monoalcohols, and asphaltene nanoaggregates. It is based on the conditional probability for one molecule to form a new bond with a molecule...

Matrix Tricks for Linear Statistical Models

CERN Document Server

Puntanen, Simo; Styan, George PH

2011-01-01

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and

Statistical Model Checking of Rich Models and Properties

DEFF Research Database (Denmark)

Poulsen, Danny Bøgsted

in undecidability issues for the traditional model checking approaches. Statistical model checking has proven itself a valuable supplement to model checking and this thesis is concerned with extending this software validation technique to stochastic hybrid systems. The thesis consists of two parts: the first part...... motivates why existing model checking technology should be supplemented by new techniques. It also contains a brief introduction to probability theory and concepts covered by the six papers making up the second part. The first two papers are concerned with developing online monitoring techniques...... systems. The fifth paper shows how stochastic hybrid automata are useful for modelling biological systems and the final paper is concerned with showing how statistical model checking is efficiently distributed. In parallel with developing the theory contained in the papers, a substantial part of this work...

Statistical Modelling of Wind Proles - Data Analysis and Modelling

DEFF Research Database (Denmark)

Jónsson, Tryggvi; Pinson, Pierre

The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....

Statistical physics of pairwise probability models

DEFF Research Database (Denmark)

Roudi, Yasser; Aurell, Erik; Hertz, John

2009-01-01

(dansk abstrakt findes ikke) Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of  data......: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying...

Automatic generation of statistical pose and shape models for articulated joints.

Science.gov (United States)

Xin Chen; Graham, Jim; Hutchinson, Charles; Muir, Lindsay

2014-02-01

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

Statistical evaluation of the data obtained from the K East Basin Sandfilter Backwash Pit samples

International Nuclear Information System (INIS)

Welsh, T.L.

1994-01-01

Samples were obtained from different locations from the K Each Sandfilter Backwash Pit to characterize the sludge material. These samples were analyzed chemically for elements, radionuclides, and residual compounds. The analytical results were statistically analyzed to determine the mean analyte content and the associated variability for each mean value

Dose-rate and temperature dependent statistical damage accumulation model for ion implantation into silicon

Energy Technology Data Exchange (ETDEWEB)

Hernandez-Mangas, J.M. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain)]. E-mail: jesus.hernandez.mangas@tel.uva.es; Arias, J. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Marques, L.A. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Ruiz-Bueno, A. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain); Bailon, L. [Dpto. de Electricidad y Electronica, Universidad de Valladolid, ETSI Telecomunicaciones, Campus Miguel Delibes, Valladolid E-47011 (Spain)

2005-01-01

Currently there are extensive atomistic studies that model some characteristics of the damage buildup due to ion irradiation (e.g. L. Pelaz et al., Appl. Phys. Lett. 82 (2003) 2038-2040). Our interest is to develop a novel statistical damage buildup model for our BCA ion implant simulator (IIS) code in order to extend its ranges of applicability. The model takes into account the abrupt regime of the crystal-amorphous transition. It works with different temperatures and dose-rates and also models the transition temperature. We have tested it with some projectiles (Ge, P) implanted into silicon. In this work we describe the new statistical damage accumulation model based on the modified Kinchin-Pease model. The results obtained have been compared with existing experimental results.

Dose-rate and temperature dependent statistical damage accumulation model for ion implantation into silicon

International Nuclear Information System (INIS)

Hernandez-Mangas, J.M.; Arias, J.; Marques, L.A.; Ruiz-Bueno, A.; Bailon, L.

2005-01-01

Currently there are extensive atomistic studies that model some characteristics of the damage buildup due to ion irradiation (e.g. L. Pelaz et al., Appl. Phys. Lett. 82 (2003) 2038-2040). Our interest is to develop a novel statistical damage buildup model for our BCA ion implant simulator (IIS) code in order to extend its ranges of applicability. The model takes into account the abrupt regime of the crystal-amorphous transition. It works with different temperatures and dose-rates and also models the transition temperature. We have tested it with some projectiles (Ge, P) implanted into silicon. In this work we describe the new statistical damage accumulation model based on the modified Kinchin-Pease model. The results obtained have been compared with existing experimental results

A statistical model for interpreting computerized dynamic posturography data

Science.gov (United States)

Feiveson, Alan H.; Metter, E. Jeffrey; Paloski, William H.

2002-01-01

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

Using the Expectancy Value Model of Motivation to Understand the Relationship between Student Attitudes and Achievement in Statistics

Science.gov (United States)

Hood, Michelle; Creed, Peter A.; Neumann, David L.

2012-01-01

We tested a model of the relationship between attitudes toward statistics and achievement based on Eccles' Expectancy Value Model (1983). Participants (n = 149; 83% female) were second-year Australian university students in a psychology statistics course (mean age = 23.36 years, SD = 7.94 years). We obtained demographic details, past performance,…

Uncertainty the soul of modeling, probability & statistics

CERN Document Server

Briggs, William

2016-01-01

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...

New statistical model of inelastic fast neutron scattering

International Nuclear Information System (INIS)

Stancicj, V.

1975-07-01

A new statistical model for treating the fast neutron inelastic scattering has been proposed by using the general expressions of the double differential cross section in impuls approximation. The use of the Fermi-Dirac distribution of nucleons makes it possible to derive an analytical expression of the fast neutron inelastic scattering kernel including the angular momenta coupling. The obtained values of the inelastic fast neutron cross section calculated from the derived expression of the scattering kernel are in a good agreement with the experiments. A main advantage of the derived expressions is in their simplicity for the practical calculations

Statistical Models for Social Networks

NARCIS (Netherlands)

Snijders, Tom A. B.; Cook, KS; Massey, DS

2011-01-01

Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

Statistical prediction of AVB wear growth and initiation in model F steam generator tubes using Monte Carlo method

International Nuclear Information System (INIS)

Lee, Jae Bong; Park, Jae Hak; Kim, Hong Deok; Chung, Han Sub; Kim, Tae Ryong

2005-01-01

The growth of AVB wear in Model F steam generator tubes is predicted using the Monte Carlo Method and statistical approaches. The statistical parameters that represent the characteristics of wear growth and wear initiation are derived from In-Service Inspection (ISI) Non-Destructive Evaluation (NDE) data. Based on the statistical approaches, wear growth model are proposed and applied to predict wear distribution at the End Of Cycle (EOC). Probabilistic distributions of the number of wear flaws and maximum wear depth at EOC are obtained from the analysis. Comparing the predicted EOC wear flaw data with the known EOC data the usefulness of the proposed method is examined and satisfactory results are obtained

Statistical prediction of AVB wear growth and initiation in model F steam generator tubes using Monte Carlo method

Energy Technology Data Exchange (ETDEWEB)

Lee, Jae Bong; Park, Jae Hak [Chungbuk National Univ., Cheongju (Korea, Republic of); Kim, Hong Deok; Chung, Han Sub; Kim, Tae Ryong [Korea Electtric Power Research Institute, Daejeon (Korea, Republic of)

2005-07-01

The growth of AVB wear in Model F steam generator tubes is predicted using the Monte Carlo Method and statistical approaches. The statistical parameters that represent the characteristics of wear growth and wear initiation are derived from In-Service Inspection (ISI) Non-Destructive Evaluation (NDE) data. Based on the statistical approaches, wear growth model are proposed and applied to predict wear distribution at the End Of Cycle (EOC). Probabilistic distributions of the number of wear flaws and maximum wear depth at EOC are obtained from the analysis. Comparing the predicted EOC wear flaw data with the known EOC data the usefulness of the proposed method is examined and satisfactory results are obtained.

Huffman and linear scanning methods with statistical language models.

Science.gov (United States)

Roark, Brian; Fried-Oken, Melanie; Gibbons, Chris

2015-03-01

Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning.

Functional summary statistics for the Johnson-Mehl model

DEFF Research Database (Denmark)

Møller, Jesper; Ghorbani, Mohammad

The Johnson-Mehl germination-growth model is a spatio-temporal point process model which among other things have been used for the description of neurotransmitters datasets. However, for such datasets parametric Johnson-Mehl models fitted by maximum likelihood have yet not been evaluated by means...... of functional summary statistics. This paper therefore invents four functional summary statistics adapted to the Johnson-Mehl model, with two of them based on the second-order properties and the other two on the nuclei-boundary distances for the associated Johnson-Mehl tessellation. The functional summary...... statistics theoretical properties are investigated, non-parametric estimators are suggested, and their usefulness for model checking is examined in a simulation study. The functional summary statistics are also used for checking fitted parametric Johnson-Mehl models for a neurotransmitters dataset....

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

Science.gov (United States)

Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P

1999-01-01

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149

Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment

Directory of Open Access Journals (Sweden)

Yuze Wang

2018-04-01

Full Text Available A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.

Distributions with given marginals and statistical modelling

CERN Document Server

Fortiana, Josep; Rodriguez-Lallena, José

2002-01-01

This book contains a selection of the papers presented at the meeting `Distributions with given marginals and statistical modelling', held in Barcelona (Spain), July 17-20, 2000. In 24 chapters, this book covers topics such as the theory of copulas and quasi-copulas, the theory and compatibility of distributions, models for survival distributions and other well-known distributions, time series, categorical models, definition and estimation of measures of dependence, monotonicity and stochastic ordering, shape and separability of distributions, hidden truncation models, diagonal families, orthogonal expansions, tests of independence, and goodness of fit assessment. These topics share the use and properties of distributions with given marginals, this being the fourth specialised text on this theme. The innovative aspect of the book is the inclusion of statistical aspects such as modelling, Bayesian statistics, estimation, and tests.

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Science.gov (United States)

Alizadeh Moghaddam, S. H.; Mokhtarzade, M.; Alizadeh Naeini, A.; Alizadeh Moghaddam, S. A.

2017-09-01

Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs' parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs' overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs' parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50-80% over the TR.

Statistical modeling of the response characteristics of mechanosensitive stimuli in the human esophagus

DEFF Research Database (Denmark)

Drewes, A.M.; Reddy, H.; Staahl, C.

2005-01-01

by using a statistical model based on correlation analysis. The esophagus was distended with a bag in 32 healthy subjects by using an inflation rate of 25 mL/min. The luminal cross-sectional areas and sensory ratings were determined during the distentions. The stimuli were repeated after relaxation...... of mechanical gut stimuli in human beings. This might increase our understanding of visceral pain in health and disease and guide the statistical analysis of experimental data obtained in the gastrointestinal tract....

Actuarial statistics with generalized linear mixed models

NARCIS (Netherlands)

Antonio, K.; Beirlant, J.

2007-01-01

Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics

Statistical pairwise interaction model of stock market

Science.gov (United States)

Bury, Thomas

2013-03-01

Financial markets are a classical example of complex systems as they are compound by many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price of restrictive assumptions on the market dynamics or they are agent-based models with rules designed in order to recover some empirical behaviors. Here we show that the pairwise model is actually a statistically consistent model with the observed first and second moments of the stocks orientation without making such restrictive assumptions. This is done with an approach only based on empirical data of price returns. Our data analysis of six major indices suggests that the actual interaction structure may be thought as an Ising model on a complex network with interaction strengths scaling as the inverse of the system size. This has potentially important implications since many properties of such a model are already known and some techniques of the spin glass theory can be straightforwardly applied. Typical behaviors, as multiple equilibria or metastable states, different characteristic time scales, spatial patterns, order-disorder, could find an explanation in this picture.

Structured statistical models of inductive reasoning.

Science.gov (United States)

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 mechanics of learning orthogonal signals for general covariance models

International Nuclear Information System (INIS)

Hoyle, David C

2010-01-01

Statistical mechanics techniques have proved to be useful tools in quantifying the accuracy with which signal vectors are extracted from experimental data. However, analysis has previously been limited to specific model forms for the population covariance C, which may be inappropriate for real world data sets. In this paper we obtain new statistical mechanical results for a general population covariance matrix C. For data sets consisting of p sample points in R N we use the replica method to study the accuracy of orthogonal signal vectors estimated from the sample data. In the asymptotic limit of N,p→∞ at fixed α = p/N, we derive analytical results for the signal direction learning curves. In the asymptotic limit the learning curves follow a single universal form, each displaying a retarded learning transition. An explicit formula for the location of the retarded learning transition is obtained and we find marked variation in the location of the retarded learning transition dependent on the distribution of population covariance eigenvalues. The results of the replica analysis are confirmed against simulation

Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

DEFF Research Database (Denmark)

Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

2013-01-01

A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

Statistical modelling in biostatistics and bioinformatics selected papers

CERN Document Server

Peng, Defen

2014-01-01

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...

Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.

Science.gov (United States)

Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J

2016-10-03

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

Zubarev's Nonequilibrium Statistical Operator Method in the Generalized Statistics of Multiparticle Systems

Science.gov (United States)

Glushak, P. A.; Markiv, B. B.; Tokarchuk, M. V.

2018-01-01

We present a generalization of Zubarev's nonequilibrium statistical operator method based on the principle of maximum Renyi entropy. In the framework of this approach, we obtain transport equations for the basic set of parameters of the reduced description of nonequilibrium processes in a classical system of interacting particles using Liouville equations with fractional derivatives. For a classical systems of particles in a medium with a fractal structure, we obtain a non-Markovian diffusion equation with fractional spatial derivatives. For a concrete model of the frequency dependence of a memory function, we obtain generalized Kettano-type diffusion equation with the spatial and temporal fractality taken into account. We present a generalization of nonequilibrium thermofield dynamics in Zubarev's nonequilibrium statistical operator method in the framework of Renyi statistics.

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error.

Science.gov (United States)

Monica, Stefania; Ferrari, Gianluigi

2018-05-17

Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%.

Statistical mechanics and dynamics of solvable models with long-range interactions

International Nuclear Information System (INIS)

Campa, Alessandro; Dauxois, Thierry; Ruffo, Stefano

2009-01-01

For systems with long-range interactions, the two-body potential decays at large distances as V(r)∼1/r α , with α≤d, where d is the space dimension. Examples are: gravitational systems, two-dimensional hydrodynamics, two-dimensional elasticity, charged and dipolar systems. Although such systems can be made extensive, they are intrinsically non additive: the sum of the energies of macroscopic subsystems is not equal to the energy of the whole system. Moreover, the space of accessible macroscopic thermodynamic parameters might be non convex. The violation of these two basic properties of the thermodynamics of short-range systems is at the origin of ensemble inequivalence. In turn, this inequivalence implies that specific heat can be negative in the microcanonical ensemble, and temperature jumps can appear at microcanonical first order phase transitions. The lack of convexity allows us to easily spot regions of parameter space where ergodicity may be broken. Historically, negative specific heat had been found for gravitational systems and was thought to be a specific property of a system for which the existence of standard equilibrium statistical mechanics itself was doubted. Realizing that such properties may be present for a wider class of systems has renewed the interest in long-range interactions. Here, we present a comprehensive review of the recent advances on the statistical mechanics and out-of-equilibrium dynamics of solvable systems with long-range interactions. The core of the review consists in the detailed presentation of the concept of ensemble inequivalence, as exemplified by the exact solution, in the microcanonical and canonical ensembles, of mean-field type models. Remarkably, the entropy of all these models can be obtained using the method of large deviations. Long-range interacting systems display an extremely slow relaxation towards thermodynamic equilibrium and, what is more striking, the convergence towards quasi-stationary states. The

A generalized statistical model for the size distribution of wealth

International Nuclear Information System (INIS)

Clementi, F; Gallegati, M; Kaniadakis, G

2012-01-01

In a recent paper in this journal (Clementi et al 2009 J. Stat. Mech. P02037), we proposed a new, physically motivated, distribution function for modeling individual incomes, having its roots in the framework of the κ-generalized statistical mechanics. The performance of the κ-generalized distribution was checked against real data on personal income for the United States in 2003. In this paper we extend our previous model so as to be able to account for the distribution of wealth. Probabilistic functions and inequality measures of this generalized model for wealth distribution are obtained in closed form. In order to check the validity of the proposed model, we analyze the US household wealth distributions from 1984 to 2009 and conclude an excellent agreement with the data that is superior to any other model already known in the literature. (paper)

A generalized statistical model for the size distribution of wealth

Science.gov (United States)

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

2012-12-01

In a recent paper in this journal (Clementi et al 2009 J. Stat. Mech. P02037), we proposed a new, physically motivated, distribution function for modeling individual incomes, having its roots in the framework of the κ-generalized statistical mechanics. The performance of the κ-generalized distribution was checked against real data on personal income for the United States in 2003. In this paper we extend our previous model so as to be able to account for the distribution of wealth. Probabilistic functions and inequality measures of this generalized model for wealth distribution are obtained in closed form. In order to check the validity of the proposed model, we analyze the US household wealth distributions from 1984 to 2009 and conclude an excellent agreement with the data that is superior to any other model already known in the literature.

Statistical approach for uncertainty quantification of experimental modal model parameters

DEFF Research Database (Denmark)

Luczak, M.; Peeters, B.; Kahsin, M.

2014-01-01

Composite materials are widely used in manufacture of aerospace and wind energy structural components. These load carrying structures are subjected to dynamic time-varying loading conditions. Robust structural dynamics identification procedure impose tight constraints on the quality of modal models...... represent different complexity levels ranging from coupon, through sub-component up to fully assembled aerospace and wind energy structural components made of composite materials. The proposed method is demonstrated on two application cases of a small and large wind turbine blade........ This paper aims at a systematic approach for uncertainty quantification of the parameters of the modal models estimated from experimentally obtained data. Statistical analysis of modal parameters is implemented to derive an assessment of the entire modal model uncertainty measure. Investigated structures...

A Stochastic Fractional Dynamics Model of Rainfall Statistics

Science.gov (United States)

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.

Statistical Models and Methods for Lifetime Data

CERN Document Server

Lawless, Jerald F

2011-01-01

Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis problems."-Biometrics"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."-Statistics in MedicineThe statistical analysis of lifetime or response time data is a key tool in engineering,

Topology for Statistical Modeling of Petascale Data

Energy Technology Data Exchange (ETDEWEB)

Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Bremer, P. -T. [Univ. of Utah, Salt Lake City, UT (United States)

2013-10-31

Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.

Current algebra, statistical mechanics and quantum models

Science.gov (United States)

Vilela Mendes, R.

2017-11-01

Results obtained in the past for free boson systems at zero and nonzero temperatures are revisited to clarify the physical meaning of current algebra reducible functionals which are associated to systems with density fluctuations, leading to observable effects on phase transitions. To use current algebra as a tool for the formulation of quantum statistical mechanics amounts to the construction of unitary representations of diffeomorphism groups. Two mathematical equivalent procedures exist for this purpose. One searches for quasi-invariant measures on configuration spaces, the other for a cyclic vector in Hilbert space. Here, one argues that the second approach is closer to the physical intuition when modelling complex systems. An example of application of the current algebra methodology to the pairing phenomenon in two-dimensional fermion systems is discussed.

Statistical models and methods for reliability and survival analysis

CERN Document Server

Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

2013-01-01

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

Experiments performed with a functional model based on statistical discrimination in mixed nuclear radiation field

International Nuclear Information System (INIS)

Valcov, N.; Celarel, A.; Purghel, L.

1999-01-01

By using the statistical discrimination technique, the components of on ionization current, due to a mixed radiation field, may be simultaneously measured. A functional model, including a serially manufactured gamma-ray ratemeter was developed, as an intermediate step in the design of specialised nuclear instrumentation, in order to check the concept of statistical discrimination method. The obtained results are in good agreement with the estimations of the statistical discrimination method. The main characteristics of the functional model are the following: - dynamic range of measurement: >300: l; - simultaneous measurement of natural radiation background and gamma-ray fields; - accuracy (for equal exposure rates from gamma's and natural radiation background): 17%, for both radiation fields; - minimum detectable exposure rate: 2μR/h. (authors)

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

Energy Technology Data Exchange (ETDEWEB)

Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-05-11

This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.

Spherical Process Models for Global Spatial Statistics

KAUST Repository

Jeong, Jaehong

2017-11-28

Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems.

Model-generated air quality statistics for application in vegetation response models in Alberta

International Nuclear Information System (INIS)

McVehil, G.E.; Nosal, M.

1990-01-01

To test and apply vegetation response models in Alberta, air pollution statistics representative of various parts of the Province are required. At this time, air quality monitoring data of the requisite accuracy and time resolution are not available for most parts of Alberta. Therefore, there exists a need to develop appropriate air quality statistics. The objectives of the work reported here were to determine the applicability of model generated air quality statistics and to develop by modelling, realistic and representative time series of hourly SO 2 concentrations that could be used to generate the statistics demanded by vegetation response models

Performance modeling, loss networks, and statistical multiplexing

CERN Document Server

Mazumdar, Ravi

2009-01-01

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

Science.gov (United States)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

Statistical meandering wake model and its application to yaw-angle optimisation of wind farms

International Nuclear Information System (INIS)

Thøgersen, E; Tranberg, B; Greiner, M; Herp, J

2017-01-01

The wake produced by a wind turbine is dynamically meandering and of rather narrow nature. Only when looking at large time averages, the wake appears to be static and rather broad, and is then well described by simple engineering models like the Jensen wake model (JWM). We generalise the latter deterministic models to a statistical meandering wake model (SMWM), where a random directional deflection is assigned to a narrow wake in such a way that on average it resembles a broad Jensen wake. In a second step, the model is further generalised to wind-farm level, where the deflections of the multiple wakes are treated as independently and identically distributed random variables. When carefully calibrated to the Nysted wind farm, the ensemble average of the statistical model produces the same wind-direction dependence of the power efficiency as obtained from the standard Jensen model. Upon using the JWM to perform a yaw-angle optimisation of wind-farm power output, we find an optimisation gain of 6.7% for the Nysted wind farm when compared to zero yaw angles and averaged over all wind directions. When applying the obtained JWM-based optimised yaw angles to the SMWM, the ensemble-averaged gain is calculated to be 7.5%. This outcome indicates the possible operational robustness of an optimised yaw control for real-life wind farms. (paper)

Statistical meandering wake model and its application to yaw-angle optimisation of wind farms

Science.gov (United States)

Thøgersen, E.; Tranberg, B.; Herp, J.; Greiner, M.

2017-05-01

The wake produced by a wind turbine is dynamically meandering and of rather narrow nature. Only when looking at large time averages, the wake appears to be static and rather broad, and is then well described by simple engineering models like the Jensen wake model (JWM). We generalise the latter deterministic models to a statistical meandering wake model (SMWM), where a random directional deflection is assigned to a narrow wake in such a way that on average it resembles a broad Jensen wake. In a second step, the model is further generalised to wind-farm level, where the deflections of the multiple wakes are treated as independently and identically distributed random variables. When carefully calibrated to the Nysted wind farm, the ensemble average of the statistical model produces the same wind-direction dependence of the power efficiency as obtained from the standard Jensen model. Upon using the JWM to perform a yaw-angle optimisation of wind-farm power output, we find an optimisation gain of 6.7% for the Nysted wind farm when compared to zero yaw angles and averaged over all wind directions. When applying the obtained JWM-based optimised yaw angles to the SMWM, the ensemble-averaged gain is calculated to be 7.5%. This outcome indicates the possible operational robustness of an optimised yaw control for real-life wind farms.

Statistical Models of Adaptive Immune populations

Science.gov (United States)

Sethna, Zachary; Callan, Curtis; Walczak, Aleksandra; Mora, Thierry

The availability of large (104-106 sequences) datasets of B or T cell populations from a single individual allows reliable fitting of complex statistical models for naïve generation, somatic selection, and hypermutation. It is crucial to utilize a probabilistic/informational approach when modeling these populations. The inferred probability distributions allow for population characterization, calculation of probability distributions of various hidden variables (e.g. number of insertions), as well as statistical properties of the distribution itself (e.g. entropy). In particular, the differences between the T cell populations of embryonic and mature mice will be examined as a case study. Comparing these populations, as well as proposed mixed populations, provides a concrete exercise in model creation, comparison, choice, and validation.

Tropical geometry of statistical models.

Science.gov (United States)

Pachter, Lior; Sturmfels, Bernd

2004-11-16

This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

12th Workshop on Stochastic Models, Statistics and Their Applications

CERN Document Server

Rafajłowicz, Ewaryst; Szajowski, Krzysztof

2015-01-01

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.

Science.gov (United States)

Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong; Yan, Jin; Wu, Guorong

2017-06-01

Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity. In addition to the low level region-to-region signal correlation, latent module-to-module connection is also estimated and used to provide high level heuristics for measuring connectivity strength. Furthermore, sparsity constraint is employed to automatically remove the spurious connections, thus alleviating the issue of searching for optimal threshold. Second, we integrate our learning-based approach with the sliding-window technique to further reveal the dynamics of functional connectivity. Specifically, we stack the functional connectivity matrix within each sliding window and form a 3D tensor where the third dimension denotes for time. Then we obtain dynamic functional connectivity (dFC) for each individual subject by simultaneously estimating the within-sliding-window functional connectivity and characterizing the across-sliding-window temporal dynamics. Third, in order to enhance the robustness of the connectome patterns extracted from dFC, we extend the individual-based 3D tensors to a population-based 4D tensor (with the fourth dimension stands for the training subjects) and learn the statistics of connectome patterns via 4D tensor analysis. Since our 4D tensor model jointly (1) optimizes dFC for each training subject and (2) captures the principle connectome patterns, our statistical model gains more statistical power of representing new subject than current state

Statistical Validation of Engineering and Scientific Models: Background

International Nuclear Information System (INIS)

Hills, Richard G.; Trucano, Timothy G.

1999-01-01

A tutorial is presented discussing the basic issues associated with propagation of uncertainty analysis and statistical validation of engineering and scientific models. The propagation of uncertainty tutorial illustrates the use of the sensitivity method and the Monte Carlo method to evaluate the uncertainty in predictions for linear and nonlinear models. Four example applications are presented; a linear model, a model for the behavior of a damped spring-mass system, a transient thermal conduction model, and a nonlinear transient convective-diffusive model based on Burger's equation. Correlated and uncorrelated model input parameters are considered. The model validation tutorial builds on the material presented in the propagation of uncertainty tutoriaI and uses the damp spring-mass system as the example application. The validation tutorial illustrates several concepts associated with the application of statistical inference to test model predictions against experimental observations. Several validation methods are presented including error band based, multivariate, sum of squares of residuals, and optimization methods. After completion of the tutorial, a survey of statistical model validation literature is presented and recommendations for future work are made

Statistical models for thermal ageing of steel materials in nuclear power plants

International Nuclear Information System (INIS)

Persoz, M.

1996-01-01

Some category of steel materials in nuclear power plants may be subjected to thermal ageing, whose extent depends on the steel chemical composition and the ageing parameters, i.e. temperature and duration. This ageing affects the 'impact strength' of the materials, which is a mechanical property. In order to assess the residual lifetime of these components, a probabilistic study has been launched, which takes into account the scatter over the input parameters of the mechanical model. Predictive formulae for estimating the impact strength of aged materials are important input data of the model. A data base has been created with impact strength results obtained from an ageing program in laboratory and statistical treatments have been undertaken. Two kinds of model have been developed, with non linear regression methods (PROC NLIN, available in SAS/STAT). The first one, using a hyperbolic tangent function, is partly based on physical considerations, and the second one, of an exponential type, is purely statistically built. The difficulties consist in selecting the significant parameters and attributing initial values to the coefficients, which is a requirement of the NLIN procedure. This global statistical analysis has led to general models that are unction of the chemical variables and the ageing parameters. These models are as precise (if not more) as local models that had been developed earlier for some specific values of ageing temperature and ageing duration. This paper describes the data and the methodology used to build the models and analyses the results given by the SAS system. (author)

Statistical modeling in phenomenological description of electromagnetic cascade processes produced by high-energy gamma quanta

International Nuclear Information System (INIS)

Slowinski, B.

1987-01-01

A description of a simple phenomenological model of electromagnetic cascade process (ECP) initiated by high-energy gamma quanta in heavy absorbents is given. Within this model spatial structure and fluctuations of ionization losses of shower electrons and positrons are described. Concrete formulae have been obtained as a result of statistical analysis of experimental data from the xenon bubble chamber of ITEP (Moscow)

Multiple commodities in statistical microeconomics: Model and market

Science.gov (United States)

Baaquie, Belal E.; Yu, Miao; Du, Xin

2016-11-01

A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

Statistical models for optimizing mineral exploration

International Nuclear Information System (INIS)

Wignall, T.K.; DeGeoffroy, J.

1987-01-01

The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)

Statistical physics of pairwise probability models

Directory of Open Access Journals (Sweden)

Yasser Roudi

2009-11-01

Full Text Available Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.

Statistical power analysis a simple and general model for traditional and modern hypothesis tests

CERN Document Server

Murphy, Kevin R; Wolach, Allen

2014-01-01

Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to choose the appropriate criterion for defining statistically significant outcomes are sprinkled throughout. The book presents a simple and g

Some properties of the statistical models of the spherical Friedman world

International Nuclear Information System (INIS)

Rumyantseva, E.N.

1981-01-01

Considered are some properties of the statistical model of the spherical Friedman world filled with quantum gas of massless particles the spin of which can be equal to 0, 1/2, 1. Obtained are approximate expressions for a number of particles of Fermi- and Bose-gas and for the radius of the world taking into account mainly the dependence on the spin. The dependence of the number of particles on the radius of the world and temperature of the gas is illustrated [ru

Statistical mechanics of sparse generalization and graphical model selection

International Nuclear Information System (INIS)

Lage-Castellanos, Alejandro; Pagnani, Andrea; Weigt, Martin

2009-01-01

One of the crucial tasks in many inference problems is the extraction of an underlying sparse graphical model from a given number of high-dimensional measurements. In machine learning, this is frequently achieved using, as a penalty term, the L p norm of the model parameters, with p≤1 for efficient dilution. Here we propose a statistical mechanics analysis of the problem in the setting of perceptron memorization and generalization. Using a replica approach, we are able to evaluate the relative performance of naive dilution (obtained by learning without dilution, following by applying a threshold to the model parameters), L 1 dilution (which is frequently used in convex optimization) and L 0 dilution (which is optimal but computationally hard to implement). Whereas both L p diluted approaches clearly outperform the naive approach, we find a small region where L 0 works almost perfectly and strongly outperforms the simpler to implement L 1 dilution

A Fuzzy Modeling Approach for Replicated Response Measures Based on Fuzzification of Replications with Descriptive Statistics and Golden Ratio

Directory of Open Access Journals (Sweden)

Özlem TÜRKŞEN

2018-03-01

Full Text Available Some of the experimental designs can be composed of replicated response measures in which the replications cannot be identified exactly and may have uncertainty different than randomness. Then, the classical regression analysis may not be proper to model the designed data because of the violation of probabilistic modeling assumptions. In this case, fuzzy regression analysis can be used as a modeling tool. In this study, the replicated response values are newly formed to fuzzy numbers by using descriptive statistics of replications and golden ratio. The main aim of the study is obtaining the most suitable fuzzy model for replicated response measures through fuzzification of the replicated values by taking into account the data structure of the replications in statistical framework. Here, the response and unknown model coefficients are considered as triangular type-1 fuzzy numbers (TT1FNs whereas the inputs are crisp. Predicted fuzzy models are obtained according to the proposed fuzzification rules by using Fuzzy Least Squares (FLS approach. The performances of the predicted fuzzy models are compared by using Root Mean Squared Error (RMSE criteria. A data set from the literature, called wheel cover component data set, is used to illustrate the performance of the proposed approach and the obtained results are discussed. The calculation results show that the combined formulation of the descriptive statistics and the golden ratio is the most preferable fuzzification rule according to the well-known decision making method, called TOPSIS, for the data set.

A New Equivalent Statistical Damage Constitutive Model on Rock Block Mixed Up with Fluid Inclusions

Directory of Open Access Journals (Sweden)

Xiao Chen

2018-01-01

Full Text Available So far, there are few studies concerning the effect of closed “fluid inclusions” on the macroscopic constitutive relation of deep rock. Fluid-matrix element (FME is defined based on rock element in statistical damage model. The properties of FME are related to the size of inclusions, fluid properties, and pore pressure. Using FME, the equivalent elastic modulus of rock block containing fluid inclusions is obtained with Eshelby inclusion theory and the double M-T homogenization method. The new statistical damage model of rock is established on the equivalent elastic modulus. Besides, the porosity and confining pressure are important influencing factors of the model. The model reflects the initial damage (void and fluid inclusion and the macroscopic deformation law of rock, which is an improvement of the traditional statistical damage model. Additionally, the model can not only be consistent with the rock damage experiment date and three-axis compression experiment date of rock containing pore water but also describe the locked-in stress experiment in rock-like material. It is a new fundamental study of the constitutive relation of locked-in stress in deep rock mass.

Statistical transmutation in doped quantum dimer models.

Science.gov (United States)

Lamas, C A; Ralko, A; Cabra, D C; Poilblanc, D; Pujol, P

2012-07-06

We prove a "statistical transmutation" symmetry of doped quantum dimer models on the square, triangular, and kagome lattices: the energy spectrum is invariant under a simultaneous change of statistics (i.e., bosonic into fermionic or vice versa) of the holes and of the signs of all the dimer resonance loops. This exact transformation enables us to define the duality equivalence between doped quantum dimer Hamiltonians and provides the analytic framework to analyze dynamical statistical transmutations. We investigate numerically the doping of the triangular quantum dimer model with special focus on the topological Z(2) dimer liquid. Doping leads to four (instead of two for the square lattice) inequivalent families of Hamiltonians. Competition between phase separation, superfluidity, supersolidity, and fermionic phases is investigated in the four families.

Obtaining mathematical models for assessing efficiency of dust collectors using integrated system of analysis and data management STATISTICA Design of Experiments

Science.gov (United States)

Azarov, A. V.; Zhukova, N. S.; Kozlovtseva, E. Yu; Dobrinsky, D. R.

2018-05-01

The article considers obtaining mathematical models to assess the efficiency of the dust collectors using an integrated system of analysis and data management STATISTICA Design of Experiments. The procedure for obtaining mathematical models and data processing is considered by the example of laboratory studies on a mounted installation containing a dust collector in counter-swirling flows (CSF) using gypsum dust of various fractions. Planning of experimental studies has been carried out in order to reduce the number of experiments and reduce the cost of experimental research. A second-order non-position plan (Box-Bencken plan) was used, which reduced the number of trials from 81 to 27. The order of statistical data research of Box-Benken plan using standard tools of integrated system for analysis and data management STATISTICA Design of Experiments is considered. Results of statistical data processing with significance estimation of coefficients and adequacy of mathematical models are presented.

Textual information access statistical models

CERN Document Server

Gaussier, Eric

2013-01-01

This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access:- information extraction and retrieval;- text classification and clustering;- opinion mining;- comprehension aids (automatic summarization, machine translation, visualization).In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications

Use of multivariate statistics to identify unreliable data obtained using CASA.

Science.gov (United States)

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

Polish Phoneme Statistics Obtained On Large Set Of Written Texts

Directory of Open Access Journals (Sweden)

Bartosz Ziółko

2009-01-01

Full Text Available The phonetical statistics were collected from several Polish corpora. The paper is a summaryof the data which are phoneme n-grams and some phenomena in the statistics. Triphonestatistics apply context-dependent speech units which have an important role in speech recognitionsystems and were never calculated for a large set of Polish written texts. The standardphonetic alphabet for Polish, SAMPA, and methods of providing phonetic transcriptions are described.

Model for neural signaling leap statistics

International Nuclear Information System (INIS)

Chevrollier, Martine; Oria, Marcos

2011-01-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.

Model for neural signaling leap statistics

Science.gov (United States)

Chevrollier, Martine; Oriá, Marcos

2011-03-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.

WE-A-201-02: Modern Statistical Modeling

Energy Technology Data Exchange (ETDEWEB)

Niemierko, A.

2016-06-15

Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear

WE-A-201-02: Modern Statistical Modeling

International Nuclear Information System (INIS)

Niemierko, A.

2016-01-01

Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear

Bayesian models a statistical primer for ecologists

CERN Document Server

Hobbs, N Thompson

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili

Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for multi-compartment lumped modeling.

Science.gov (United States)

Huttary, Rudolf; Goubergrits, Leonid; Schütte, Christof; Bernhard, Stefan

2017-08-01

It has not yet been possible to obtain modeling approaches suitable for covering a wide range of real world scenarios in cardiovascular physiology because many of the system parameters are uncertain or even unknown. Natural variability and statistical variation of cardiovascular system parameters in healthy and diseased conditions are characteristic features for understanding cardiovascular diseases in more detail. This paper presents SISCA, a novel software framework for cardiovascular system modeling and its MATLAB implementation. The framework defines a multi-model statistical ensemble approach for dimension reduced, multi-compartment models and focuses on statistical variation, system identification and patient-specific simulation based on clinical data. We also discuss a data-driven modeling scenario as a use case example. The regarded dataset originated from routine clinical examinations and comprised typical pre and post surgery clinical data from a patient diagnosed with coarctation of aorta. We conducted patient and disease specific pre/post surgery modeling by adapting a validated nominal multi-compartment model with respect to structure and parametrization using metadata and MRI geometry. In both models, the simulation reproduced measured pressures and flows fairly well with respect to stenosis and stent treatment and by pre-treatment cross stenosis phase shift of the pulse wave. However, with post-treatment data showing unrealistic phase shifts and other more obvious inconsistencies within the dataset, the methods and results we present suggest that conditioning and uncertainty management of routine clinical data sets needs significantly more attention to obtain reasonable results in patient-specific cardiovascular modeling. Copyright © 2017 Elsevier Ltd. All rights reserved.

Equilibrium statistical mechanics of lattice models

CERN Document Server

Lavis, David A

2015-01-01

Most interesting and difficult problems in equilibrium statistical mechanics concern models which exhibit phase transitions. For graduate students and more experienced researchers this book provides an invaluable reference source of approximate and exact solutions for a comprehensive range of such models. Part I contains background material on classical thermodynamics and statistical mechanics, together with a classification and survey of lattice models. The geometry of phase transitions is described and scaling theory is used to introduce critical exponents and scaling laws. An introduction is given to finite-size scaling, conformal invariance and Schramm—Loewner evolution. Part II contains accounts of classical mean-field methods. The parallels between Landau expansions and catastrophe theory are discussed and Ginzburg—Landau theory is introduced. The extension of mean-field theory to higher-orders is explored using the Kikuchi—Hijmans—De Boer hierarchy of approximations. In Part III the use of alge...

Statistical Clustering and Compositional Modeling of Iapetus VIMS Spectral Data

Science.gov (United States)

Pinilla-Alonso, N.; Roush, T. L.; Marzo, G.; Dalle Ore, C. M.; Cruikshank, D. P.

2009-12-01

It has long been known that the surfaces of Saturn's major satellites are predominantly icy objects [e.g. 1 and references therein]. Since 2004, these bodies have been the subject of observations by the Cassini-VIMS (Visual and Infrared Mapping Spectrometer) experiment [2]. Iapetus has the unique property that the hemisphere centered on the apex of its locked synchronous orbital motion around Saturn has a very low geometrical albedo of 2-6%, while the opposite hemisphere is about 10 times more reflective. The nature and origin of the dark material of Iapetus has remained a question since its discovery [3 and references therein]. The nature of this material and how it is distributed on the surface of this body, can shed new light into the knowledge of the Saturnian system. We apply statistical clustering [4] and theoretical modeling [5,6] to address the surface composition of Iapetus. The VIMS data evaluated were obtained during the second flyby of Iapetus, in September 2007. This close approach allowed VIMS to obtain spectra at relatively high spatial resolution, ~1-22 km/pixel. The data we study sampled the trailing hemisphere and part of the dark leading one. The statistical clustering [4] is used to identify statistically distinct spectra on Iapetus. The composition of these distinct spectra are evaluated using theoretical models [5,6]. We thank Allan Meyer for his help. This research was supported by an appointment to the NASA Postdoctoral Program at the Ames Research Center, administered by Oak Ridge Associated Universities through a contract with NASA. [1] A, Coradini et al., 2009, Earth, Moon & Planets, 105, 289-310. [2] Brown et al., 2004, Space Science Reviews, 115, 111-168. [3] Cruikshank, D. et al Icarus, 2008, 193, 334-343. [4] Marzo, G. et al. 2008, Journal of Geophysical Research, 113, E12, CiteID E12009. [5] Hapke, B. 1993, Theory of reflectance and emittance spectroscopy, Cambridge University Press. [6] Shkuratov, Y. et al. 1999, Icarus, 137, 235-246.

Computational and Statistical Models: A Comparison for Policy Modeling of Childhood Obesity

Science.gov (United States)

Mabry, Patricia L.; Hammond, Ross; Ip, Edward Hak-Sing; Huang, Terry T.-K.

As systems science methodologies have begun to emerge as a set of innovative approaches to address complex problems in behavioral, social science, and public health research, some apparent conflicts with traditional statistical methodologies for public health have arisen. Computational modeling is an approach set in context that integrates diverse sources of data to test the plausibility of working hypotheses and to elicit novel ones. Statistical models are reductionist approaches geared towards proving the null hypothesis. While these two approaches may seem contrary to each other, we propose that they are in fact complementary and can be used jointly to advance solutions to complex problems. Outputs from statistical models can be fed into computational models, and outputs from computational models can lead to further empirical data collection and statistical models. Together, this presents an iterative process that refines the models and contributes to a greater understanding of the problem and its potential solutions. The purpose of this panel is to foster communication and understanding between statistical and computational modelers. Our goal is to shed light on the differences between the approaches and convey what kinds of research inquiries each one is best for addressing and how they can serve complementary (and synergistic) roles in the research process, to mutual benefit. For each approach the panel will cover the relevant "assumptions" and how the differences in what is assumed can foster misunderstandings. The interpretations of the results from each approach will be compared and contrasted and the limitations for each approach will be delineated. We will use illustrative examples from CompMod, the Comparative Modeling Network for Childhood Obesity Policy. The panel will also incorporate interactive discussions with the audience on the issues raised here.

Pre-equilibrium assumptions and statistical model parameters effects on reaction cross-section calculations

International Nuclear Information System (INIS)

Avrigeanu, M.; Avrigeanu, V.

1992-02-01

A systematic study on effects of statistical model parameters and semi-classical pre-equilibrium emission models has been carried out for the (n,p) reactions on the 56 Fe and 60 Co target nuclei. The results obtained by using various assumptions within a given pre-equilibrium emission model differ among them more than the ones of different models used under similar conditions. The necessity of using realistic level density formulas is emphasized especially in connection with pre-equilibrium emission models (i.e. with the exciton state density expression), while a basic support could be found only by replacement of the Williams exciton state density formula with a realistic one. (author). 46 refs, 12 figs, 3 tabs

Spherical Process Models for Global Spatial Statistics

KAUST Repository

Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.

2017-01-01

Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture

Model for neural signaling leap statistics

Energy Technology Data Exchange (ETDEWEB)

Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)

2011-03-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.

Analysis and Evaluation of Statistical Models for Integrated Circuits Design

Directory of Open Access Journals (Sweden)

Sáenz-Noval J.J.

2011-10-01

Full Text Available Statistical models for integrated circuits (IC allow us to estimate the percentage of acceptable devices in the batch before fabrication. Actually, Pelgrom is the statistical model most accepted in the industry; however it was derived from a micrometer technology, which does not guarantee reliability in nanometric manufacturing processes. This work considers three of the most relevant statistical models in the industry and evaluates their limitations and advantages in analog design, so that the designer has a better criterion to make a choice. Moreover, it shows how several statistical models can be used for each one of the stages and design purposes.

Terminal-Dependent Statistical Inference for the FBSDEs Models

Directory of Open Access Journals (Sweden)

Yunquan Song

2014-01-01

Full Text Available The original stochastic differential equations (OSDEs and forward-backward stochastic differential equations (FBSDEs are often used to model complex dynamic process that arise in financial, ecological, and many other areas. The main difference between OSDEs and FBSDEs is that the latter is designed to depend on a terminal condition, which is a key factor in some financial and ecological circumstances. It is interesting but challenging to estimate FBSDE parameters from noisy data and the terminal condition. However, to the best of our knowledge, the terminal-dependent statistical inference for such a model has not been explored in the existing literature. We proposed a nonparametric terminal control variables estimation method to address this problem. The reason why we use the terminal control variables is that the newly proposed inference procedures inherit the terminal-dependent characteristic. Through this new proposed method, the estimators of the functional coefficients of the FBSDEs model are obtained. The asymptotic properties of the estimators are also discussed. Simulation studies show that the proposed method gives satisfying estimates for the FBSDE parameters from noisy data and the terminal condition. A simulation is performed to test the feasibility of our method.

The issue of statistical power for overall model fit in evaluating structural equation models

Directory of Open Access Journals (Sweden)

Richard HERMIDA

2015-06-01

Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.

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

Science.gov (United States)

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.

Understanding and forecasting polar stratospheric variability with statistical models

Directory of Open Access Journals (Sweden)

C. Blume

2012-07-01

Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.

Improved model for statistical alignment

Energy Technology Data Exchange (ETDEWEB)

Miklos, I.; Toroczkai, Z. (Zoltan)

2001-01-01

The statistical approach to molecular sequence evolution involves the stochastic modeling of the substitution, insertion and deletion processes. Substitution has been modeled in a reliable way for more than three decades by using finite Markov-processes. Insertion and deletion, however, seem to be more difficult to model, and thc recent approaches cannot acceptably deal with multiple insertions and deletions. A new method based on a generating function approach is introduced to describe the multiple insertion process. The presented algorithm computes the approximate joint probability of two sequences in 0(13) running time where 1 is the geometric mean of the sequence lengths.

Snow cover and End of Summer Snowline statistics from a simple stochastic model

Science.gov (United States)

Petrelli, A.; Crouzy, B.; Perona, P.

2012-04-01

One essential parameter characterizing snow cover statistics is the End Of Summer Snowline (EOSS), which is also a good indicator of actual climatic trends in mountain regions. EOSS is usually modelled by means of spatially distributed physically based models, and typically require heavy parameterization. In this paper we validate the simple stochastic model proposed by Perona et al. (2007), by showing that the snow cover statistics and the position of EOSS can in principle be explained by only four essential (meteorological) parameters. Perona et al. (2007) proposed a model accounting for stochastic snow accumulation in the cold period, and deterministic melting dynamics in the warm period, and studied the statistical distribution of the snowdepth on the long term. By reworking the ensemble average of the steady state evolution equation we single out a relationship between the snowdepth statistics (including the position of EOSS) and the involved parameters. The validation of the established relationship is done using 50 years of field data from 73 Swiss stations located above 2000 m a.s.l. First an estimation of the meteorological parameters is made. Snow height data are used as a precipitation proxy, using temperature data to estimate SWE during the precipitation event. Thresholds are used both to separate accumulation from actual precipitation and wind transport phenomena, and to better assess summer melting rate, considered to be constant over the melting period according to the simplified model. First results show that data for most of the weather stations actually scales with the proposed relationship. This indicates that, on the long term, the effect of spatial and temporal noise masks most of the process detail so that minimalist models suffice to obtain reliable statistics. Future works will test the validity of this approach at different spatial scales, e.g., regional, continental and planetary. Reference: P. Perona, A. Porporato, and L. Ridolfi, "A

Daily precipitation statistics in regional climate models

DEFF Research Database (Denmark)

Frei, Christoph; Christensen, Jens Hesselbjerg; Déqué, Michel

2003-01-01

An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km...

An R2 statistic for fixed effects in the linear mixed model.

Science.gov (United States)

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

Mixed deterministic statistical modelling of regional ozone air pollution

KAUST Repository

Kalenderski, Stoitchko

2011-03-17

We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..

Adaptive Maneuvering Frequency Method of Current Statistical Model

Institute of Scientific and Technical Information of China (English)

Wei Sun; Yongjian Yang

2017-01-01

Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

Statistical modelling of citation exchange between statistics journals.

Science.gov (United States)

Varin, Cristiano; Cattelan, Manuela; Firth, David

2016-01-01

Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.

The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis

DEFF Research Database (Denmark)

Debrabant, Birgit

2017-01-01

MOTIVATION: Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis....... This is a major handicap to the interpretation of results obtained from a gene set analysis. RESULTS: This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set...... analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures...

Shell model in large spaces and statistical spectroscopy

International Nuclear Information System (INIS)

Kota, V.K.B.

1996-01-01

For many nuclear structure problems of current interest it is essential to deal with shell model in large spaces. For this, three different approaches are now in use and two of them are: (i) the conventional shell model diagonalization approach but taking into account new advances in computer technology; (ii) the shell model Monte Carlo method. A brief overview of these two methods is given. Large space shell model studies raise fundamental questions regarding the information content of the shell model spectrum of complex nuclei. This led to the third approach- the statistical spectroscopy methods. The principles of statistical spectroscopy have their basis in nuclear quantum chaos and they are described (which are substantiated by large scale shell model calculations) in some detail. (author)

Advances in statistical models for data analysis

CERN Document Server

Minerva, Tommaso; Vichi, Maurizio

2015-01-01

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

Science.gov (United States)

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

A new statistical scission-point model fed with microscopic ingredients to predict fission fragments distributions

International Nuclear Information System (INIS)

Heinrich, S.

2006-01-01

Nucleus fission process is a very complex phenomenon and, even nowadays, no realistic models describing the overall process are available. The work presented here deals with a theoretical description of fission fragments distributions in mass, charge, energy and deformation. We have reconsidered and updated the B.D. Wilking Scission Point model. Our purpose was to test if this statistic model applied at the scission point and by introducing new results of modern microscopic calculations allows to describe quantitatively the fission fragments distributions. We calculate the surface energy available at the scission point as a function of the fragments deformations. This surface is obtained from a Hartree Fock Bogoliubov microscopic calculation which guarantee a realistic description of the potential dependence on the deformation for each fragment. The statistic balance is described by the level densities of the fragment. We have tried to avoid as much as possible the input of empirical parameters in the model. Our only parameter, the distance between each fragment at the scission point, is discussed by comparison with scission configuration obtained from full dynamical microscopic calculations. Also, the comparison between our results and experimental data is very satisfying and allow us to discuss the success and limitations of our approach. We finally proposed ideas to improve the model, in particular by applying dynamical corrections. (author)

Computationally efficient statistical differential equation modeling using homogenization

Science.gov (United States)

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.

A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

Science.gov (United States)

Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

2015-09-01

Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Models to estimate volume of individual trees by morphometry of crowns obtained with lidar

Directory of Open Access Journals (Sweden)

Evandro Orfanó Figueiredo

2014-12-01

Full Text Available The volumetric estimate from digital scanning of the forests through the use of LIDAR increases the precision of forest management techniques in planning tropical forest logging operations. The use of this remote detection technology allows the incorporation of crown morphometric variables which are still little known and little used due to the difficulty of collecting field data for volume equations. The objective of this study was to build equations capable of estimating the stem volume of dominant and codominant individual trees from the crown's morphometry obtained by airborne LIDAR, considering two forest inventory situations: a with the collection of diameter at breast height (DBH, and crown morphometric variables obtained from LIDAR data and b using only the crown morphometry variables. For the selection of models the factors considered were: the correlation matrix of predictor variables and the combination of variables that generates the best results by statistical criteria Syx, Syx(% and Pressp, and that were homoscedastic and had a normal and independent distribution of errors. The influence analysis was performed for the best equations. The results for the statistical fit of the equations to the two situations allowed the selection of models with and without DBH, with R2aj.( % values of a 92.92 and b 79.44, Syx(% values of a 16.73 and b 27.47, and, Pressp criterion values of a 201.15 m6 and b 537.47 m6, respectively. Through morphometric variables it was possible to develop equations capable of accurately estimating the stem volume of dominant and codominant trees in tropical forests.

Models for probability and statistical inference theory and applications

CERN Document Server

Stapleton, James H

2007-01-01

This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...

A Solution to Modeling Multilevel Confirmatory Factor Analysis with Data Obtained from Complex Survey Sampling to Avoid Conflated Parameter Estimates

Directory of Open Access Journals (Sweden)

Jiun-Yu Wu

2017-09-01

Full Text Available The issue of equality in the between-and within-level structures in Multilevel Confirmatory Factor Analysis (MCFA models has been influential for obtaining unbiased parameter estimates and statistical inferences. A commonly seen condition is the inequality of factor loadings under equal level-varying structures. With mathematical investigation and Monte Carlo simulation, this study compared the robustness of five statistical models including two model-based (a true and a mis-specified models, one design-based, and two maximum models (two models where the full rank of variance-covariance matrix is estimated in between level and within level, respectively in analyzing complex survey measurement data with level-varying factor loadings. The empirical data of 120 3rd graders' (from 40 classrooms perceived Harter competence scale were modeled using MCFA and the parameter estimates were used as true parameters to perform the Monte Carlo simulation study. Results showed maximum models was robust to unequal factor loadings while the design-based and the miss-specified model-based approaches produced conflated results and spurious statistical inferences. We recommend the use of maximum models if researchers have limited information about the pattern of factor loadings and measurement structures. Measurement models are key components of Structural Equation Modeling (SEM; therefore, the findings can be generalized to multilevel SEM and CFA models. Mplus codes are provided for maximum models and other analytical models.

Fluctuations and correlations in statistical models of hadron production

International Nuclear Information System (INIS)

Gorenstein, M. I.

2012-01-01

An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution are introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.

Corrected Statistical Energy Analysis Model for Car Interior Noise

Directory of Open Access Journals (Sweden)

A. Putra

2015-01-01

Full Text Available Statistical energy analysis (SEA is a well-known method to analyze the flow of acoustic and vibration energy in a complex structure. For an acoustic space where significant absorptive materials are present, direct field component from the sound source dominates the total sound field rather than a reverberant field, where the latter becomes the basis in constructing the conventional SEA model. Such environment can be found in a car interior and thus a corrected SEA model is proposed here to counter this situation. The model is developed by eliminating the direct field component from the total sound field and only the power after the first reflection is considered. A test car cabin was divided into two subsystems and by using a loudspeaker as a sound source, the power injection method in SEA was employed to obtain the corrected coupling loss factor and the damping loss factor from the corrected SEA model. These parameters were then used to predict the sound pressure level in the interior cabin using the injected input power from the engine. The results show satisfactory agreement with the directly measured SPL.

Growth curve models and statistical diagnostics

CERN Document Server

Pan, Jian-Xin

2002-01-01

Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.

Advanced data analysis in neuroscience integrating statistical and computational models

CERN Document Server

Durstewitz, Daniel

2017-01-01

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering.  Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerfu...

How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

NARCIS (Netherlands)

Borsboom, D.; Haig, B.D.

2013-01-01

Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

α -induced reactions on 115In: Cross section measurements and statistical model analysis

Science.gov (United States)

Kiss, G. G.; Szücs, T.; Mohr, P.; Török, Zs.; Huszánk, R.; Gyürky, Gy.; Fülöp, Zs.

2018-05-01

Background: α -nucleus optical potentials are basic ingredients of statistical model calculations used in nucleosynthesis simulations. While the nucleon+nucleus optical potential is fairly well known, for the α +nucleus optical potential several different parameter sets exist and large deviations, reaching sometimes even an order of magnitude, are found between the cross section predictions calculated using different parameter sets. Purpose: A measurement of the radiative α -capture and the α -induced reaction cross sections on the nucleus 115In at low energies allows a stringent test of statistical model predictions. Since experimental data are scarce in this mass region, this measurement can be an important input to test the global applicability of α +nucleus optical model potentials and further ingredients of the statistical model. Methods: The reaction cross sections were measured by means of the activation method. The produced activities were determined by off-line detection of the γ rays and characteristic x rays emitted during the electron capture decay of the produced Sb isotopes. The 115In(α ,γ )119Sb and 115In(α ,n )Sb118m reaction cross sections were measured between Ec .m .=8.83 and 15.58 MeV, and the 115In(α ,n )Sb118g reaction was studied between Ec .m .=11.10 and 15.58 MeV. The theoretical analysis was performed within the statistical model. Results: The simultaneous measurement of the (α ,γ ) and (α ,n ) cross sections allowed us to determine a best-fit combination of all parameters for the statistical model. The α +nucleus optical potential is identified as the most important input for the statistical model. The best fit is obtained for the new Atomki-V1 potential, and good reproduction of the experimental data is also achieved for the first version of the Demetriou potentials and the simple McFadden-Satchler potential. The nucleon optical potential, the γ -ray strength function, and the level density parametrization are also

Statistical evaluation of accelerated stability data obtained at a single temperature. I. Effect of experimental errors in evaluation of stability data obtained.

Science.gov (United States)

Yoshioka, S; Aso, Y; Takeda, Y

1990-06-01

Accelerated stability data obtained at a single temperature is statistically evaluated, and the utility of such data for assessment of stability is discussed focussing on the chemical stability of solution-state dosage forms. The probability that the drug content of a product is observed to be within the lower specification limit in the accelerated test is interpreted graphically. This probability depends on experimental errors in the assay and temperature control, as well as the true degradation rate and activation energy. Therefore, the observation that the drug content meets the specification in the accelerated testing can provide only limited information on the shelf-life of the drug, without the knowledge of the activation energy and the accuracy and precision of the assay and temperature control.

Cellular automata and statistical mechanical models

International Nuclear Information System (INIS)

Rujan, P.

1987-01-01

The authors elaborate on the analogy between the transfer matrix of usual lattice models and the master equation describing the time development of cellular automata. Transient and stationary properties of probabilistic automata are linked to surface and bulk properties, respectively, of restricted statistical mechanical systems. It is demonstrated that methods of statistical physics can be successfully used to describe the dynamic and the stationary behavior of such automata. Some exact results are derived, including duality transformations, exact mappings, disorder, and linear solutions. Many examples are worked out in detail to demonstrate how to use statistical physics in order to construct cellular automata with desired properties. This approach is considered to be a first step toward the design of fully parallel, probabilistic systems whose computational abilities rely on the cooperative behavior of their components

Growth Curve Models and Applications : Indian Statistical Institute

CERN Document Server

2017-01-01

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas.   There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...

Improving statistical reasoning theoretical models and practical implications

CERN Document Server

Sedlmeier, Peter

1999-01-01

This book focuses on how statistical reasoning works and on training programs that can exploit people''s natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

The statistics of multi-step direct reactions

International Nuclear Information System (INIS)

Koning, A.J.; Akkermans, J.M.

1991-01-01

We propose a quantum-statistical framework that provides an integrated perspective on the differences and similarities between the many current models for multi-step direct reactions in the continuum. It is argued that to obtain a statistical theory two physically different approaches are conceivable to postulate randomness, respectively called leading-particle statistics and residual-system statistics. We present a new leading-particle statistics theory for multi-step direct reactions. It is shown that the model of Feshbach et al. can be derived as a simplification of this theory and thus can be founded solely upon leading-particle statistics. The models developed by Tamura et al. and Nishioka et al. are based upon residual-system statistics and hence fall into a physically different class of multi-step direct theories, although the resulting cross-section formulae for the important first step are shown to be the same. The widely used semi-classical models such as the generalized exciton model can be interpreted as further phenomenological simplifications of the leading-particle statistics theory. A more comprehensive exposition will appear before long. (author). 32 refs, 4 figs

Solar radiation data - statistical analysis and simulation models

Energy Technology Data Exchange (ETDEWEB)

Mustacchi, C; Cena, V; Rocchi, M; Haghigat, F

1984-01-01

The activities consisted in collecting meteorological data on magnetic tape for ten european locations (with latitudes ranging from 42/sup 0/ to 56/sup 0/ N), analysing the multi-year sequences, developing mathematical models to generate synthetic sequences having the same statistical properties of the original data sets, and producing one or more Short Reference Years (SRY's) for each location. The meteorological parameters examinated were (for all the locations) global + diffuse radiation on horizontal surface, dry bulb temperature, sunshine duration. For some of the locations additional parameters were available, namely, global, beam and diffuse radiation on surfaces other than horizontal, wet bulb temperature, wind velocity, cloud type, cloud cover. The statistical properties investigated were mean, variance, autocorrelation, crosscorrelation with selected parameters, probability density function. For all the meteorological parameters, various mathematical models were built: linear regression, stochastic models of the AR and the DAR type. In each case, the model with the best statistical behaviour was selected for the production of a SRY for the relevant parameter/location.

Statistical Model Checking for Biological Systems

DEFF Research Database (Denmark)

David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel

2014-01-01

Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...

Right-sizing statistical models for longitudinal data.

Science.gov (United States)

Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M

2015-12-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).

Statistical Modeling of Large Wind Plant System's Generation - A Case Study

International Nuclear Information System (INIS)

Sabolic, D.

2014-01-01

This paper presents simplistic, yet very accurate, descriptive statistical models of various static and dynamic parameters of energy output from a large system of wind plants operated by Bonneville Power Administration (BPA), USA. The system's size at the end of 2013 was 4515 MW of installed capacity. The 5-minute readings from the beginning of 2007 to the end of 2013, recorded and published by BPA, were used to derive a number of experimental distributions, which were then used to devise theoretic statistical models with merely one or two parameters. In spite of the simplicity, they reproduced experimental data with great accuracy, which was checked by rigorous tests of goodness-of-fit. Statistical distribution functions were obtained for the following wind generation-related quantities: total generation as percentage of total installed capacity; change in total generation power in 5, 10, 15, 20, 25, 30, 45, and 60 minutes as percentage of total installed capacity; duration of intervals with total generated power, expressed as percentage of total installed capacity, lower than certain pre-specified level. Limitation of total installed wind plant capacity, when it is determined by regulation demand from wind plants, is discussed, too. The models presented here can be utilized in analyses related to power system economics/policy, which is also briefly discussed in the paper. (author).

Statistical models based on conditional probability distributions

International Nuclear Information System (INIS)

Narayanan, R.S.

1991-10-01

We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)

A statistical model for mapping morphological shape

Directory of Open Access Journals (Sweden)

Li Jiahan

2010-07-01

Full Text Available Abstract Background Living things come in all shapes and sizes, from bacteria, plants, and animals to humans. Knowledge about the genetic mechanisms for biological shape has far-reaching implications for a range spectrum of scientific disciplines including anthropology, agriculture, developmental biology, evolution and biomedicine. Results We derived a statistical model for mapping specific genes or quantitative trait loci (QTLs that control morphological shape. The model was formulated within the mixture framework, in which different types of shape are thought to result from genotypic discrepancies at a QTL. The EM algorithm was implemented to estimate QTL genotype-specific shapes based on a shape correspondence analysis. Computer simulation was used to investigate the statistical property of the model. Conclusion By identifying specific QTLs for morphological shape, the model developed will help to ask, disseminate and address many major integrative biological and genetic questions and challenges in the genetic control of biological shape and function.

A statistical model of macromolecules dynamics for Fluorescence Correlation Spectroscopy data analysis

Directory of Open Access Journals (Sweden)

Dmitri Koroliouk

2016-08-01

Full Text Available In this paper, we propose a new mathematical model to describe the mechanisms of biological macromolecules interactions. Our model consists of a discrete stationary random sequence given by a solution of difference stochastic equation, characterized by a drift predictive component and by a diffusion term. The relative statistical estimations are very simple and effective, promising to be a good tool for the mathematical description of collective biological reactions. This paper presents the mathematical model and its verification on a simulated data set, obtained on the basis of the well-known Stokes-Einsteinmodel. In particular, we considered several mix of particles of different diffusion coefficient, respectively: D1=10 mm2/sec and D2=100 mm2/sec. The parameters evaluated by this new mathematical model on simulated data show good estimation accuracy, in comparison with the prior parameters used in the simulations. Furthermore, when analyzing the data for the mix of particles with different diffusion coefficient, the proposed model parameters  (regression and  (square variance of the stochastic component have a good discriminative ability for the molar fraction determination.  In this paper, we propose a new mathematical model to describe the mechanisms of biological macromolecules interactions. Our model consists of a discrete stationary random sequence given by a solution of difference stochastic equation, characterized by a drift predictive component and by a diffusion term. The relative statistical estimations are very simple and effective, promising to be a good tool for mathematical description of collective biological reactions. This paper presents the mathematical model and its verification on simulated data set, obtained on the basis of the well-known Stokes-Einsteinmodel. In particular we considered several mix of particles of different diffusion coefficient, respectively: D1=10 mm2/sec and D2=100 mm2/sec. The parameters

An integrated model of statistical process control and maintenance based on the delayed monitoring

International Nuclear Information System (INIS)

Yin, Hui; Zhang, Guojun; Zhu, Haiping; Deng, Yuhao; He, Fei

2015-01-01

This paper develops an integrated model of statistical process control and maintenance decision. The proposal of delayed monitoring policy postpones the sampling process till a scheduled time and contributes to ten-scenarios of the production process, where equipment failure may occur besides quality shift. The equipment failure and the control chart alert trigger the corrective maintenance and the predictive maintenance, respectively. The occurrence probability, the cycle time and the cycle cost of each scenario are obtained by integral calculation; therefore, a mathematical model is established to minimize the expected cost by using the genetic algorithm. A Monte Carlo simulation experiment is conducted and compared with the integral calculation in order to ensure the analysis of the ten-scenario model. Another ordinary integrated model without delayed monitoring is also established as comparison. The results of a numerical example indicate satisfactory economic performance of the proposed model. Finally, a sensitivity analysis is performed to investigate the effect of model parameters. - Highlights: • We develop an integrated model of statistical process control and maintenance. • We propose delayed monitoring policy and derive an economic model with 10 scenarios. • We consider two deterioration mechanisms, quality shift and equipment failure. • The delayed monitoring policy will help reduce the expected cost

Statistical model selection with “Big Data”

Directory of Open Access Journals (Sweden)

Jurgen A. Doornik

2015-12-01

Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

Statistical shape and appearance models of bones.

Science.gov (United States)

Sarkalkan, Nazli; Weinans, Harrie; Zadpoor, Amir A

2014-03-01

When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone. Copyright © 2013 Elsevier Inc. All rights reserved.

Statistical models and NMR analysis of polymer microstructure

Science.gov (United States)

Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...

Statistical modeling of temperature, humidity and wind fields in the atmospheric boundary layer over the Siberian region

Science.gov (United States)

Lomakina, N. Ya.

2017-11-01

The work presents the results of the applied climatic division of the Siberian region into districts based on the methodology of objective classification of the atmospheric boundary layer climates by the "temperature-moisture-wind" complex realized with using the method of principal components and the special similarity criteria of average profiles and the eigen values of correlation matrices. On the territory of Siberia, it was identified 14 homogeneous regions for winter season and 10 regions were revealed for summer. The local statistical models were constructed for each region. These include vertical profiles of mean values, mean square deviations, and matrices of interlevel correlation of temperature, specific humidity, zonal and meridional wind velocity. The advantage of the obtained local statistical models over the regional models is shown.

Workshop on Model Uncertainty and its Statistical Implications

CERN Document Server

1988-01-01

In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

Strange statistics, braid group representations and multipoint functions in the N-component model

International Nuclear Information System (INIS)

Lee, H.C.; Ge, M.L.; Couture, M.; Wu, Y.S.

1989-01-01

The statistics of fields in low dimensions is studied from the point of view of the braid group B n of n strings. Explicit representations M R for the N-component model, N = 2 to 5, are derived by solving the Yang-Baxter-like braid group relations for the statistical matrix R, which describes the transformation of the bilinear product of two N-component fields under the transposition of coordinates. When R 2 not equal to 1 the statistics is neither Bose-Einstein nor Fermi-Dirac; it is strange. It is shown that for each N, the N + 1 parameter family of solutions obtained is the most general one under a given set of constraints including charge conservation. Extended Nth order (N > 2) Alexander-Conway relations for link polynomials are derived. They depend nonhomogeneously only on one of the N + 1 parameters. The N = 3 and 4 ones agree with those previously derived

Kolmogorov complexity, pseudorandom generators and statistical models testing

Czech Academy of Sciences Publication Activity Database

Šindelář, Jan; Boček, Pavel

2002-01-01

Roč. 38, č. 6 (2002), s. 747-759 ISSN 0023-5954 R&D Projects: GA ČR GA102/99/1564 Institutional research plan: CEZ:AV0Z1075907 Keywords : Kolmogorov complexity * pseudorandom generators * statistical models testing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.341, year: 2002

Prediction of hydrate formation temperature by both statistical models and artificial neural network approaches

International Nuclear Information System (INIS)

Zahedi, Gholamreza; Karami, Zohre; Yaghoobi, Hamed

2009-01-01

In this study, various estimation methods have been reviewed for hydrate formation temperature (HFT) and two procedures have been presented. In the first method, two general correlations have been proposed for HFT. One of the correlations has 11 parameters, and the second one has 18 parameters. In order to obtain constants in proposed equations, 203 experimental data points have been collected from literatures. The Engineering Equation Solver (EES) and Statistical Package for the Social Sciences (SPSS) soft wares have been employed for statistical analysis of the data. Accuracy of the obtained correlations also has been declared by comparison with experimental data and some recent common used correlations. In the second method, HFT is estimated by artificial neural network (ANN) approach. In this case, various architectures have been checked using 70% of experimental data for training of ANN. Among the various architectures multi layer perceptron (MLP) network with trainlm training algorithm was found as the best architecture. Comparing the obtained ANN model results with 30% of unseen data confirms ANN excellent estimation performance. It was found that ANN is more accurate than traditional methods and even our two proposed correlations for HFT estimation.

Visualization of the variability of 3D statistical shape models by animation.

Science.gov (United States)

Lamecker, Hans; Seebass, Martin; Lange, Thomas; Hege, Hans-Christian; Deuflhard, Peter

2004-01-01

Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.

Applied systems ecology: models, data, and statistical methods

Energy Technology Data Exchange (ETDEWEB)

Eberhardt, L L

1976-01-01

In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.

The Dirac equation in classical statistical mechanics

International Nuclear Information System (INIS)

Ord, G.N.

2002-01-01

The Dirac equation, usually obtained by 'quantizing' a classical stochastic model is here obtained directly within classical statistical mechanics. The special underlying space-time geometry of the random walk replaces the missing analytic continuation, making the model 'self-quantizing'. This provides a new context for the Dirac equation, distinct from its usual context in relativistic quantum mechanics

The use of statistical models in heavy-ion reactions studies

International Nuclear Information System (INIS)

Stokstad, R.G.

1984-01-01

This chapter reviews the use of statistical models to describe nuclear level densities and the decay of equilibrated nuclei. The statistical models of nuclear structure and nuclear reactions presented here have wide application in the analysis of heavy-ion reaction data. Applications are illustrated with examples of gamma-ray decay, the emission of light particles and heavier clusters of nucleons, and fission. In addition to the compound nucleus, the treatment of equilibrated fragments formed in binary reactions is discussed. The statistical model is shown to be an important tool for the identification of products from nonequilibrium decay

Multivariate statistical modelling based on generalized linear models

CERN Document Server

Fahrmeir, Ludwig

1994-01-01

This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

Obtaining natural-like flow releases in diverted river reaches from simple riparian benefit economic models.

Science.gov (United States)

Perona, Paolo; Dürrenmatt, David J; Characklis, Gregory W

2013-03-30

We propose a theoretical river modeling framework for generating variable flow patterns in diverted-streams (i.e., no reservoir). Using a simple economic model and the principle of equal marginal utility in an inverse fashion we first quantify the benefit of the water that goes to the environment in relation to that of the anthropic activity. Then, we obtain exact expressions for optimal water allocation rules between the two competing uses, as well as the related statistical distributions. These rules are applied using both synthetic and observed streamflow data, to demonstrate that this approach may be useful in 1) generating more natural flow patterns in the river reach downstream of the diversion, thus reducing the ecodeficit; 2) obtaining a more enlightened economic interpretation of Minimum Flow Release (MFR) strategies, and; 3) comparing the long-term costs and benefits of variable versus MFR policies and showing the greater ecological sustainability of this new approach. Copyright © 2013 Elsevier Ltd. All rights reserved.

Linear mixed models a practical guide using statistical software

CERN Document Server

West, Brady T; Galecki, Andrzej T

2006-01-01

Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo

Active Learning with Statistical Models.

Science.gov (United States)

1995-01-01

Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

Mathematical models for correction of images, obtained at radioisotope scan

International Nuclear Information System (INIS)

Glaz, A.; Lubans, A.

2002-01-01

The images, which obtained at radioisotope scintigraphy, contain distortions. Distortions appear as a result of absorption of radiation by patient's body's tissues. Two mathematical models for reducing of such distortions are proposed. Image obtained by only one gamma camera is used in the first mathematical model. Unfortunately, this model allows processing of the images only in case, when it can be assumed, that the investigated organ has a symmetric form. The images obtained by two gamma cameras are used in the second model. It gives possibility to assume that the investigated organ has non-symmetric form and to acquire more precise results. (authors)

Parametric analysis of the statistical model of the stick-slip process

Science.gov (United States)

Lima, Roberta; Sampaio, Rubens

2017-06-01

In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.

A matrix approach to the statistics of longevity in heterogeneous frailty models

Directory of Open Access Journals (Sweden)

Hal Caswell

2014-09-01

Full Text Available Background: The gamma-Gompertz model is a fixed frailty model in which baseline mortality increasesexponentially with age, frailty has a proportional effect on mortality, and frailty at birth follows a gamma distribution. Mortality selects against the more frail, so the marginal mortality rate decelerates, eventually reaching an asymptote. The gamma-Gompertz is one of a wider class of frailty models, characterized by the choice of baseline mortality, effects of frailty, distributions of frailty, and assumptions about the dynamics of frailty. Objective: To develop a matrix model to compute all the statistical properties of longevity from thegamma-Gompertz and related models. Methods: I use the vec-permutation matrix formulation to develop a model in which individuals are jointly classified by age and frailty. The matrix is used to project the age and frailty dynamicsof a cohort and the fundamental matrix is used to obtain the statistics of longevity. Results: The model permits calculation of the mean, variance, coefficient of variation, skewness and all moments of longevity, the marginal mortality and survivorship functions, the dynamics of the frailty distribution, and other quantities. The matrix formulation extends naturally to other frailty models. I apply the analysis to the gamma-Gompertz model (for humans and laboratory animals, the gamma-Makeham model, and the gamma-Siler model, and to a hypothetical dynamic frailty model characterized by diffusion of frailty with reflecting boundaries.The matrix model permits partitioning the variance in longevity into components due to heterogeneity and to individual stochasticity. In several published human data sets, heterogeneity accounts for less than 10Š of the variance in longevity. In laboratory populations of five invertebrate animal species, heterogeneity accounts for 46Š to 83Š ofthe total variance in longevity.

STATISTIC MODEL OF DYNAMIC DELAY AND DROPOUT ON CELLULAR DATA NETWORKED CONTROL SYSTEM

Directory of Open Access Journals (Sweden)

MUHAMMAD A. MURTI

2017-07-01

Full Text Available Delay and dropout are important parameters influence overall control performance in Networked Control System (NCS. The goal of this research is to find a model of delay and dropout of data communication link in the NCS. Experiments have been done in this research to a water level control of boiler tank as part of the NCS based on internet communication network using High Speed Packet Access (HSPA cellular technology. By this experiments have been obtained closed-loop system response as well as data delay and dropout of data packets. This research contributes on modeling of the NCS which is combination of controlled plant and data communication link. Another contribution is statistical model of delay and dropout on the NCS.

Smooth extrapolation of unknown anatomy via statistical shape models

Science.gov (United States)

Grupp, R. B.; Chiang, H.; Otake, Y.; Murphy, R. J.; Gordon, C. R.; Armand, M.; Taylor, R. H.

2015-03-01

Several methods to perform extrapolation of unknown anatomy were evaluated. The primary application is to enhance surgical procedures that may use partial medical images or medical images of incomplete anatomy. Le Fort-based, face-jaw-teeth transplant is one such procedure. From CT data of 36 skulls and 21 mandibles separate Statistical Shape Models of the anatomical surfaces were created. Using the Statistical Shape Models, incomplete surfaces were projected to obtain complete surface estimates. The surface estimates exhibit non-zero error in regions where the true surface is known; it is desirable to keep the true surface and seamlessly merge the estimated unknown surface. Existing extrapolation techniques produce non-smooth transitions from the true surface to the estimated surface, resulting in additional error and a less aesthetically pleasing result. The three extrapolation techniques evaluated were: copying and pasting of the surface estimate (non-smooth baseline), a feathering between the patient surface and surface estimate, and an estimate generated via a Thin Plate Spline trained from displacements between the surface estimate and corresponding vertices of the known patient surface. Feathering and Thin Plate Spline approaches both yielded smooth transitions. However, feathering corrupted known vertex values. Leave-one-out analyses were conducted, with 5% to 50% of known anatomy removed from the left-out patient and estimated via the proposed approaches. The Thin Plate Spline approach yielded smaller errors than the other two approaches, with an average vertex error improvement of 1.46 mm and 1.38 mm for the skull and mandible respectively, over the baseline approach.

Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams

Directory of Open Access Journals (Sweden)

Chongshi Gu

2013-01-01

Full Text Available Cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Using the parametric statistical model of safety monitoring of cracks in concrete dams, with the help of the semi-parametric statistical theory, and considering the abnormal behaviors of these cracks, the semi-parametric statistical model of safety monitoring of concrete dam cracks is established to overcome the limitation of the parametric model in expressing the objective model. Previous projects show that the semi-parametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semi-parametric statistical model is equivalent to that of the parametric statistical model. The modeling of the semi-parametric statistical model is simple, has a reasonable principle, and has a strong practicality, with a good application prospect in the actual project.

The non-equilibrium statistical mechanics of a simple geophysical fluid dynamics model

Science.gov (United States)

Verkley, Wim; Severijns, Camiel

2014-05-01

Lorenz [1] has devised a dynamical system that has proved to be very useful as a benchmark system in geophysical fluid dynamics. The system in its simplest form consists of a periodic array of variables that can be associated with an atmospheric field on a latitude circle. The system is driven by a constant forcing, is damped by linear friction and has a simple advection term that causes the model to behave chaotically if the forcing is large enough. Our aim is to predict the statistics of Lorenz' model on the basis of a given average value of its total energy - obtained from a numerical integration - and the assumption of statistical stationarity. Our method is the principle of maximum entropy [2] which in this case reads: the information entropy of the system's probability density function shall be maximal under the constraints of normalization, a given value of the average total energy and statistical stationarity. Statistical stationarity is incorporated approximately by using `stationarity constraints', i.e., by requiring that the average first and possibly higher-order time-derivatives of the energy are zero in the maximization of entropy. The analysis [3] reveals that, if the first stationarity constraint is used, the resulting probability density function rather accurately reproduces the statistics of the individual variables. If the second stationarity constraint is used as well, the correlations between the variables are also reproduced quite adequately. The method can be generalized straightforwardly and holds the promise of a viable non-equilibrium statistical mechanics of the forced-dissipative systems of geophysical fluid dynamics. [1] E.N. Lorenz, 1996: Predictability - A problem partly solved, in Proc. Seminar on Predictability (ECMWF, Reading, Berkshire, UK), Vol. 1, pp. 1-18. [2] E.T. Jaynes, 2003: Probability Theory - The Logic of Science (Cambridge University Press, Cambridge). [3] W.T.M. Verkley and C.A. Severijns, 2014: The maximum entropy

Statistical models for competing risk analysis

International Nuclear Information System (INIS)

Sather, H.N.

1976-08-01

Research results on three new models for potential applications in competing risks problems. One section covers the basic statistical relationships underlying the subsequent competing risks model development. Another discusses the problem of comparing cause-specific risk structure by competing risks theory in two homogeneous populations, P1 and P2. Weibull models which allow more generality than the Berkson and Elveback models are studied for the effect of time on the hazard function. The use of concomitant information for modeling single-risk survival is extended to the multiple failure mode domain of competing risks. The model used to illustrate the use of this methodology is a life table model which has constant hazards within pre-designated intervals of the time scale. Two parametric models for bivariate dependent competing risks, which provide interesting alternatives, are proposed and examined

SoS contract verification using statistical model checking

Directory of Open Access Journals (Sweden)

Alessandro Mignogna

2013-11-01

Full Text Available Exhaustive formal verification for systems of systems (SoS is impractical and cannot be applied on a large scale. In this paper we propose to use statistical model checking for efficient verification of SoS. We address three relevant aspects for systems of systems: 1 the model of the SoS, which includes stochastic aspects; 2 the formalization of the SoS requirements in the form of contracts; 3 the tool-chain to support statistical model checking for SoS. We adapt the SMC technique for application to heterogeneous SoS. We extend the UPDM/SysML specification language to express the SoS requirements that the implemented strategies over the SoS must satisfy. The requirements are specified with a new contract language specifically designed for SoS, targeting a high-level English- pattern language, but relying on an accurate semantics given by the standard temporal logics. The contracts are verified against the UPDM/SysML specification using the Statistical Model Checker (SMC PLASMA combined with the simulation engine DESYRE, which integrates heterogeneous behavioral models through the functional mock-up interface (FMI standard. The tool-chain allows computing an estimation of the satisfiability of the contracts by the SoS. The results help the system architect to trade-off different solutions to guide the evolution of the SoS.

Complex Data Modeling and Computationally Intensive Statistical Methods

CERN Document Server

Mantovan, Pietro

2010-01-01

The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici

A statistical model for porous structure of rocks

Institute of Scientific and Technical Information of China (English)

JU Yang; YANG YongMing; SONG ZhenDuo; XU WenJing

2008-01-01

The geometric features and the distribution properties of pores in rocks were In-vestigated by means of CT scanning tests of sandstones. The centroidal coordl-nares of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob-ability density functions upon which the random distribution of pore position, dis-tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex-amine the stress distribution, the pattern of element failure and the inoaculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.

A statistical model for porous structure of rocks

Institute of Scientific and Technical Information of China (English)

2008-01-01

The geometric features and the distribution properties of pores in rocks were in- vestigated by means of CT scanning tests of sandstones. The centroidal coordi- nates of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob- ability density functions upon which the random distribution of pore position, dis- tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex- amine the stress distribution, the pattern of element failure and the inosculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

International Nuclear Information System (INIS)

Debón, A.; Carlos Garcia-Díaz, J.

2012-01-01

Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.

Representation of the contextual statistical model by hyperbolic amplitudes

International Nuclear Information System (INIS)

Khrennikov, Andrei

2005-01-01

We continue the development of a so-called contextual statistical model (here context has the meaning of a complex of physical conditions). It is shown that, besides contexts producing the conventional trigonometric cos-interference, there exist contexts producing the hyperbolic cos-interference. Starting with the corresponding interference formula of total probability we represent such contexts by hyperbolic probabilistic amplitudes or in the abstract formalism by normalized vectors of a hyperbolic analogue of the Hilbert space. There is obtained a hyperbolic Born's rule. Incompatible observables are represented by noncommutative operators. This paper can be considered as the first step towards hyperbolic quantum probability. We also discuss possibilities of experimental verification of hyperbolic quantum mechanics: in physics of elementary particles, string theory as well as in experiments with nonphysical systems, e.g., in psychology, cognitive sciences, and economy

(ajst) statistical mechanics model for orientational

African Journals Online (AJOL)

Science and Engineering Series Vol. 6, No. 2, pp. 94 - 101. STATISTICAL MECHANICS MODEL FOR ORIENTATIONAL. MOTION OF TWO-DIMENSIONAL RIGID ROTATOR. Malo, J.O. ... there is no translational motion and that they are well separated so .... constant and I is the moment of inertia of a linear rotator. Thus, the ...

Evaluation of digital dental models obtained from dental cone-beam computed tomography scan of alginate impressions.

Science.gov (United States)

Jiang, Tingting; Lee, Sang-Mi; Hou, Yanan; Chang, Xin; Hwang, Hyeon-Shik

2016-05-01

To investigate the dimensional accuracy of digital dental models obtained from the dental cone-beam computed tomography (CBCT) scan of alginate impressions according to the time elapse when the impressions are stored under ambient conditions. Alginate impressions were obtained from 20 adults using 3 different alginate materials, 2 traditional alginate materials (Alginoplast and Cavex Impressional) and 1 extended-pour alginate material (Cavex ColorChange). The impressions were stored under ambient conditions, and scanned by CBCT immediately after the impressions were taken, and then at 1 hour intervals for 6 hours. After reconstructing three-dimensional digital dental models, the models were measured and the data were analyzed to determine dimensional changes according to the elapsed time. The changes within the measurement error were regarded as clinically acceptable in this study. All measurements showed a decreasing tendency with an increase in the elapsed time after the impressions. Although the extended-pour alginate exhibited a less decreasing tendency than the other 2 materials, there were no statistically significant differences between the materials. Changes above the measurement error occurred between the time points of 3 and 4 hours after the impressions. The results of this study indicate that digital dental models can be obtained simply from a CBCT scan of alginate impressions without sending them to a remote laboratory. However, when the impressions are not stored under special conditions, they should be scanned immediately, or at least within 2 to 3 hours after the impressions are taken.

Performance modeling, stochastic networks, and statistical multiplexing

CERN Document Server

Mazumdar, Ravi R

2013-01-01

This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Directory of Open Access Journals (Sweden)

Shuai Luo

2016-02-01

Full Text Available Bioelectrochemical systems (BES are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

Validation of statistical models for creep rupture by parametric analysis

Energy Technology Data Exchange (ETDEWEB)

Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)

2012-01-15

Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).

Flashover of a vacuum-insulator interface: A statistical model

Directory of Open Access Journals (Sweden)

W. A. Stygar

2004-07-01

Full Text Available We have developed a statistical model for the flashover of a 45° vacuum-insulator interface (such as would be found in an accelerator subject to a pulsed electric field. The model assumes that the initiation of a flashover plasma is a stochastic process, that the characteristic statistical component of the flashover delay time is much greater than the plasma formative time, and that the average rate at which flashovers occur is a power-law function of the instantaneous value of the electric field. Under these conditions, we find that the flashover probability is given by 1-exp(-E_{p}^{β}t_{eff}C/k^{β}, where E_{p} is the peak value in time of the spatially averaged electric field E(t, t_{eff}≡∫[E(t/E_{p}]^{β}dt is the effective pulse width, C is the insulator circumference, k∝exp(λ/d, and β and λ are constants. We define E(t as V(t/d, where V(t is the voltage across the insulator and d is the insulator thickness. Since the model assumes that flashovers occur at random azimuthal locations along the insulator, it does not apply to systems that have a significant defect, i.e., a location contaminated with debris or compromised by an imperfection at which flashovers repeatedly take place, and which prevents a random spatial distribution. The model is consistent with flashover measurements to within 7% for pulse widths between 0.5 ns and 10   μs, and to within a factor of 2 between 0.5 ns and 90 s (a span of over 11 orders of magnitude. For these measurements, E_{p} ranges from 64 to 651  kV/cm, d from 0.50 to 4.32 cm, and C from 4.96 to 95.74 cm. The model is significantly more accurate, and is valid over a wider range of parameters, than the J. C. Martin flashover relation that has been in use since 1971 [J. C. Martin on Pulsed Power, edited by T. H. Martin, A. H. Guenther, and M. Kristiansen (Plenum, New York, 1996]. We have generalized the statistical model to estimate the total-flashover probability of an

Statistical modelling of fine red wine production

Directory of Open Access Journals (Sweden)

María Rosa Castro

2010-01-01

Full Text Available Producing wine is a very important economic activity in the province of San Juan in Argentina; it is therefore most important to predict production regarding the quantity of raw material needed. This work was aimed at obtaining a model relating kilograms of crushed grape to the litres of wine so produced. Such model will be used for predicting precise future values and confidence intervals for determined quantities of crushed grapes. Data from a vineyard in the province of San Juan was thus used in this work. The sampling coefficient of correlation was calculated and a dispersion diagram was then constructed; this indicated a li- neal relationship between the litres of wine obtained and the kilograms of crushed grape. Two lineal models were then adopted and variance analysis was carried out because the data came from normal populations having the same variance. The most appropriate model was obtained from this analysis; it was validated with experimental values, a good approach being obtained.

Automated robust generation of compact 3D statistical shape models

Science.gov (United States)

Vrtovec, Tomaz; Likar, Bostjan; Tomazevic, Dejan; Pernus, Franjo

2004-05-01

Ascertaining the detailed shape and spatial arrangement of anatomical structures is important not only within diagnostic settings but also in the areas of planning, simulation, intraoperative navigation, and tracking of pathology. Robust, accurate and efficient automated segmentation of anatomical structures is difficult because of their complexity and inter-patient variability. Furthermore, the position of the patient during image acquisition, the imaging device and protocol, image resolution, and other factors induce additional variations in shape and appearance. Statistical shape models (SSMs) have proven quite successful in capturing structural variability. A possible approach to obtain a 3D SSM is to extract reference voxels by precisely segmenting the structure in one, reference image. The corresponding voxels in other images are determined by registering the reference image to each other image. The SSM obtained in this way describes statistically plausible shape variations over the given population as well as variations due to imperfect registration. In this paper, we present a completely automated method that significantly reduces shape variations induced by imperfect registration, thus allowing a more accurate description of variations. At each iteration, the derived SSM is used for coarse registration, which is further improved by describing finer variations of the structure. The method was tested on 64 lumbar spinal column CT scans, from which 23, 38, 45, 46 and 42 volumes of interest containing vertebra L1, L2, L3, L4 and L5, respectively, were extracted. Separate SSMs were generated for each vertebra. The results show that the method is capable of reducing the variations induced by registration errors.

Nuclear Statistical Equilibrium for compact stars: modelling the nuclear energy functional

International Nuclear Information System (INIS)

Aymard, Francois

2015-01-01

The core collapse supernova is one of the most powerful known phenomena in the universe. It results from the explosion of very massive stars after they have burnt all their fuel. The hot compact remnant, the so-called proto-neutron star, cools down to become an inert catalyzed neutron star. The dynamics and structure of compact stars, that is core collapse supernovae, proto-neutron stars and neutron stars, are still not fully understood and are currently under active research, in association with astrophysical observations and nuclear experiments. One of the key components for modelling compact stars concerns the Equation of State. The task of computing a complete realistic consistent Equation of State for all such stars is challenging because a wide range of densities, proton fractions and temperatures is spanned. This thesis deals with the microscopic modelling of the structure and internal composition of baryonic matter with nucleonic degrees of freedom in compact stars, in order to obtain a realistic unified Equation of State. In particular, we are interested in a formalism which can be applied both at sub-saturation and super-saturation densities, and which gives in the zero temperature limit results compatible with the microscopic Hartree-Fock-Bogoliubov theory with modern realistic effective interactions constrained on experimental nuclear data. For this purpose, we present, for sub-saturated matter, a Nuclear Statistical Equilibrium model which corresponds to a statistical superposition of finite configurations, the so-called Wigner-Seitz cells. Each cell contains a nucleus, or cluster, embedded in a homogeneous electron gas as well as a homogeneous neutron and proton gas. Within each cell, we investigate the different components of the nuclear energy of clusters in interaction with gases. The use of the nuclear mean-field theory for the description of both the clusters and the nucleon gas allows a theoretical consistency with the treatment at saturation

Statistical models describing the energy signature of buildings

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Thavlov, Anders

2010-01-01

Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i...... or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA......-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i...

Statistical physics of an anyon gas

International Nuclear Information System (INIS)

Dasnieres de Veigy, A.

1994-01-01

In quantum two-dimensional physics, anyons are particles which have an intermediate statistics between Bose-Einstein and Fermi-Dirac statistics. The wave amplitude can change by an arbitrary phase under particle exchanges. Contrary to bosons or fermions, the permutation group cannot uniquely characterize this phase and one must introduce the braid group. One shows that the statistical ''interaction'' is equivalent to an Aharonov-Bohm interaction which derives from a Chern-Simons lagrangian. The main subject of this thesis is the thermodynamics of an anyon gas. Since the complete spectrum of N anyons seems out of reach, we have done a perturbative computation of the equation of state at second order near Bose or Fermi statistics. One avoids ultraviolet divergences by noticing that the short-range singularities of the statistical interaction enforce the wave functions to vanish when two particles approach each other (statistical exclusion). The gas is confined in a harmonic well in order to obtain the thermodynamics limit when the harmonic attraction goes to zero. Infrared divergences thus cancel in this limit and a finite virial expansion is obtained. The complexity of the anyon model appears in this result. We have also computed the equation of state of an anyon gas in a magnetic field strong enough to project the system in its degenerate groundstate. This result concerns anyons with any statistics. One then finds an exclusion principle generalizing the Pauli principle to anyons. On the other hand, we have defined a model of two-dimensional particles topologically interacting at a distance. The anyon model is recovered as a particular case where all particles are identical. (orig.)

STATISTICAL MODELS OF REPRESENTING INTELLECTUAL CAPITAL

Directory of Open Access Journals (Sweden)

Andreea Feraru

2016-06-01

Full Text Available This article entitled Statistical Models of Representing Intellectual Capital approaches and analyses the concept of intellectual capital, as well as the main models which can support enterprisers/managers in evaluating and quantifying the advantages of intellectual capital. Most authors examine intellectual capital from a static perspective and focus on the development of its various evaluation models. In this chapter we surveyed the classical static models: Sveiby, Edvisson, Balanced Scorecard, as well as the canonical model of intellectual capital. Among the group of static models for evaluating organisational intellectual capital the canonical model stands out. This model enables the structuring of organisational intellectual capital in: human capital, structural capital and relational capital. Although the model is widely spread, it is a static one and can thus create a series of errors in the process of evaluation, because all the three entities mentioned above are not independent from the viewpoint of their contents, as any logic of structuring complex entities requires.

Statistical mechanics of directed models of polymers in the square lattice

CERN Document Server

Rensburg, J V

2003-01-01

Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce...

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

Science.gov (United States)

Rodgers, Joseph Lee

2010-01-01

A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models. Copyrigiht 2009 APA, all rights reserved.

Establishing statistical models of manufacturing parameters

International Nuclear Information System (INIS)

Senevat, J.; Pape, J.L.; Deshayes, J.F.

1991-01-01

This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature

Simulating Durum Wheat (Triticum turgidum L. Response to Root Zone Salinity based on Statistics and Macroscopic Models

Directory of Open Access Journals (Sweden)

Vahid Reza Jalali

2017-10-01

water was taken from Maharlu Lake, Fars province, Iran. This natural and highly saline water with electrical conductivity of 512 dS/m diluted with fresh water to obtain the designated saline waters required for the experimental treatments. The designed experimental treatments were consisted of a non-saline water and five salinity levels of 2, 4, 6, 8 and 10 dS/m with three replicates. Three statistics of modified coefficient efficiency (E', modified index of agreement (d' and coefficient of residual mass (CRM were used to compare the used models and to assess their performances. Results and discussion Comparing the relative performance of models based on statistical indices of Modified Coefficient Efficiency (E' and Modified Index of agreement (d' indicated that the nonlinear model of Homaee et al. is most accurate between process-physical models and Modified Gompertz Function is most accurate between statistical-experimental models. Comparison assessment of all models based on statistical index indicated that Homaee et al. model was the most accurate model for simulation of durum wheat yield. This is while the parameters of Homaee et al. equation is well-defined concept and is easily measurable, but in statistical-experimental models, parameters of each model have no biophysical concept and the absolute values of each parameter do not express any information about development status of the plant. So, the nonlinear model of Homaee et al. was chosen as the optimal model in this research. Conclusion Most of the plants such as wheat, are sensitive to salinity and by increasing the age, their sensitivity to salinity are reduced. Based on the obtained results of this study, by knowing and quantitative assessment of the dominant cultivars sensitivity of each region, as well as using appropriate simulation models, one can use brackish or saline waters to partly compensate fresh water shortage for scientific and extension Agricultural programs.

Strangeness content and structure function of the nucleon in a statistical quark model

CERN Document Server

Trevisan, L A; Tomio, L

1999-01-01

The strangeness content of the nucleon is determined from a statistical model using confined quark levels, and is shown to have a good agreement with the corresponding values extracted from experimental data. The quark levels are generated in a Dirac equation that uses a linear confining potential (scalar plus vector). With the requirement that the result for the Gottfried sum rule violation, given by the new muon collaboration (NMC), is well reproduced, we also obtain the difference between the structure functions of the proton and neutron, and the corresponding sea quark contributions. (27 refs).

Statistical geological discrete fracture network model. Forsmark modelling stage 2.2

Energy Technology Data Exchange (ETDEWEB)

Fox, Aaron; La Pointe, Paul [Golder Associates Inc (United States); Simeonov, Assen [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden); Hermanson, Jan; Oehman, Johan [Golder Associates AB, Stockholm (Sweden)

2007-11-15

The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions

Statistical geological discrete fracture network model. Forsmark modelling stage 2.2

International Nuclear Information System (INIS)

Fox, Aaron; La Pointe, Paul; Simeonov, Assen; Hermanson, Jan; Oehman, Johan

2007-11-01

The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions

Modelling of classical ghost images obtained using scattered light

International Nuclear Information System (INIS)

Crosby, S; Castelletto, S; Aruldoss, C; Scholten, R E; Roberts, A

2007-01-01

The images obtained in ghost imaging with pseudo-thermal light sources are highly dependent on the spatial coherence properties of the incident light. Pseudo-thermal light is often created by reducing the coherence length of a coherent source by passing it through a turbid mixture of scattering spheres. We describe a model for simulating ghost images obtained with such partially coherent light, using a wave-transport model to calculate the influence of the scattering on initially coherent light. The model is able to predict important properties of the pseudo-thermal source, such as the coherence length and the amplitude of the residual unscattered component of the light which influence the resolution and visibility of the final ghost image. We show that the residual ballistic component introduces an additional background in the reconstructed image, and the spatial resolution obtainable depends on the size of the scattering spheres

Modelling of classical ghost images obtained using scattered light

Energy Technology Data Exchange (ETDEWEB)

Crosby, S; Castelletto, S; Aruldoss, C; Scholten, R E; Roberts, A [School of Physics, University of Melbourne, Victoria, 3010 (Australia)

2007-08-15

The images obtained in ghost imaging with pseudo-thermal light sources are highly dependent on the spatial coherence properties of the incident light. Pseudo-thermal light is often created by reducing the coherence length of a coherent source by passing it through a turbid mixture of scattering spheres. We describe a model for simulating ghost images obtained with such partially coherent light, using a wave-transport model to calculate the influence of the scattering on initially coherent light. The model is able to predict important properties of the pseudo-thermal source, such as the coherence length and the amplitude of the residual unscattered component of the light which influence the resolution and visibility of the final ghost image. We show that the residual ballistic component introduces an additional background in the reconstructed image, and the spatial resolution obtainable depends on the size of the scattering spheres.

Planar-channeling spatial density under statistical equilibrium

International Nuclear Information System (INIS)

Ellison, J.A.; Picraux, S.T.

1978-01-01

The phase-space density for planar channeled particles has been derived for the continuum model under statistical equilibrium. This is used to obtain the particle spatial probability density as a function of incident angle. The spatial density is shown to depend on only two parameters, a normalized incident angle and a normalized planar spacing. This normalization is used to obtain, by numerical calculation, a set of universal curves for the spatial density and also for the channeled-particle wavelength as a function of amplitude. Using these universal curves, the statistical-equilibrium spatial density and the channeled-particle wavelength can be easily obtained for any case for which the continuum model can be applied. Also, a new one-parameter analytic approximation to the spatial density is developed. This parabolic approximation is shown to give excellent agreement with the exact calculations

Statistical Modelling of the Soil Dielectric Constant

Science.gov (United States)

Usowicz, Boguslaw; Marczewski, Wojciech; Bogdan Usowicz, Jerzy; Lipiec, Jerzy

2010-05-01

The dielectric constant of soil is the physical property being very sensitive on water content. It funds several electrical measurement techniques for determining the water content by means of direct (TDR, FDR, and others related to effects of electrical conductance and/or capacitance) and indirect RS (Remote Sensing) methods. The work is devoted to a particular statistical manner of modelling the dielectric constant as the property accounting a wide range of specific soil composition, porosity, and mass density, within the unsaturated water content. Usually, similar models are determined for few particular soil types, and changing the soil type one needs switching the model on another type or to adjust it by parametrization of soil compounds. Therefore, it is difficult comparing and referring results between models. The presented model was developed for a generic representation of soil being a hypothetical mixture of spheres, each representing a soil fraction, in its proper phase state. The model generates a serial-parallel mesh of conductive and capacitive paths, which is analysed for a total conductive or capacitive property. The model was firstly developed to determine the thermal conductivity property, and now it is extended on the dielectric constant by analysing the capacitive mesh. The analysis is provided by statistical means obeying physical laws related to the serial-parallel branching of the representative electrical mesh. Physical relevance of the analysis is established electrically, but the definition of the electrical mesh is controlled statistically by parametrization of compound fractions, by determining the number of representative spheres per unitary volume per fraction, and by determining the number of fractions. That way the model is capable covering properties of nearly all possible soil types, all phase states within recognition of the Lorenz and Knudsen conditions. In effect the model allows on generating a hypothetical representative of

Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

Science.gov (United States)

Qi, D.; Majda, A.

2017-12-01

A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with

Bayesian models based on test statistics for multiple hypothesis testing problems.

Science.gov (United States)

Ji, Yuan; Lu, Yiling; Mills, Gordon B

2008-04-01

We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.

On-the-fly confluence detection for statistical model checking (extended version)

NARCIS (Netherlands)

Hartmanns, Arnd; Timmer, Mark

Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the

THE STATISTICAL MODEL OF PRESSURE RIDGE MORPHOMETRY ON THE NORTHEAST SHELF OF SAKHALIN ISLAND

Directory of Open Access Journals (Sweden)

E. U. Mironov

2012-01-01

Full Text Available The work presents characteristics on geometry and inner structure of ice ridges investigated at offshore the northeast coast of SakhalinIsland. A formula was obtained which allows one to calculate the ice ridge keel depth by the height of its sail. Plots of the probability distribution density for ice ridge characteristics are given. A model of morphometry of a mean statistical ice ridge was constructed, and its mass is determined. Factors influencing the hydrostatic ice ridge equilibrium are considered.

A statistical model for horizontal mass flux of erodible soil

International Nuclear Information System (INIS)

Babiker, A.G.A.G.; Eltayeb, I.A.; Hassan, M.H.A.

1986-11-01

It is shown that the mass flux of erodible soil transported horizontally by a statistically distributed wind flow has a statistical distribution. Explicit expression for the probability density function, p.d.f., of the flux is derived for the case in which the wind speed has a Weibull distribution. The statistical distribution for a mass flux characterized by a generalized Bagnold formula is found to be Weibull for the case of zero threshold speed. Analytic and numerical values for the average horizontal mass flux of soil are obtained for various values of wind parameters, by evaluating the first moment of the flux density function. (author)

Statistical evaluation of fatty acid profile and cholesterol content in fish (common carp) lipids obtained by different sample preparation procedures.

Science.gov (United States)

Spiric, Aurelija; Trbovic, Dejana; Vranic, Danijela; Djinovic, Jasna; Petronijevic, Radivoj; Matekalo-Sverak, Vesna

2010-07-05

Studies performed on lipid extraction from animal and fish tissues do not provide information on its influence on fatty acid composition of the extracted lipids as well as on cholesterol content. Data presented in this paper indicate the impact of extraction procedures on fatty acid profile of fish lipids extracted by the modified Soxhlet and ASE (accelerated solvent extraction) procedure. Cholesterol was also determined by direct saponification method, too. Student's paired t-test used for comparison of the total fat content in carp fish population obtained by two extraction methods shows that differences between values of the total fat content determined by ASE and modified Soxhlet method are not statistically significant. Values obtained by three different methods (direct saponification, ASE and modified Soxhlet method), used for determination of cholesterol content in carp, were compared by one-way analysis of variance (ANOVA). The obtained results show that modified Soxhlet method gives results which differ significantly from the results obtained by direct saponification and ASE method. However the results obtained by direct saponification and ASE method do not differ significantly from each other. The highest quantities for cholesterol (37.65 to 65.44 mg/100 g) in the analyzed fish muscle were obtained by applying direct saponification method, as less destructive one, followed by ASE (34.16 to 52.60 mg/100 g) and modified Soxhlet extraction method (10.73 to 30.83 mg/100 g). Modified Soxhlet method for extraction of fish lipids gives higher values for n-6 fatty acids than ASE method (t(paired)=3.22 t(c)=2.36), while there is no statistically significant difference in the n-3 content levels between the methods (t(paired)=1.31). The UNSFA/SFA ratio obtained by using modified Soxhlet method is also higher than the ratio obtained using ASE method (t(paired)=4.88 t(c)=2.36). Results of Principal Component Analysis (PCA) showed that the highest positive impact to

Statistical theory of dynamo

Science.gov (United States)

Kim, E.; Newton, A. P.

2012-04-01

One major problem in dynamo theory is the multi-scale nature of the MHD turbulence, which requires statistical theory in terms of probability distribution functions. In this contribution, we present the statistical theory of magnetic fields in a simplified mean field α-Ω dynamo model by varying the statistical property of alpha, including marginal stability and intermittency, and then utilize observational data of solar activity to fine-tune the mean field dynamo model. Specifically, we first present a comprehensive investigation into the effect of the stochastic parameters in a simplified α-Ω dynamo model. Through considering the manifold of marginal stability (the region of parameter space where the mean growth rate is zero), we show that stochastic fluctuations are conductive to dynamo. Furthermore, by considering the cases of fluctuating alpha that are periodic and Gaussian coloured random noise with identical characteristic time-scales and fluctuating amplitudes, we show that the transition to dynamo is significantly facilitated for stochastic alpha with random noise. Furthermore, we show that probability density functions (PDFs) of the growth-rate, magnetic field and magnetic energy can provide a wealth of useful information regarding the dynamo behaviour/intermittency. Finally, the precise statistical property of the dynamo such as temporal correlation and fluctuating amplitude is found to be dependent on the distribution the fluctuations of stochastic parameters. We then use observations of solar activity to constrain parameters relating to the effect in stochastic α-Ω nonlinear dynamo models. This is achieved through performing a comprehensive statistical comparison by computing PDFs of solar activity from observations and from our simulation of mean field dynamo model. The observational data that are used are the time history of solar activity inferred for C14 data in the past 11000 years on a long time scale and direct observations of the sun spot

Order, disorder and generalized statistics

International Nuclear Information System (INIS)

Marino, E.C.; Swieca, J.A.

1980-06-01

We generalize the prescription of Kadanoff and Ceva for the computation of disorder variables correlation functions in the Ising model for continuous field theories with U(1) symmetry. By considering the product of order and disorder variables, we obtain a path integral representation for fields with generalized statistics. We discuss in detail the cases of massless Thirring and Schwinger models. (Author) [pt

Order, disorder and generalized statistics

International Nuclear Information System (INIS)

Marino, E.C.; Swieca, J.A.; Pontificia Universidade Catolica do Rio de Janeiro

1980-01-01

We generalize the prescription of Kadanoff and Ceva for the computation of disorder variable correlation functions in the Ising model for continuous field theories with U(1) symmetry. By considering the product of order and disorder variables, we obtain a path integral representation for fields with generalized statistics. We discuss in detail the cases of massless Thirring and Schwinger models. (orig.)

Topology for Statistical Modeling of Petascale Data

Energy Technology Data Exchange (ETDEWEB)

Bennett, Janine Camille [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pebay, Philippe Pierre [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Rojas, Maurice [Texas A & M Univ., College Station, TX (United States)

2014-07-01

This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled "Topology for Statistical Modeling of Petascale Data", funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program.

Evaluation of digital dental models obtained from dental cone-beam computed tomography scan of alginate impressions

Science.gov (United States)

Jiang, Tingting; Lee, Sang-Mi; Hou, Yanan; Chang, Xin

2016-01-01

Objective To investigate the dimensional accuracy of digital dental models obtained from the dental cone-beam computed tomography (CBCT) scan of alginate impressions according to the time elapse when the impressions are stored under ambient conditions. Methods Alginate impressions were obtained from 20 adults using 3 different alginate materials, 2 traditional alginate materials (Alginoplast and Cavex Impressional) and 1 extended-pour alginate material (Cavex ColorChange). The impressions were stored under ambient conditions, and scanned by CBCT immediately after the impressions were taken, and then at 1 hour intervals for 6 hours. After reconstructing three-dimensional digital dental models, the models were measured and the data were analyzed to determine dimensional changes according to the elapsed time. The changes within the measurement error were regarded as clinically acceptable in this study. Results All measurements showed a decreasing tendency with an increase in the elapsed time after the impressions. Although the extended-pour alginate exhibited a less decreasing tendency than the other 2 materials, there were no statistically significant differences between the materials. Changes above the measurement error occurred between the time points of 3 and 4 hours after the impressions. Conclusions The results of this study indicate that digital dental models can be obtained simply from a CBCT scan of alginate impressions without sending them to a remote laboratory. However, when the impressions are not stored under special conditions, they should be scanned immediately, or at least within 2 to 3 hours after the impressions are taken. PMID:27226958

Analyzing sickness absence with statistical models for survival data

DEFF Research Database (Denmark)

Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars

2007-01-01

OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation...

Statistical properties of the coarse-grained velocity gradient tensor in turbulence: Monte-Carlo simulations of the tetrad model

International Nuclear Information System (INIS)

Pumir, Alain; Naso, Aurore

2010-01-01

A proper description of the velocity gradient tensor is crucial for understanding the dynamics of turbulent flows, in particular the energy transfer from large to small scales. Insight into the statistical properties of the velocity gradient tensor and into its coarse-grained generalization can be obtained with the help of a stochastic 'tetrad model' that describes the coarse-grained velocity gradient tensor based on the evolution of four points. Although the solution of the stochastic model can be formally expressed in terms of path integrals, its numerical determination in terms of the Monte-Carlo method is very challenging, as very few configurations contribute effectively to the statistical weight. Here, we discuss a strategy that allows us to solve the tetrad model numerically. The algorithm is based on the importance sampling method, which consists here of identifying and sampling preferentially the configurations that are likely to correspond to a large statistical weight, and selectively rejecting configurations with a small statistical weight. The algorithm leads to an efficient numerical determination of the solutions of the model and allows us to determine their qualitative behavior as a function of scale. We find that the moments of order n≤4 of the solutions of the model scale with the coarse-graining scale and that the scaling exponents are very close to the predictions of the Kolmogorov theory. The model qualitatively reproduces quite well the statistics concerning the local structure of the flow. However, we find that the model generally tends to predict an excess of strain compared to vorticity. Thus, our results show that while some physical aspects are not fully captured by the model, our approach leads to a very good description of several important qualitative properties of real turbulent flows.

Physics-based statistical model and simulation method of RF propagation in urban environments

Science.gov (United States)

Pao, Hsueh-Yuan; Dvorak, Steven L.

2010-09-14

A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.

Encoding Dissimilarity Data for Statistical Model Building.

Science.gov (United States)

Wahba, Grace

2010-12-01

We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.

Simple classical model for Fano statistics in radiation detectors

Energy Technology Data Exchange (ETDEWEB)

Jordan, David V. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)], E-mail: David.Jordan@pnl.gov; Renholds, Andrea S.; Jaffe, John E.; Anderson, Kevin K.; Rene Corrales, L.; Peurrung, Anthony J. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)

2008-02-01

A simple classical model that captures the essential statistics of energy partitioning processes involved in the creation of information carriers (ICs) in radiation detectors is presented. The model pictures IC formation from a fixed amount of deposited energy in terms of the statistically analogous process of successively sampling water from a large, finite-volume container ('bathtub') with a small dipping implement ('shot or whiskey glass'). The model exhibits sub-Poisson variance in the distribution of the number of ICs generated (the 'Fano effect'). Elementary statistical analysis of the model clarifies the role of energy conservation in producing the Fano effect and yields Fano's prescription for computing the relative variance of the IC number distribution in terms of the mean and variance of the underlying, single-IC energy distribution. The partitioning model is applied to the development of the impact ionization cascade in semiconductor radiation detectors. It is shown that, in tandem with simple assumptions regarding the distribution of energies required to create an (electron, hole) pair, the model yields an energy-independent Fano factor of 0.083, in accord with the lower end of the range of literature values reported for silicon and high-purity germanium. The utility of this simple picture as a diagnostic tool for guiding or constraining more detailed, 'microscopic' physical models of detector material response to ionizing radiation is discussed.

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Statistical learning modeling method for space debris photometric measurement

Science.gov (United States)

Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

2016-03-01

Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science

Science.gov (United States)

Caron, L.; Ivins, E. R.; Larour, E.; Adhikari, S.; Nilsson, J.; Blewitt, G.

2018-03-01

We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (≤2 mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7 year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14 years of GRACE data in polar regions.

Experimental, statistical, and biological models of radon carcinogenesis

International Nuclear Information System (INIS)

Cross, F.T.

1991-09-01

Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared to domestic environments and from uncertainties about the interaction between cigarette-smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research program that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models), and the relationship of radon to smoking and other copollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. 20 refs., 1 fig

A statistical model for instable thermodynamical systems

International Nuclear Information System (INIS)

Sommer, Jens-Uwe

2003-01-01

A generic model is presented for statistical systems which display thermodynamic features in contrast to our everyday experience, such as infinite and negative heat capacities. Such system are instable in terms of classical equilibrium thermodynamics. Using our statistical model, we are able to investigate states of instable systems which are undefined in the framework of equilibrium thermodynamics. We show that a region of negative heat capacity in the adiabatic environment, leads to a first order like phase transition when the system is coupled to a heat reservoir. This phase transition takes place without a phase coexistence. Nevertheless, all intermediate states are stable due to fluctuations. When two instable system are brought in thermal contact, the temperature of the composed system is lower than the minimum temperature of the individual systems. Generally, the equilibrium states of instable system cannot be simply decomposed into equilibrium states of the individual systems. The properties of instable system depend on the environment, ensemble equivalence is broken

Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

Science.gov (United States)

Zhang, Yue; Fu, Kun; Sun, Xian; Xu, Guangluan; Wang, Hongqi

2016-06-01

The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS

Directory of Open Access Journals (Sweden)

Y. Zhang

2016-06-01

Full Text Available The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

Notes on the Implementation of Non-Parametric Statistics within the Westinghouse Realistic Large Break LOCA Evaluation Model (ASTRUM)

International Nuclear Information System (INIS)

Frepoli, Cesare; Oriani, Luca

2006-01-01

In recent years, non-parametric or order statistics methods have been widely used to assess the impact of the uncertainties within Best-Estimate LOCA evaluation models. The bounding of the uncertainties is achieved with a direct Monte Carlo sampling of the uncertainty attributes, with the minimum trial number selected to 'stabilize' the estimation of the critical output values (peak cladding temperature (PCT), local maximum oxidation (LMO), and core-wide oxidation (CWO A non-parametric order statistics uncertainty analysis was recently implemented within the Westinghouse Realistic Large Break LOCA evaluation model, also referred to as 'Automated Statistical Treatment of Uncertainty Method' (ASTRUM). The implementation or interpretation of order statistics in safety analysis is not fully consistent within the industry. This has led to an extensive public debate among regulators and researchers which can be found in the open literature. The USNRC-approved Westinghouse method follows a rigorous implementation of the order statistics theory, which leads to the execution of 124 simulations within a Large Break LOCA analysis. This is a solid approach which guarantees that a bounding value (at 95% probability) of the 95 th percentile for each of the three 10 CFR 50.46 ECCS design acceptance criteria (PCT, LMO and CWO) is obtained. The objective of this paper is to provide additional insights on the ASTRUM statistical approach, with a more in-depth analysis of pros and cons of the order statistics and of the Westinghouse approach in the implementation of this statistical methodology. (authors)

Statistical models for expert judgement and wear prediction

International Nuclear Information System (INIS)

Pulkkinen, U.

1994-01-01

This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)

Security of statistical data bases: invasion of privacy through attribute correlational modeling

Energy Technology Data Exchange (ETDEWEB)

Palley, M.A.

1985-01-01

This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queries of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.

A no extensive statistical model for the nucleon structure function

International Nuclear Information System (INIS)

Trevisan, Luis A.; Mirez, Carlos

2013-01-01

We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon.

Understanding search trees via statistical physics

Indian Academy of Sciences (India)

ary search tree model (where stands for the number of branches of the search tree), an important problem for data storage in computer science, using a variety of statistical physics techniques that allow us to obtain exact asymptotic results.

A RANS knock model to predict the statistical occurrence of engine knock

International Nuclear Information System (INIS)

D'Adamo, Alessandro; Breda, Sebastiano; Fontanesi, Stefano; Irimescu, Adrian; Merola, Simona Silvia; Tornatore, Cinzia

2017-01-01

the experimental evidence. The proposed model estimates not only the regions where the average knock is promoted, but also where and when the first knock is more likely to be encountered. The application of the model to a RANS simulation of a modern turbocharged direct injection (DI) SI engine with optical access is presented and the analysis of the knock statistical occurrence obtained by the proposed model adds an innovative contribution to overcome the limitation of consolidated “average knock” analyses typical of a RANS approach.

Inclusion of temperature dependence of fission barriers in statistical model calculations

International Nuclear Information System (INIS)

Newton, J.O.; Popescu, D.G.; Leigh, J.R.

1990-08-01

The temperature dependence of fission barriers has been interpolated from the results of recent theoretical calculations and included in the statistical model code PACE2. It is shown that the inclusion of temperature dependence causes significant changes to the values of the statistical model parameters deduced from fits to experimental data. 21 refs., 2 figs

A Census of Statistics Requirements at U.S. Journalism Programs and a Model for a "Statistics for Journalism" Course

Science.gov (United States)

Martin, Justin D.

2017-01-01

This essay presents data from a census of statistics requirements and offerings at all 4-year journalism programs in the United States (N = 369) and proposes a model of a potential course in statistics for journalism majors. The author proposes that three philosophies underlie a statistics course for journalism students. Such a course should (a)…

Reduced order modeling, statistical analysis and system identification for a bladed rotor with geometric mistuning

Science.gov (United States)

Vishwakarma, Vinod

Modified Modal Domain Analysis (MMDA) is a novel method for the development of a reduced-order model (ROM) of a bladed rotor. This method utilizes proper orthogonal decomposition (POD) of Coordinate Measurement Machine (CMM) data of blades' geometries and sector analyses using ANSYS. For the first time ROM of a geometrically mistuned industrial scale rotor (Transonic rotor) with large size of Finite Element (FE) model is generated using MMDA. Two methods for estimating mass and stiffness mistuning matrices are used a) exact computation from sector FE analysis, b) estimates based on POD mistuning parameters. Modal characteristics such as mistuned natural frequencies, mode shapes and forced harmonic response are obtained from ROM for various cases, and results are compared with full rotor ANSYS analysis and other ROM methods such as Subset of Nominal Modes (SNM) and Fundamental Model of Mistuning (FMM). Accuracy of MMDA ROM is demonstrated with variations in number of POD features and geometric mistuning parameters. It is shown for the aforementioned case b) that the high accuracy of ROM studied in previous work with Academic rotor does not directly translate to the Transonic rotor. Reasons for such mismatch in results are investigated and attributed to higher mistuning in Transonic rotor. Alternate solutions such as estimation of sensitivities via least squares, and interpolation of mass and stiffness matrices on manifolds are developed, and their results are discussed. Statistics such as mean and standard deviations of forced harmonic response peak amplitude are obtained from random permutations, and are shown to have similar results as those of Monte Carlo simulations. These statistics are obtained and compared for 3 degree of freedom (DOF) lumped parameter model (LPM) of rotor, Academic rotor and Transonic rotor. A state -- estimator based on MMDA ROM and Kalman filter is also developed for offline or online estimation of harmonic forcing function from

Central Limit Theorem for Exponentially Quasi-local Statistics of Spin Models on Cayley Graphs

Science.gov (United States)

Reddy, Tulasi Ram; Vadlamani, Sreekar; Yogeshwaran, D.

2018-04-01

Central limit theorems for linear statistics of lattice random fields (including spin models) are usually proven under suitable mixing conditions or quasi-associativity. Many interesting examples of spin models do not satisfy mixing conditions, and on the other hand, it does not seem easy to show central limit theorem for local statistics via quasi-associativity. In this work, we prove general central limit theorems for local statistics and exponentially quasi-local statistics of spin models on discrete Cayley graphs with polynomial growth. Further, we supplement these results by proving similar central limit theorems for random fields on discrete Cayley graphs taking values in a countable space, but under the stronger assumptions of α -mixing (for local statistics) and exponential α -mixing (for exponentially quasi-local statistics). All our central limit theorems assume a suitable variance lower bound like many others in the literature. We illustrate our general central limit theorem with specific examples of lattice spin models and statistics arising in computational topology, statistical physics and random networks. Examples of clustering spin models include quasi-associated spin models with fast decaying covariances like the off-critical Ising model, level sets of Gaussian random fields with fast decaying covariances like the massive Gaussian free field and determinantal point processes with fast decaying kernels. Examples of local statistics include intrinsic volumes, face counts, component counts of random cubical complexes while exponentially quasi-local statistics include nearest neighbour distances in spin models and Betti numbers of sub-critical random cubical complexes.

Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions.

Science.gov (United States)

Nishino, Ko; Lombardi, Stephen

2011-01-01

We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.

High energy behaviour of particles and unified statistics

International Nuclear Information System (INIS)

Chang, Y.

1984-01-01

Theories and experiments suggest that particles at high energy appear to possess a new statistics unifying Bose-Einstein and Fermi-Dirac statistics via the GAMMA distribution. This hypothesis can be obtained from many models, and agrees quantitatively with scaling, the multiplicty, large transverse momentum, the mass spectrum, and other data. It may be applied to scatterings at high energy, and agrees with experiments and known QED's results. The Veneziano model and other theories have implied new statistics, such as, the B distribution and the Polya distribution. They revert to the GAMMA distribution at high energy. The possible inapplicability of Pauli's exclusion principle within the unified statistics is considered and associated to the quark constituents

Whole vertebral bone segmentation method with a statistical intensity-shape model based approach

Science.gov (United States)

Hanaoka, Shouhei; Fritscher, Karl; Schuler, Benedikt; Masutani, Yoshitaka; Hayashi, Naoto; Ohtomo, Kuni; Schubert, Rainer

2011-03-01

An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939 mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar vertebrae.

Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.

Science.gov (United States)

MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C

2018-03-29

This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables

Directory of Open Access Journals (Sweden)

Philip Leifeld

2013-11-01

Full Text Available A recurrent task in applied statistics is the (mostly manual preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.

Bayesian statistic methods and theri application in probabilistic simulation models

Directory of Open Access Journals (Sweden)

Sergio Iannazzo

2007-03-01

Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

Statistical modelling for recurrent events: an application to sports injuries.

Science.gov (United States)

Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F

2014-09-01

Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A classical statistical model of heavy ion collisions

International Nuclear Information System (INIS)

Schmidt, R.; Teichert, J.

1980-01-01

The use of the computer code TRAJEC which represents the numerical realization of a classical statistical model for heavy ion collisions is described. The code calculates the results of a classical friction model as well as various multi-differential cross sections for heavy ion collisions. INPUT and OUTPUT information of the code are described. Two examples of data sets are given [ru

The GNASH preequilibrium-statistical nuclear model code

International Nuclear Information System (INIS)

Arthur, E. D.

1988-01-01

The following report is based on materials presented in a series of lectures at the International Center for Theoretical Physics, Trieste, which were designed to describe the GNASH preequilibrium statistical model code and its use. An overview is provided of the code with emphasis upon code's calculational capabilities and the theoretical models that have been implemented in it. Two sample problems are discussed, the first dealing with neutron reactions on 58 Ni. the second illustrates the fission model capabilities implemented in the code and involves n + 235 U reactions. Finally a description is provided of current theoretical model and code development underway. Examples of calculated results using these new capabilities are also given. 19 refs., 17 figs., 3 tabs

Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

DEFF Research Database (Denmark)

ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto

2015-01-01

We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting pa...

Improving Fuel Statistics for Danish Aviation

DEFF Research Database (Denmark)

Winther, M.

This report contains fuel use figures for Danish civil aviation broken down into domestic and international numbers from 1985 to 2000, using a refined fuel split procedure and official fuel sale totals. The results from two different models are used. The NERI (National Environmental Research...... Institute) model estimates the fuel use per flight for all flights leaving Danish airports in 1998, while the annual Danish CORINAIR inventories are based on improved LTO/aircraft type statistics. A time series of fuel use from 1985 to 2000 is also shown for flights between Denmark and Greenland/the Faroe...... Islands, obtained with the NERI model. In addition a complete overview of the aviation fuel use from the two latter areas is given, based on fuel sale information from Statistics Greenland and Statistics Faroe Islands, and fuel use data from airline companies. The fuel use figures are presented on a level...

The Statistical Modeling of the Trends Concerning the Romanian Population

Directory of Open Access Journals (Sweden)

Gabriela OPAIT

2014-11-01

Full Text Available This paper reflects the statistical modeling concerning the resident population in Romania, respectively the total of the romanian population, through by means of the „Least Squares Method”. Any country it develops by increasing of the population, respectively of the workforce, which is a factor of influence for the growth of the Gross Domestic Product (G.D.P.. The „Least Squares Method” represents a statistical technique for to determine the trend line of the best fit concerning a model.

Statistical modeling of copper losses in the silicate slag of the sulfide concentrate smelting process

Directory of Open Access Journals (Sweden)

Savic Marija V.

2015-09-01

Full Text Available This article presents the results of the statistical modeling of copper losses in the silicate slag of the sulfide concentrates smelting process. The aim of this study was to define the correlation dependence of the degree of copper losses in the silicate slag on the following parameters of technological processes: SiO2, FeO, Fe3O4, CaO and Al2O3 content in the slag and copper content in the matte. Multiple linear regression analysis (MLRA, artificial neural networks (ANNs and adaptive network based fuzzy inference system (ANFIS were used as tools for mathematical analysis of the indicated problem. The best correlation coefficient (R2 = 0.719 of the final model was obtained using the ANFIS modeling approach.

Statistics Based Models for the Dynamics of Chernivtsi Children Disease

Directory of Open Access Journals (Sweden)

Igor G. Nesteruk

2017-10-01

Full Text Available Background. Simple mathematical models of contamination and SIR-model of spreading an infection were used to simulate the time dynamics of the unknown before children disease, which occurred in Chernivtsi (Ukraine. The cause of many cases of alopecia, which began in this city in August 1988 is still not fully clarified. According to the official report of the governmental commission, the last new cases occurred in the middle of November 1988, and the reason of the illness was reported as chemical exogenous intoxication. Later this illness became the name “Chernivtsi chemical disease”. Nevertheless, the significantly increased number of new cases of the local alopecia was registered almost three years and is still not clarified. Objective. The comparison of two different versions of the disease: chemical exogenous intoxication and infection. Identification of the parameters of mathematical models and prediction of the disease development. Methods. Analytical solutions of the contamination models and SIR-model for an epidemic are obtained. The optimal values of parameters with the use of linear regression were found. Results. The optimal values of the models parameters with the use of statistical approach were identified. The calculations showed that the infectious version of the disease is more reliable in comparison with the popular contamination one. The possible date of the epidemic beginning was estimated. Conclusions. The optimal parameters of SIR-model allow calculating the realistic number of victims and other characteristics of possible epidemic. They also show that increased number of cases of local alopecia could be a part of the same epidemic as “Chernivtsi chemical disease”.

Sound statistical model checking for MDP using partial order and confluence reduction

NARCIS (Netherlands)

Hartmanns, Arnd; Timmer, Mark

Statistical model checking (SMC) is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can in general only provide sound

Statistical modeling to support power system planning

Science.gov (United States)

Staid, Andrea

This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today's power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate

Record statistics of financial time series and geometric random walks.

Science.gov (United States)

Sabir, Behlool; Santhanam, M S

2014-09-01

The study of record statistics of correlated series in physics, such as random walks, is gaining momentum, and several analytical results have been obtained in the past few years. In this work, we study the record statistics of correlated empirical data for which random walk models have relevance. We obtain results for the records statistics of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent α lying in the range 1.5≤α≤1.8. Further, the longest record ages follow the Fréchet distribution of extreme value theory. The records statistics of geometric random walk series is in good agreement with that obtained from empirical stock data.

Efficient Parallel Statistical Model Checking of Biochemical Networks

Directory of Open Access Journals (Sweden)

Paolo Ballarini

2009-12-01

Full Text Available We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.

A Model of Statistics Performance Based on Achievement Goal Theory.

Science.gov (United States)

Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.

2003-01-01

Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…

Enhanced surrogate models for statistical design exploiting space mapping technology

DEFF Research Database (Denmark)

Koziel, Slawek; Bandler, John W.; Mohamed, Achmed S.

2005-01-01

We present advances in microwave and RF device modeling exploiting Space Mapping (SM) technology. We propose new SM modeling formulations utilizing input mappings, output mappings, frequency scaling and quadratic approximations. Our aim is to enhance circuit models for statistical analysis...

SU-C-BRD-01: A Statistical Modeling Method for Quality Control of Intensity- Modulated Radiation Therapy Planning

International Nuclear Information System (INIS)

Gao, S; Meyer, R; Shi, L; D'Souza, W; Zhang, H

2014-01-01

Purpose: To apply a statistical modeling approach, threshold modeling (TM), for quality control of intensity-modulated radiation therapy (IMRT) treatment plans. Methods: A quantitative measure, which was the weighted sum of violations of dose/dose-volume constraints, was first developed to represent the quality of each IMRT plan. Threshold modeling approach, which is is an extension of extreme value theory in statistics and is an effect way to model extreme values, was then applied to analyze the quality of the plans summarized by our quantitative measures. Our approach modeled the plans generated by planners as a series of independent and identically distributed random variables and described the behaviors of them if the plan quality was controlled below certain threshold. We tested our approach with five locally advanced head and neck cancer patients retrospectively. Two statistics were incorporated for numerical analysis: probability of quality improvement (PQI) of the plans and expected amount of improvement on the quantitative measure (EQI). Results: After clinical planners generated 15 plans for each patient, we applied our approach to obtain the PQI and EQI as if planners would generate additional 15 plans. For two of the patients, the PQI was significantly higher than the other three (0.17 and 0.18 comparing to 0.08, 0.01 and 0.01). The actual percentage of the additional 15 plans that outperformed the best of initial 15 plans was 20% and 27% comparing to 11%, 0% and 0%. EQI for the two potential patients were 34.5 and 32.9 and the rest of three patients were 9.9, 1.4 and 6.6. The actual improvements obtained were 28.3 and 20.5 comparing to 6.2, 0 and 0. Conclusion: TM is capable of reliably identifying the potential quality improvement of IMRT plans. It provides clinicians an effective tool to assess the trade-off between extra planning effort and achievable plan quality. This work was supported in part by NIH/NCI grant CA130814

Logarithmic transformed statistical models in calibration

International Nuclear Information System (INIS)

Zeis, C.D.

1975-01-01

A general type of statistical model used for calibration of instruments having the property that the standard deviations of the observed values increase as a function of the mean value is described. The application to the Helix Counter at the Rocky Flats Plant is primarily from a theoretical point of view. The Helix Counter measures the amount of plutonium in certain types of chemicals. The method described can be used also for other calibrations. (U.S.)

Numerical solutions of the semiclassical Boltzmann ellipsoidal-statistical kinetic model equation

Science.gov (United States)

Yang, Jaw-Yen; Yan, Chin-Yuan; Huang, Juan-Chen; Li, Zhihui

2014-01-01

Computations of rarefied gas dynamical flows governed by the semiclassical Boltzmann ellipsoidal-statistical (ES) kinetic model equation using an accurate numerical method are presented. The semiclassical ES model was derived through the maximum entropy principle and conserves not only the mass, momentum and energy, but also contains additional higher order moments that differ from the standard quantum distributions. A different decoding procedure to obtain the necessary parameters for determining the ES distribution is also devised. The numerical method in phase space combines the discrete-ordinate method in momentum space and the high-resolution shock capturing method in physical space. Numerical solutions of two-dimensional Riemann problems for two configurations covering various degrees of rarefaction are presented and various contours of the quantities unique to this new model are illustrated. When the relaxation time becomes very small, the main flow features a display similar to that of ideal quantum gas dynamics, and the present solutions are found to be consistent with existing calculations for classical gas. The effect of a parameter that permits an adjustable Prandtl number in the flow is also studied. PMID:25104904

Development of a statistical shape model of multi-organ and its performance evaluation

International Nuclear Information System (INIS)

Nakada, Misaki; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru

2010-01-01

Existing statistical shape modeling methods for an organ can not take into account the correlation between neighboring organs. This study focuses on a level set distribution model and proposes two modeling methods for multiple organs that can take into account the correlation between neighboring organs. The first method combines level set functions of multiple organs into a vector. Subsequently it analyses the distribution of the vectors of a training dataset by a principal component analysis and builds a multiple statistical shape model. Second method constructs a statistical shape model for each organ independently and assembles component scores of different organs in a training dataset so as to generate a vector. It analyses the distribution of the vectors of to build a statistical shape model of multiple organs. This paper shows results of applying the proposed methods trained by 15 abdominal CT volumes to unknown 8 CT volumes. (author)

Statistical validation of normal tissue complication probability models

NARCIS (Netherlands)

Xu, Cheng-Jian; van der Schaaf, Arjen; van t Veld, Aart; Langendijk, Johannes A.; Schilstra, Cornelis

2012-01-01

PURPOSE: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: A penalized regression method, LASSO (least absolute shrinkage

Building damage assessment from PolSAR data using texture parameters of statistical model

Science.gov (United States)

Li, Linlin; Liu, Xiuguo; Chen, Qihao; Yang, Shuai

2018-04-01

Accurate building damage assessment is essential in providing decision support for disaster relief and reconstruction. Polarimetric synthetic aperture radar (PolSAR) has become one of the most effective means of building damage assessment, due to its all-day/all-weather ability and richer backscatter information of targets. However, intact buildings that are not parallel to the SAR flight pass (termed oriented buildings) and collapsed buildings share similar scattering mechanisms, both of which are dominated by volume scattering. This characteristic always leads to misjudgments between assessments of collapsed buildings and oriented buildings from PolSAR data. Because the collapsed buildings and the intact buildings (whether oriented or parallel buildings) have different textures, a novel building damage assessment method is proposed in this study to address this problem by introducing texture parameters of statistical models. First, the logarithms of the estimated texture parameters of different statistical models are taken as a new texture feature to describe the collapse of the buildings. Second, the collapsed buildings and intact buildings are distinguished using an appropriate threshold. Then, the building blocks are classified into three levels based on the building block collapse rate. Moreover, this paper also discusses the capability for performing damage assessment using texture parameters from different statistical models or using different estimators. The RADARSAT-2 and ALOS-1 PolSAR images are used to present and analyze the performance of the proposed method. The results show that using the texture parameters avoids the problem of confusing collapsed and oriented buildings and improves the assessment accuracy. The results assessed by using the K/G0 distribution texture parameters estimated based on the second moment obtain the highest extraction accuracies. For the RADARSAT-2 and ALOS-1 data, the overall accuracy (OA) for these three types of

Statistical thermodynamics

International Nuclear Information System (INIS)

Lim, Gyeong Hui

2008-03-01

This book consists of 15 chapters, which are basic conception and meaning of statistical thermodynamics, Maxwell-Boltzmann's statistics, ensemble, thermodynamics function and fluctuation, statistical dynamics with independent particle system, ideal molecular system, chemical equilibrium and chemical reaction rate in ideal gas mixture, classical statistical thermodynamics, ideal lattice model, lattice statistics and nonideal lattice model, imperfect gas theory on liquid, theory on solution, statistical thermodynamics of interface, statistical thermodynamics of a high molecule system and quantum statistics

Accurate corresponding point search using sphere-attribute-image for statistical bone model generation

International Nuclear Information System (INIS)

Saito, Toki; Nakajima, Yoshikazu; Sugita, Naohiko; Mitsuishi, Mamoru; Hashizume, Hiroyuki; Kuramoto, Kouichi; Nakashima, Yosio

2011-01-01

Statistical deformable model based two-dimensional/three-dimensional (2-D/3-D) registration is a promising method for estimating the position and shape of patient bone in the surgical space. Since its accuracy depends on the statistical model capacity, we propose a method for accurately generating a statistical bone model from a CT volume. Our method employs the Sphere-Attribute-Image (SAI) and has improved the accuracy of corresponding point search in statistical model generation. At first, target bone surfaces are extracted as SAIs from the CT volume. Then the textures of SAIs are classified to some regions using Maximally-stable-extremal-regions methods. Next, corresponding regions are determined using Normalized cross-correlation (NCC). Finally, corresponding points in each corresponding region are determined using NCC. The application of our method to femur bone models was performed, and worked well in the experiments. (author)

An efficient soil water balance model based on hybrid numerical and statistical methods

Science.gov (United States)

Mao, Wei; Yang, Jinzhong; Zhu, Yan; Ye, Ming; Liu, Zhao; Wu, Jingwei

2018-04-01

Most soil water balance models only consider downward soil water movement driven by gravitational potential, and thus cannot simulate upward soil water movement driven by evapotranspiration especially in agricultural areas. In addition, the models cannot be used for simulating soil water movement in heterogeneous soils, and usually require many empirical parameters. To resolve these problems, this study derives a new one-dimensional water balance model for simulating both downward and upward soil water movement in heterogeneous unsaturated zones. The new model is based on a hybrid of numerical and statistical methods, and only requires four physical parameters. The model uses three governing equations to consider three terms that impact soil water movement, including the advective term driven by gravitational potential, the source/sink term driven by external forces (e.g., evapotranspiration), and the diffusive term driven by matric potential. The three governing equations are solved separately by using the hybrid numerical and statistical methods (e.g., linear regression method) that consider soil heterogeneity. The four soil hydraulic parameters required by the new models are as follows: saturated hydraulic conductivity, saturated water content, field capacity, and residual water content. The strength and weakness of the new model are evaluated by using two published studies, three hypothetical examples and a real-world application. The evaluation is performed by comparing the simulation results of the new model with corresponding results presented in the published studies, obtained using HYDRUS-1D and observation data. The evaluation indicates that the new model is accurate and efficient for simulating upward soil water flow in heterogeneous soils with complex boundary conditions. The new model is used for evaluating different drainage functions, and the square drainage function and the power drainage function are recommended. Computational efficiency of the new

Statistical modeling of static strengths of nuclear graphites with relevance to structural design

International Nuclear Information System (INIS)

Arai, Taketoshi

1992-02-01

Use of graphite materials for structural members poses a problem as to how to take into account of statistical properties of static strength, especially tensile fracture stresses, in component structural design. The present study concerns comprehensive examinations on statistical data base and modelings on nuclear graphites. First, the report provides individual samples and their analyses on strengths of IG-110 and PGX graphites for HTTR components. Those statistical characteristics on other HTGR graphites are also exemplified from the literature. Most of statistical distributions of individual samples are found to be approximately normal. The goodness of fit to normal distributions is more satisfactory with larger sample sizes. Molded and extruded graphites, however, possess a variety of statistical properties depending of samples from different with-in-log locations and/or different orientations. Second, the previous statistical models including the Weibull theory are assessed from the viewpoint of applicability to design procedures. This leads to a conclusion that the Weibull theory and its modified ones are satisfactory only for limited parts of tensile fracture behavior. They are not consistent for whole observations. Only normal statistics are justifiable as practical approaches to discuss specified minimum ultimate strengths as statistical confidence limits for individual samples. Third, the assessment of various statistical models emphasizes the need to develop advanced analytical ones which should involve modeling of microstructural features of actual graphite materials. Improvements of other structural design methodologies are also presented. (author)

Deterministic Method for Obtaining Nominal and Uncertainty Models of CD Drives

DEFF Research Database (Denmark)

Vidal, Enrique Sanchez; Stoustrup, Jakob; Andersen, Palle

2002-01-01

In this paper a deterministic method for obtaining the nominal and uncertainty models of the focus loop in a CD-player is presented based on parameter identification and measurements in the focus loop of 12 actual CD drives that differ by having worst-case behaviors with respect to various...... properties. The method provides a systematic way to derive a nominal average model as well as a structures multiplicative input uncertainty model, and it is demonstrated how to apply mu-theory to design a controller based on the models obtained that meets certain robust performance criteria....

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

International Nuclear Information System (INIS)

Lovejoy, S.; Lima, M. I. P. de

2015-01-01

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time

Monitor-Based Statistical Model Checking for Weighted Metric Temporal Logic

DEFF Research Database (Denmark)

Bulychev, Petr; David, Alexandre; Larsen, Kim Guldstrand

2012-01-01

We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction with desi......We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction...

Statistical Model of the 2001 Czech Census for Interactive Presentation

Czech Academy of Sciences Publication Activity Database

Grim, Jiří; Hora, Jan; Boček, Pavel; Somol, Petr; Pudil, Pavel

Vol. 26, č. 4 (2010), s. 1-23 ISSN 0282-423X R&D Projects: GA ČR GA102/07/1594; GA MŠk 1M0572 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Interactive statistical model * census data presentation * distribution mixtures * data modeling * EM algorithm * incomplete data * data reproduction accuracy * data mining Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.492, year: 2010 http://library.utia.cas.cz/separaty/2010/RO/grim-0350513.pdf

Statistical thermodynamics of a two-dimensional relativistic gas.

Science.gov (United States)

Montakhab, Afshin; Ghodrat, Malihe; Barati, Mahmood

2009-03-01

In this paper we study a fully relativistic model of a two-dimensional hard-disk gas. This model avoids the general problems associated with relativistic particle collisions and is therefore an ideal system to study relativistic effects in statistical thermodynamics. We study this model using molecular-dynamics simulation, concentrating on the velocity distribution functions. We obtain results for x and y components of velocity in the rest frame (Gamma) as well as the moving frame (Gamma;{'}) . Our results confirm that Jüttner distribution is the correct generalization of Maxwell-Boltzmann distribution. We obtain the same "temperature" parameter beta for both frames consistent with a recent study of a limited one-dimensional model. We also address the controversial topic of temperature transformation. We show that while local thermal equilibrium holds in the moving frame, relying on statistical methods such as distribution functions or equipartition theorem are ultimately inconclusive in deciding on a correct temperature transformation law (if any).

A statistical mechanical model of economics

Science.gov (United States)

Lubbers, Nicholas Edward Williams

Statistical mechanics pursues low-dimensional descriptions of systems with a very large number of degrees of freedom. I explore this theme in two contexts. The main body of this dissertation explores and extends the Yard Sale Model (YSM) of economic transactions using a combination of simulations and theory. The YSM is a simple interacting model for wealth distributions which has the potential to explain the empirical observation of Pareto distributions of wealth. I develop the link between wealth condensation and the breakdown of ergodicity due to nonlinear diffusion effects which are analogous to the geometric random walk. Using this, I develop a deterministic effective theory of wealth transfer in the YSM that is useful for explaining many quantitative results. I introduce various forms of growth to the model, paying attention to the effect of growth on wealth condensation, inequality, and ergodicity. Arithmetic growth is found to partially break condensation, and geometric growth is found to completely break condensation. Further generalizations of geometric growth with growth in- equality show that the system is divided into two phases by a tipping point in the inequality parameter. The tipping point marks the line between systems which are ergodic and systems which exhibit wealth condensation. I explore generalizations of the YSM transaction scheme to arbitrary betting functions to develop notions of universality in YSM-like models. I find that wealth vi condensation is universal to a large class of models which can be divided into two phases. The first exhibits slow, power-law condensation dynamics, and the second exhibits fast, finite-time condensation dynamics. I find that the YSM, which exhibits exponential dynamics, is the critical, self-similar model which marks the dividing line between the two phases. The final chapter develops a low-dimensional approach to materials microstructure quantification. Modern materials design harnesses complex

Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos

DEFF Research Database (Denmark)

Ganz, Melanie; Nielsen, Mads; Brandt, Sami

2010-01-01

We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning ...

Statistically Modeling I-V Characteristics of CNT-FET with LASSO

Science.gov (United States)

Ma, Dongsheng; Ye, Zuochang; Wang, Yan

2017-08-01

With the advent of internet of things (IOT), the need for studying new material and devices for various applications is increasing. Traditionally we build compact models for transistors on the basis of physics. But physical models are expensive and need a very long time to adjust for non-ideal effects. As the vision for the application of many novel devices is not certain or the manufacture process is not mature, deriving generalized accurate physical models for such devices is very strenuous, whereas statistical modeling is becoming a potential method because of its data oriented property and fast implementation. In this paper, one classical statistical regression method, LASSO, is used to model the I-V characteristics of CNT-FET and a pseudo-PMOS inverter simulation based on the trained model is implemented in Cadence. The normalized relative mean square prediction error of the trained model versus experiment sample data and the simulation results show that the model is acceptable for digital circuit static simulation. And such modeling methodology can extend to general devices.

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.

Science.gov (United States)

Sapsis, Themistoklis P; Majda, Andrew J

2013-08-20

A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.

Statistical model for prediction of hearing loss in patients receiving cisplatin chemotherapy.

Science.gov (United States)

Johnson, Andrew; Tarima, Sergey; Wong, Stuart; Friedland, David R; Runge, Christina L

2013-03-01

This statistical model might be used to predict cisplatin-induced hearing loss, particularly in patients undergoing concomitant radiotherapy. To create a statistical model based on pretreatment hearing thresholds to provide an individual probability for hearing loss from cisplatin therapy and, secondarily, to investigate the use of hearing classification schemes as predictive tools for hearing loss. Retrospective case-control study. Tertiary care medical center. A total of 112 subjects receiving chemotherapy and audiometric evaluation were evaluated for the study. Of these subjects, 31 met inclusion criteria for analysis. The primary outcome measurement was a statistical model providing the probability of hearing loss following the use of cisplatin chemotherapy. Fifteen of the 31 subjects had significant hearing loss following cisplatin chemotherapy. American Academy of Otolaryngology-Head and Neck Society and Gardner-Robertson hearing classification schemes revealed little change in hearing grades between pretreatment and posttreatment evaluations for subjects with or without hearing loss. The Chang hearing classification scheme could effectively be used as a predictive tool in determining hearing loss with a sensitivity of 73.33%. Pretreatment hearing thresholds were used to generate a statistical model, based on quadratic approximation, to predict hearing loss (C statistic = 0.842, cross-validated = 0.835). The validity of the model improved when only subjects who received concurrent head and neck irradiation were included in the analysis (C statistic = 0.91). A calculated cutoff of 0.45 for predicted probability has a cross-validated sensitivity and specificity of 80%. Pretreatment hearing thresholds can be used as a predictive tool for cisplatin-induced hearing loss, particularly with concomitant radiotherapy.

Statistics of a neuron model driven by asymmetric colored noise.

Science.gov (United States)

Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

2015-02-01

Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

Improving thermal model prediction through statistical analysis of irradiation and post-irradiation data from AGR experiments

International Nuclear Information System (INIS)

Pham, Binh T.; Hawkes, Grant L.; Einerson, Jeffrey J.

2014-01-01

As part of the High Temperature Reactors (HTR) R and D program, a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. While not possible to obtain by direct measurements in the tests, crucial fuel conditions (e.g., temperature, neutron fast fluence, and burnup) are calculated using core physics and thermal modeling codes. This paper is focused on AGR test fuel temperature predicted by the ABAQUS code's finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for qualification of AGR-1 thermocouple data. Abnormal trends in measured data revealed by the statistical analysis are traced to either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. The main thrust of this work is to exploit the variety of data obtained in irradiation and post-irradiation examination (PIE) for assessment of modeling assumptions. As an example, the uneven reduction of the control gas gap in Capsule 5 found in the capsule metrology measurements in PIE helps identify mechanisms other than TC drift causing the decrease in TC readings. This suggests a more physics-based modification of the thermal model that leads to a better fit with experimental data, thus reducing model uncertainty and increasing confidence in the calculated fuel temperatures of the AGR-1 test

Improving thermal model prediction through statistical analysis of irradiation and post-irradiation data from AGR experiments

Energy Technology Data Exchange (ETDEWEB)

Pham, Binh T., E-mail: Binh.Pham@inl.gov [Human Factor, Controls and Statistics Department, Nuclear Science and Technology, Idaho National Laboratory, Idaho Falls, ID 83415 (United States); Hawkes, Grant L. [Thermal Science and Safety Analysis Department, Nuclear Science and Technology, Idaho National Laboratory, Idaho Falls, ID 83415 (United States); Einerson, Jeffrey J. [Human Factor, Controls and Statistics Department, Nuclear Science and Technology, Idaho National Laboratory, Idaho Falls, ID 83415 (United States)

2014-05-01

As part of the High Temperature Reactors (HTR) R and D program, a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. While not possible to obtain by direct measurements in the tests, crucial fuel conditions (e.g., temperature, neutron fast fluence, and burnup) are calculated using core physics and thermal modeling codes. This paper is focused on AGR test fuel temperature predicted by the ABAQUS code's finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for qualification of AGR-1 thermocouple data. Abnormal trends in measured data revealed by the statistical analysis are traced to either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. The main thrust of this work is to exploit the variety of data obtained in irradiation and post-irradiation examination (PIE) for assessment of modeling assumptions. As an example, the uneven reduction of the control gas gap in Capsule 5 found in the capsule metrology measurements in PIE helps identify mechanisms other than TC drift causing the decrease in TC readings. This suggests a more physics-based modification of the thermal model that leads to a better fit with experimental data, thus reducing model uncertainty and increasing confidence in the calculated fuel temperatures of the AGR-1 test.

Statistical aspects of carbon fiber risk assessment modeling. [fire accidents involving aircraft

Science.gov (United States)

Gross, D.; Miller, D. R.; Soland, R. M.

1980-01-01

The probabilistic and statistical aspects of the carbon fiber risk assessment modeling of fire accidents involving commercial aircraft are examined. Three major sources of uncertainty in the modeling effort are identified. These are: (1) imprecise knowledge in establishing the model; (2) parameter estimation; and (3)Monte Carlo sampling error. All three sources of uncertainty are treated and statistical procedures are utilized and/or developed to control them wherever possible.

Acid hydrolysis of corn stover using hydrochloric acid: Kinetic modeling and statistical optimization

Directory of Open Access Journals (Sweden)

Sun Yong

2014-01-01

Full Text Available The hydrolysis of corn stover using hydrochloric acid was studied. The kinetic parameters of the mathematical models for predicting the yields of xylose, glucose, furfural and acetic acid were obtained, and the corresponding xylose generation activation energy of 100 kJ/mol was determined. The characterization of corn stover using with different techniques during hydrolysis indicated an effective removal of xylan and the slightly alteration on the structures of cellulose and lignin. A 23five levels Central Composite Design (CCD was used to develop a statistical model for the optimization of process variables including acid concentration, pretreatment temperature and time. The optimum conditions determined by this model were found to be 108ºC for 80 minutes with acid concentration of 5.8%. Under these conditions, the maximised results are the following: xylose 19.93 g/L, glucose 1.2 g/L, furfural 1.5 g/L, acetic acid 1.3 g/L. The validation of the model indicates a good agreement between the experimental results and the predicted values.

Model output statistics applied to wind power prediction

Energy Technology Data Exchange (ETDEWEB)

Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

1999-03-01

Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

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

Science.gov (United States)

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''.

Model selection for contingency tables with algebraic statistics

NARCIS (Netherlands)

Krampe, A.; Kuhnt, S.; Gibilisco, P.; Riccimagno, E.; Rogantin, M.P.; Wynn, H.P.

2009-01-01

Goodness-of-fit tests based on chi-square approximations are commonly used in the analysis of contingency tables. Results from algebraic statistics combined with MCMC methods provide alternatives to the chi-square approximation. However, within a model selection procedure usually a large number of

Nanoscale Optimization and Statistical Modeling of Photoelectrochemical Water Splitting Efficiency of N-Doped TiO2 Nanotubes

KAUST Repository

Isimjan, Tayirjan T.

2014-12-19

Highly ordered nitrogen-doped titanium dioxide (N-doped TiO2) nanotube array films with enhanced photo-electrochemical water splitting efficiency (PCE) for hydrogen generation were fabricated by electrochemical anodization, followed by annealing in a nitrogen atmosphere. Morphology, structure and composition of the N-doped TiO2 nanotube array films were investigated by FE-SEM, XPS, UV-Vis and XRD. The effect of annealing temperature, heating rate and annealing time on the morphology, structure, and photo-electrochemical property of the N-doped TiO2 nanotube array films were investigated. A design of experiments method was applied in order to minimize the number of experiments and obtain a statistical model for this system. From the modelling results, optimum values for the influential factors were obtained in order to achieve the maximum PCE. The optimized experiment resulted in 7.42 % PCE which was within 95 % confidence interval of the predicted value by the model. © 2014 Springer Science+Business Media.

New robust statistical procedures for the polytomous logistic regression models.

Science.gov (United States)

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

Science.gov (United States)

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become

Probing the exchange statistics of one-dimensional anyon models

Science.gov (United States)

Greschner, Sebastian; Cardarelli, Lorenzo; Santos, Luis

2018-05-01

We propose feasible scenarios for revealing the modified exchange statistics in one-dimensional anyon models in optical lattices based on an extension of the multicolor lattice-depth modulation scheme introduced in [Phys. Rev. A 94, 023615 (2016), 10.1103/PhysRevA.94.023615]. We show that the fast modulation of a two-component fermionic lattice gas in the presence a magnetic field gradient, in combination with additional resonant microwave fields, allows for the quantum simulation of hardcore anyon models with periodic boundary conditions. Such a semisynthetic ring setup allows for realizing an interferometric arrangement sensitive to the anyonic statistics. Moreover, we show as well that simple expansion experiments may reveal the formation of anomalously bound pairs resulting from the anyonic exchange.

Statistical inference for noisy nonlinear ecological dynamic systems.

Science.gov (United States)

Wood, Simon N

2010-08-26

Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a 'synthetic likelihood' that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson's classic blowfly experiments.

Isotopic safeguards statistics

International Nuclear Information System (INIS)

Timmerman, C.L.; Stewart, K.B.

1978-06-01

The methods and results of our statistical analysis of isotopic data using isotopic safeguards techniques are illustrated using example data from the Yankee Rowe reactor. The statistical methods used in this analysis are the paired comparison and the regression analyses. A paired comparison results when a sample from a batch is analyzed by two different laboratories. Paired comparison techniques can be used with regression analysis to detect and identify outlier batches. The second analysis tool, linear regression, involves comparing various regression approaches. These approaches use two basic types of models: the intercept model (y = α + βx) and the initial point model [y - y 0 = β(x - x 0 )]. The intercept model fits strictly the exposure or burnup values of isotopic functions, while the initial point model utilizes the exposure values plus the initial or fabricator's data values in the regression analysis. Two fitting methods are applied to each of these models. These methods are: (1) the usual least squares fitting approach where x is measured without error, and (2) Deming's approach which uses the variance estimates obtained from the paired comparison results and considers x and y are both measured with error. The Yankee Rowe data were first measured by Nuclear Fuel Services (NFS) and remeasured by Nuclear Audit and Testing Company (NATCO). The ratio of Pu/U versus 235 D (in which 235 D is the amount of depleted 235 U expressed in weight percent) using actual numbers is the isotopic function illustrated. Statistical results using the Yankee Rowe data indicates the attractiveness of Deming's regression model over the usual approach by simple comparison of the given regression variances with the random variance from the paired comparison results

CONFIDENCE LEVELS AND/VS. STATISTICAL HYPOTHESIS TESTING IN STATISTICAL ANALYSIS. CASE STUDY

Directory of Open Access Journals (Sweden)

ILEANA BRUDIU

2009-05-01

Full Text Available Estimated parameters with confidence intervals and testing statistical assumptions used in statistical analysis to obtain conclusions on research from a sample extracted from the population. Paper to the case study presented aims to highlight the importance of volume of sample taken in the study and how this reflects on the results obtained when using confidence intervals and testing for pregnant. If statistical testing hypotheses not only give an answer "yes" or "no" to some questions of statistical estimation using statistical confidence intervals provides more information than a test statistic, show high degree of uncertainty arising from small samples and findings build in the "marginally significant" or "almost significant (p very close to 0.05.

Bayesian Sensitivity Analysis of Statistical Models with Missing Data.

Science.gov (United States)

Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng

2014-04-01

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.

Statistical shape (ASM) and appearance (AAM) models for the segmentation of the cerebellum in fetal ultrasound

Science.gov (United States)

Reyes López, Misael; Arámbula Cosío, Fernando

2017-11-01

The cerebellum is an important structure to determine the gestational age of the fetus, moreover most of the abnormalities it presents are related to growth disorders. In this work, we present the results of the segmentation of the fetal cerebellum applying statistical shape and appearance models. Both models were tested on ultrasound images of the fetal brain taken from 23 pregnant women, between 18 and 24 gestational weeks. The accuracy results obtained on 11 ultrasound images show a mean Hausdorff distance of 6.08 mm between the manual segmentation and the segmentation using active shape model, and a mean Hausdorff distance of 7.54 mm between the manual segmentation and the segmentation using active appearance model. The reported results demonstrate that the active shape model is more robust in the segmentation of the fetal cerebellum in ultrasound images.

Atmospheric corrosion: statistical validation of models

International Nuclear Information System (INIS)

Diaz, V.; Martinez-Luaces, V.; Guineo-Cobs, G.

2003-01-01

In this paper we discuss two different methods for validation of regression models, applied to corrosion data. One of them is based on the correlation coefficient and the other one is the statistical test of lack of fit. Both methods are used here to analyse fitting of bi logarithmic model in order to predict corrosion for very low carbon steel substrates in rural and urban-industrial atmospheres in Uruguay. Results for parameters A and n of the bi logarithmic model are reported here. For this purpose, all repeated values were used instead of using average values as usual. Modelling is carried out using experimental data corresponding to steel substrates under the same initial meteorological conditions ( in fact, they are put in the rack at the same time). Results of correlation coefficient are compared with the lack of it tested at two different signification levels (α=0.01 and α=0.05). Unexpected differences between them are explained and finally, it is possible to conclude, at least in the studied atmospheres, that the bi logarithmic model does not fit properly the experimental data. (Author) 18 refs

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

Science.gov (United States)

Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T

2016-05-01

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.

The power and robustness of maximum LOD score statistics.

Science.gov (United States)

Yoo, Y J; Mendell, N R

2008-07-01

The maximum LOD score statistic is extremely powerful for gene mapping when calculated using the correct genetic parameter value. When the mode of genetic transmission is unknown, the maximum of the LOD scores obtained using several genetic parameter values is reported. This latter statistic requires higher critical value than the maximum LOD score statistic calculated from a single genetic parameter value. In this paper, we compare the power of maximum LOD scores based on three fixed sets of genetic parameter values with the power of the LOD score obtained after maximizing over the entire range of genetic parameter values. We simulate family data under nine generating models. For generating models with non-zero phenocopy rates, LOD scores maximized over the entire range of genetic parameters yielded greater power than maximum LOD scores for fixed sets of parameter values with zero phenocopy rates. No maximum LOD score was consistently more powerful than the others for generating models with a zero phenocopy rate. The power loss of the LOD score maximized over the entire range of genetic parameters, relative to the maximum LOD score calculated using the correct genetic parameter value, appeared to be robust to the generating models.

What type of statistical model to choose for the analysis of radioimmunoassays

International Nuclear Information System (INIS)

Huet, S.

1984-01-01

The current techniques used for statistical analysis of radioimmunoassays are not very satisfactory for either the statistician or the biologist. They are based on an attempt to make the response curve linear to avoid complicated computations. The present article shows that this practice has considerable effects (often neglected) on the statistical assumptions which must be formulated. A more strict analysis is proposed by applying the four-parameter logistic model. The advantages of this method are: the statistical assumptions formulated are based on observed data, and the model can be applied to almost all radioimmunoassays [fr

Development of temperature statistical model when machining of aerospace alloy materials

Directory of Open Access Journals (Sweden)

Kadirgama Kumaran

2014-01-01

Full Text Available This paper presents to develop first-order models for predicting the cutting temperature for end-milling operation of Hastelloy C-22HS by using four different coated carbide cutting tools and two different cutting environments. The first-order equations of cutting temperature are developed using the response surface methodology (RSM. The cutting variables are cutting speed, feed rate, and axial depth. The analyses are carried out with the aid of the statistical software package. It can be seen that the model is suitable to predict the longitudinal component of the cutting temperature close to those readings recorded experimentally with a 95% confident level. The results obtained from the predictive models are also compared with results obtained from finite-element analysis (FEA. The developed first-order equations for the cutting temperature revealed that the feed rate is the most crucial factor, followed by axial depth and cutting speed. The PVD coated cutting tools perform better than the CVD-coated cutting tools in terms of cutting temperature. The cutting tools coated with TiAlN perform better compared with other cutting tools during the machining performance of Hastelloy C-22HS. It followed by TiN/TiCN/TiN and CVD coated with TiN/TiCN/Al2O3 and TiN/TiCN/TiN. From the finite-element analysis, the distribution of the cutting temperature can be discussed. High temperature appears in the lower sliding friction zone and at the cutting tip of the cutting tool. Maximum temperature is developed at the rake face some distance away from the tool nose, however, before the chip lift away.

Multimesonic decays of charmonium states in the statistical quark model

International Nuclear Information System (INIS)

Montvay, I.; Toth, J.D.

1978-01-01

The data known at present of multimesonic decays of chi and psi states are fitted in a statistical quark model, in which the matrix elements are assumed to be constant and resonances as well as both strong and second order electromagnetic processes are taken into account. The experimental data are well reproduced by the model. Unknown branching ratios for the rest of multimesonic channels are predicted. The fit leaves about 40% for baryonic and radiative channels in the case of J/psi(3095). The fitted parameters of the J/psi decays are used to predict the mesonic decays of the pseudoscalar eta c. The statistical quark model seems to allow the calculation of competitive multiparticle processes for the studied decays. (D.P.)

Uniting statistical and individual-based approaches for animal movement modelling.

Science.gov (United States)

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Statistical inference to advance network models in epidemiology.

Science.gov (United States)

Welch, David; Bansal, Shweta; Hunter, David R

2011-03-01

Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data. Copyright © 2011 Elsevier B.V. All rights reserved.

Statistical properties of several models of fractional random point processes

Science.gov (United States)

Bendjaballah, C.

2011-08-01

Statistical properties of several models of fractional random point processes have been analyzed from the counting and time interval statistics points of view. Based on the criterion of the reduced variance, it is seen that such processes exhibit nonclassical properties. The conditions for these processes to be treated as conditional Poisson processes are examined. Numerical simulations illustrate part of the theoretical calculations.

Statistical inference based on latent ability estimates

NARCIS (Netherlands)

Hoijtink, H.J.A.; Boomsma, A.

The quality of approximations to first and second order moments (e.g., statistics like means, variances, regression coefficients) based on latent ability estimates is being discussed. The ability estimates are obtained using either the Rasch, oi the two-parameter logistic model. Straightforward use

Statistical Power Analysis with Missing Data A Structural Equation Modeling Approach

CERN Document Server

Davey, Adam

2009-01-01

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research.  Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling.  It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types

Statistical methods for mechanistic model validation: Salt Repository Project

International Nuclear Information System (INIS)

Eggett, D.L.

1988-07-01

As part of the Department of Energy's Salt Repository Program, Pacific Northwest Laboratory (PNL) is studying the emplacement of nuclear waste containers in a salt repository. One objective of the SRP program is to develop an overall waste package component model which adequately describes such phenomena as container corrosion, waste form leaching, spent fuel degradation, etc., which are possible in the salt repository environment. The form of this model will be proposed, based on scientific principles and relevant salt repository conditions with supporting data. The model will be used to predict the future characteristics of the near field environment. This involves several different submodels such as the amount of time it takes a brine solution to contact a canister in the repository, how long it takes a canister to corrode and expose its contents to the brine, the leach rate of the contents of the canister, etc. These submodels are often tested in a laboratory and should be statistically validated (in this context, validate means to demonstrate that the model adequately describes the data) before they can be incorporated into the waste package component model. This report describes statistical methods for validating these models. 13 refs., 1 fig., 3 tabs

A Noise Robust Statistical Texture Model

DEFF Research Database (Denmark)

Hilger, Klaus Baggesen; Stegmann, Mikkel Bille; Larsen, Rasmus

2002-01-01

Appearance Models segmentation framework. This is accomplished by augmenting the model with an estimate of the covariance of the noise present in the training data. This results in a more compact model maximising the signal-to-noise ratio, thus favouring subspaces rich on signal, but low on noise......This paper presents a novel approach to the problem of obtaining a low dimensional representation of texture (pixel intensity) variation present in a training set after alignment using a Generalised Procrustes analysis.We extend the conventional analysis of training textures in the Active...

[The research protocol VI: How to choose the appropriate statistical test. Inferential statistics].

Science.gov (United States)

Flores-Ruiz, Eric; Miranda-Novales, María Guadalupe; Villasís-Keever, Miguel Ángel

2017-01-01

The statistical analysis can be divided in two main components: descriptive analysis and inferential analysis. An inference is to elaborate conclusions from the tests performed with the data obtained from a sample of a population. Statistical tests are used in order to establish the probability that a conclusion obtained from a sample is applicable to the population from which it was obtained. However, choosing the appropriate statistical test in general poses a challenge for novice researchers. To choose the statistical test it is necessary to take into account three aspects: the research design, the number of measurements and the scale of measurement of the variables. Statistical tests are divided into two sets, parametric and nonparametric. Parametric tests can only be used if the data show a normal distribution. Choosing the right statistical test will make it easier for readers to understand and apply the results.

The research protocol VI: How to choose the appropriate statistical test. Inferential statistics

Directory of Open Access Journals (Sweden)

Eric Flores-Ruiz

2017-10-01

Full Text Available The statistical analysis can be divided in two main components: descriptive analysis and inferential analysis. An inference is to elaborate conclusions from the tests performed with the data obtained from a sample of a population. Statistical tests are used in order to establish the probability that a conclusion obtained from a sample is applicable to the population from which it was obtained. However, choosing the appropriate statistical test in general poses a challenge for novice researchers. To choose the statistical test it is necessary to take into account three aspects: the research design, the number of measurements and the scale of measurement of the variables. Statistical tests are divided into two sets, parametric and nonparametric. Parametric tests can only be used if the data show a normal distribution. Choosing the right statistical test will make it easier for readers to understand and apply the results.

Statistical Modeling of Energy Production by Photovoltaic Farms

Czech Academy of Sciences Publication Activity Database

Brabec, Marek; Pelikán, Emil; Krč, Pavel; Eben, Kryštof; Musílek, P.

2011-01-01

Roč. 5, č. 9 (2011), s. 785-793 ISSN 1934-8975 Grant - others:GA AV ČR(CZ) M100300904 Institutional research plan: CEZ:AV0Z10300504 Keywords : electrical energy * solar energy * numerical weather prediction model * nonparametric regression * beta regression Subject RIV: BB - Applied Statistics, Operational Research

Statistical analysis of probabilistic models of software product lines with quantitative constraints

DEFF Research Database (Denmark)

Beek, M.H. ter; Legay, A.; Lluch Lafuente, Alberto

2015-01-01

We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra...... of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study....

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

International Nuclear Information System (INIS)

Weathers, J.B.; Luck, R.; Weathers, J.W.

2009-01-01

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

Energy Technology Data Exchange (ETDEWEB)

Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com

2009-11-15

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

Atmospheric statistical dynamic models. Model performance: the Lawrence Livermore Laboratoy Zonal Atmospheric Model

International Nuclear Information System (INIS)

Potter, G.L.; Ellsaesser, H.W.; MacCracken, M.C.; Luther, F.M.

1978-06-01

Results from the zonal model indicate quite reasonable agreement with observation in terms of the parameters and processes that influence the radiation and energy balance calculations. The model produces zonal statistics similar to those from general circulation models, and has also been shown to produce similar responses in sensitivity studies. Further studies of model performance are planned, including: comparison with July data; comparison of temperature and moisture transport and wind fields for winter and summer months; and a tabulation of atmospheric energetics. Based on these preliminary performance studies, however, it appears that the zonal model can be used in conjunction with more complex models to help unravel the problems of understanding the processes governing present climate and climate change. As can be seen in the subsequent paper on model sensitivity studies, in addition to reduced cost of computation, the zonal model facilitates analysis of feedback mechanisms and simplifies analysis of the interactions between processes

Statistical shear lag model - unraveling the size effect in hierarchical composites.

Science.gov (United States)

Wei, Xiaoding; Filleter, Tobin; Espinosa, Horacio D

2015-05-01

Numerous experimental and computational studies have established that the hierarchical structures encountered in natural materials, such as the brick-and-mortar structure observed in sea shells, are essential for achieving defect tolerance. Due to this hierarchy, the mechanical properties of natural materials have a different size dependence compared to that of typical engineered materials. This study aimed to explore size effects on the strength of bio-inspired staggered hierarchical composites and to define the influence of the geometry of constituents in their outstanding defect tolerance capability. A statistical shear lag model is derived by extending the classical shear lag model to account for the statistics of the constituents' strength. A general solution emerges from rigorous mathematical derivations, unifying the various empirical formulations for the fundamental link length used in previous statistical models. The model shows that the staggered arrangement of constituents grants composites a unique size effect on mechanical strength in contrast to homogenous continuous materials. The model is applied to hierarchical yarns consisting of double-walled carbon nanotube bundles to assess its predictive capabilities for novel synthetic materials. Interestingly, the model predicts that yarn gauge length does not significantly influence the yarn strength, in close agreement with experimental observations. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Uncertainty propagation for statistical impact prediction of space debris

Science.gov (United States)

Hoogendoorn, R.; Mooij, E.; Geul, J.

2018-01-01

Predictions of the impact time and location of space debris in a decaying trajectory are highly influenced by uncertainties. The traditional Monte Carlo (MC) method can be used to perform accurate statistical impact predictions, but requires a large computational effort. A method is investigated that directly propagates a Probability Density Function (PDF) in time, which has the potential to obtain more accurate results with less computational effort. The decaying trajectory of Delta-K rocket stages was used to test the methods using a six degrees-of-freedom state model. The PDF of the state of the body was propagated in time to obtain impact-time distributions. This Direct PDF Propagation (DPP) method results in a multi-dimensional scattered dataset of the PDF of the state, which is highly challenging to process. No accurate results could be obtained, because of the structure of the DPP data and the high dimensionality. Therefore, the DPP method is less suitable for practical uncontrolled entry problems and the traditional MC method remains superior. Additionally, the MC method was used with two improved uncertainty models to obtain impact-time distributions, which were validated using observations of true impacts. For one of the two uncertainty models, statistically more valid impact-time distributions were obtained than in previous research.

Modelling diversity in building occupant behaviour: a novel statistical approach

DEFF Research Database (Denmark)

Haldi, Frédéric; Calì, Davide; Andersen, Rune Korsholm

2016-01-01

We propose an advanced modelling framework to predict the scope and effects of behavioural diversity regarding building occupant actions on window openings, shading devices and lighting. We develop a statistical approach based on generalised linear mixed models to account for the longitudinal nat...

Solution of the statistical bootstrap with Bose statistics

International Nuclear Information System (INIS)

Engels, J.; Fabricius, K.; Schilling, K.

1977-01-01

A brief and transparent way to introduce Bose statistics into the statistical bootstrap of Hagedorn and Frautschi is presented. The resulting bootstrap equation is solved by a cluster expansion for the grand canonical partition function. The shift of the ultimate temperature due to Bose statistics is determined through an iteration process. We discuss two-particle spectra of the decaying fireball (with given mass) as obtained from its grand microcanonical level density

A Unified Statistical Rain-Attenuation Model for Communication Link Fade Predictions and Optimal Stochastic Fade Control Design Using a Location-Dependent Rain-Statistic Database

Science.gov (United States)

Manning, Robert M.

1990-01-01

A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.

Streamwise evolution of statistical events and the triple correlation in a model wind turbine array

Science.gov (United States)

Viestenz, Kyle; Cal, Raúl Bayoán

2013-11-01

Hot-wire anemometry data, obtained from a wind tunnel experiment containing a 3 × 3 wind turbine array, are used to conditionally average the Reynolds stresses. Nine profiles at the centerline behind the array are analyzed to characterize the turbulent velocity statistics of the wake flow. Quadrant analysis yields statistical events occurring in the wake of the wind farm, where quadrants 2 and 4 produce ejections and sweeps, respectively. A balance between these quadrants is expressed via the ΔSo parameter, which attains a maximum value at the bottom tip and changes sign near the top tip of the rotor. These are then associated to the triple correlation term present in the turbulent kinetic energy equation of the fluctuations. The development of these various quantities is assessed in light of wake remediation, energy transport and possess significance in closure models. National Science Foundation: ECCS-1032647.

Effect of model choice and sample size on statistical tolerance limits

International Nuclear Information System (INIS)

Duran, B.S.; Campbell, K.

1980-03-01

Statistical tolerance limits are estimates of large (or small) quantiles of a distribution, quantities which are very sensitive to the shape of the tail of the distribution. The exact nature of this tail behavior cannot be ascertained brom small samples, so statistical tolerance limits are frequently computed using a statistical model chosen on the basis of theoretical considerations or prior experience with similar populations. This report illustrates the effects of such choices on the computations

Statistical properties of the nuclear shell-model Hamiltonian

International Nuclear Information System (INIS)

Dias, H.; Hussein, M.S.; Oliveira, N.A. de

1986-01-01

The statistical properties of realistic nuclear shell-model Hamiltonian are investigated in sd-shell nuclei. The probability distribution of the basic-vector amplitude is calculated and compared with the Porter-Thomas distribution. Relevance of the results to the calculation of the giant resonance mixing parameter is pointed out. (Author) [pt

On the statistical comparison of climate model output and climate data

International Nuclear Information System (INIS)

Solow, A.R.

1991-01-01

Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question. 9 refs

On the statistical comparison of climate model output and climate data

International Nuclear Information System (INIS)

Solow, A.R.

1990-01-01

Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question

Statistical inference for the lifetime performance index based on generalised order statistics from exponential distribution

Science.gov (United States)

Vali Ahmadi, Mohammad; Doostparast, Mahdi; Ahmadi, Jafar

2015-04-01

In manufacturing industries, the lifetime of an item is usually characterised by a random variable X and considered to be satisfactory if X exceeds a given lower lifetime limit L. The probability of a satisfactory item is then ηL := P(X ≥ L), called conforming rate. In industrial companies, however, the lifetime performance index, proposed by Montgomery and denoted by CL, is widely used as a process capability index instead of the conforming rate. Assuming a parametric model for the random variable X, we show that there is a connection between the conforming rate and the lifetime performance index. Consequently, the statistical inferences about ηL and CL are equivalent. Hence, we restrict ourselves to statistical inference for CL based on generalised order statistics, which contains several ordered data models such as usual order statistics, progressively Type-II censored data and records. Various point and interval estimators for the parameter CL are obtained and optimal critical regions for the hypothesis testing problems concerning CL are proposed. Finally, two real data-sets on the lifetimes of insulating fluid and ball bearings, due to Nelson (1982) and Caroni (2002), respectively, and a simulated sample are analysed.

Statistical Models to Assess the Health Effects and to Forecast Ground Level Ozone

Czech Academy of Sciences Publication Activity Database

Schlink, U.; Herbath, O.; Richter, M.; Dorling, S.; Nunnari, G.; Cawley, G.; Pelikán, Emil

2006-01-01

Roč. 21, č. 4 (2006), s. 547-558 ISSN 1364-8152 R&D Projects: GA AV ČR 1ET400300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : statistical models * ground level ozone * health effects * logistic model * forecasting * prediction performance * neural network * generalised additive model * integrated assessment Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.992, year: 2006

A statistical approach to optimizing concrete mixture design.

Science.gov (United States)

Ahmad, Shamsad; Alghamdi, Saeid A

2014-01-01

A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (3(3)). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m(3)), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

A Statistical Approach to Optimizing Concrete Mixture Design

Directory of Open Access Journals (Sweden)

Shamsad Ahmad

2014-01-01

Full Text Available A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33. A total of 27 concrete mixtures with three replicates (81 specimens were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48, cementitious materials content (350, 375, and 400 kg/m3, and fine/total aggregate ratio (0.35, 0.40, and 0.45. The experimental data were utilized to carry out analysis of variance (ANOVA and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.

A new simple model for composite fading channels: Second order statistics and channel capacity

KAUST Repository

Yilmaz, Ferkan

2010-09-01

In this paper, we introduce the most general composite fading distribution to model the envelope and the power of the received signal in such fading channels as millimeter wave (60 GHz or above) fading channels and free-space optical channels, which we term extended generalized-K (EGK) composite fading distribution. We obtain the second-order statistics of the received signal envelope characterized by the EGK composite fading distribution. Expressions for probability density function, cumulative distribution function, level crossing rate and average fade duration, moments, amount of fading and average capacity are derived. Numerical and computer simulation examples validate the accuracy of the presented mathematical analysis. © 2010 IEEE.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

Science.gov (United States)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Preliminary results of sup(40)Ca(e,e'c) reaction analysis c p,α, based on statistical model

International Nuclear Information System (INIS)

Herdade, S.B.; Emrich, H.J.

1990-01-01

Statistical model calculations relative to the reactions sup(40)Ca (e,e'p) sup(39)K and sup(40)Ca(e,e'P sub(o)) sup(39)K sup(gs), using a modified version of the program STAPRE are compared with experimental results obtained from coincidence experiments carried out at the Mainz microtron MAMI A. Preliminary results indicate that the statistical decay of a 1 sup(-) level in the sup(40)Ca compound nucleus, at an excitation energy + 20 MeV, to the ground state of the sup(39)K residual nucleus is only about 15% of the total decay, indicating that direct and/or semi-direct mechanisms contribute to the major part of the decay. (author)

On an uncorrelated jet model with Bose-Einstein statistics

International Nuclear Information System (INIS)

Bilic, N.; Dadic, I.; Martinis, M.

1978-01-01

Starting from the density of states of an ideal Bose-Einstein gas, an uncorrelated jet model with Bose-Einstein statistics has been formulated. The transition to continuum is based on the Touschek invariant measure. It has been shown that in this model average multiplicity increases logarithmically with total energy, while the inclusive distribution shows ln s violation of scaling. (author)

Efficient pan-European river flood hazard modelling through a combination of statistical and physical models

NARCIS (Netherlands)

Paprotny, D.; Morales Napoles, O.; Jonkman, S.N.

2017-01-01

Flood hazard is currently being researched on continental and global scales, using models of increasing complexity. In this paper we investigate a different, simplified approach, which combines statistical and physical models in place of conventional rainfall-run-off models to carry out flood

Vortex dynamics and Lagrangian statistics in a model for active turbulence.

Science.gov (United States)

James, Martin; Wilczek, Michael

2018-02-14

Cellular suspensions such as dense bacterial flows exhibit a turbulence-like phase under certain conditions. We study this phenomenon of "active turbulence" statistically by using numerical tools. Following Wensink et al. (Proc. Natl. Acad. Sci. U.S.A. 109, 14308 (2012)), we model active turbulence by means of a generalized Navier-Stokes equation. Two-point velocity statistics of active turbulence, both in the Eulerian and the Lagrangian frame, is explored. We characterize the scale-dependent features of two-point statistics in this system. Furthermore, we extend this statistical study with measurements of vortex dynamics in this system. Our observations suggest that the large-scale statistics of active turbulence is close to Gaussian with sub-Gaussian tails.

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

Science.gov (United States)

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik

Discrete ellipsoidal statistical BGK model and Burnett equations

Science.gov (United States)

Zhang, Yu-Dong; Xu, Ai-Guo; Zhang, Guang-Cai; Chen, Zhi-Hua; Wang, Pei

2018-06-01

A new discrete Boltzmann model, the discrete ellipsoidal statistical Bhatnagar-Gross-Krook (ESBGK) model, is proposed to simulate nonequilibrium compressible flows. Compared with the original discrete BGK model, the discrete ES-BGK has a flexible Prandtl number. For the discrete ES-BGK model in the Burnett level, two kinds of discrete velocity model are introduced and the relations between nonequilibrium quantities and the viscous stress and heat flux in the Burnett level are established. The model is verified via four benchmark tests. In addition, a new idea is introduced to recover the actual distribution function through the macroscopic quantities and their space derivatives. The recovery scheme works not only for discrete Boltzmann simulation but also for hydrodynamic ones, for example, those based on the Navier-Stokes or the Burnett equations.

Eigenfunction statistics for Anderson model with Hölder continuous ...

Indian Academy of Sciences (India)

The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India ... Anderson model; Hölder continuous measure; Poisson statistics. ...... [4] Combes J-M, Hislop P D and Klopp F, An optimal Wegner estimate and its application to.

A BRDF statistical model applying to space target materials modeling

Science.gov (United States)

Liu, Chenghao; Li, Zhi; Xu, Can; Tian, Qichen

2017-10-01

In order to solve the problem of poor effect in modeling the large density BRDF measured data with five-parameter semi-empirical model, a refined statistical model of BRDF which is suitable for multi-class space target material modeling were proposed. The refined model improved the Torrance-Sparrow model while having the modeling advantages of five-parameter model. Compared with the existing empirical model, the model contains six simple parameters, which can approximate the roughness distribution of the material surface, can approximate the intensity of the Fresnel reflectance phenomenon and the attenuation of the reflected light's brightness with the azimuth angle changes. The model is able to achieve parameter inversion quickly with no extra loss of accuracy. The genetic algorithm was used to invert the parameters of 11 different samples in the space target commonly used materials, and the fitting errors of all materials were below 6%, which were much lower than those of five-parameter model. The effect of the refined model is verified by comparing the fitting results of the three samples at different incident zenith angles in 0° azimuth angle. Finally, the three-dimensional modeling visualizations of these samples in the upper hemisphere space was given, in which the strength of the optical scattering of different materials could be clearly shown. It proved the good describing ability of the refined model at the material characterization as well.

A New Form of Nondestructive Strength-Estimating Statistical Models Accounting for Uncertainty of Model and Aging Effect of Concrete

International Nuclear Information System (INIS)

Hong, Kee Jeung; Kim, Jee Sang

2009-01-01

As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.

Spatio-temporal statistical models with applications to atmospheric processes

International Nuclear Information System (INIS)

Wikle, C.K.

1996-01-01

This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model

Interpretation of the results of statistical measurements. [search for basic probability model

Science.gov (United States)

Olshevskiy, V. V.

1973-01-01

For random processes, the calculated probability characteristic, and the measured statistical estimate are used in a quality functional, which defines the difference between the two functions. Based on the assumption that the statistical measurement procedure is organized so that the parameters for a selected model are optimized, it is shown that the interpretation of experimental research is a search for a basic probability model.

An improved mixing model providing joint statistics of scalar and scalar dissipation

Energy Technology Data Exchange (ETDEWEB)

Meyer, Daniel W. [Department of Energy Resources Engineering, Stanford University, Stanford, CA (United States); Jenny, Patrick [Institute of Fluid Dynamics, ETH Zurich (Switzerland)

2008-11-15

For the calculation of nonpremixed turbulent flames with thin reaction zones the joint probability density function (PDF) of the mixture fraction and its dissipation rate plays an important role. The corresponding PDF transport equation involves a mixing model for the closure of the molecular mixing term. Here, the parameterized scalar profile (PSP) mixing model is extended to provide the required joint statistics. Model predictions are validated using direct numerical simulation (DNS) data of a passive scalar mixing in a statistically homogeneous turbulent flow. Comparisons between the DNS and the model predictions are provided, which involve different initial scalar-field lengthscales. (author)

Statistics of excitations in the electron glass model

Science.gov (United States)

Palassini, Matteo

2011-03-01

We study the statistics of elementary excitations in the classical electron glass model of localized electrons interacting via the unscreened Coulomb interaction in the presence of disorder. We reconsider the long-standing puzzle of the exponential suppression of the single-particle density of states near the Fermi level, by measuring accurately the density of states of charged and electron-hole pair excitations via finite temperature Monte Carlo simulation and zero-temperature relaxation. We also investigate the statistics of large charge rearrangements after a perturbation of the system, which may shed some light on the slow relaxation and glassy phenomena recently observed in a variety of Anderson insulators. In collaboration with Martin Goethe.

Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

Directory of Open Access Journals (Sweden)

Luciano Pivoto Specht

2007-03-01

Full Text Available It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.

ARSENIC CONTAMINATION IN GROUNDWATER: A STATISTICAL MODELING

OpenAIRE

Palas Roy; Naba Kumar Mondal; Biswajit Das; Kousik Das

2013-01-01

High arsenic in natural groundwater in most of the tubewells of the Purbasthali- Block II area of Burdwan district (W.B, India) has recently been focused as a serious environmental concern. This paper is intending to illustrate the statistical modeling of the arsenic contaminated groundwater to identify the interrelation of that arsenic contain with other participating groundwater parameters so that the arsenic contamination level can easily be predicted by analyzing only such parameters. Mul...

Improved air ventilation rate estimation based on a statistical model

International Nuclear Information System (INIS)

Brabec, M.; Jilek, K.

2004-01-01

A new approach to air ventilation rate estimation from CO measurement data is presented. The approach is based on a state-space dynamic statistical model, allowing for quick and efficient estimation. Underlying computations are based on Kalman filtering, whose practical software implementation is rather easy. The key property is the flexibility of the model, allowing various artificial regimens of CO level manipulation to be treated. The model is semi-parametric in nature and can efficiently handle time-varying ventilation rate. This is a major advantage, compared to some of the methods which are currently in practical use. After a formal introduction of the statistical model, its performance is demonstrated on real data from routine measurements. It is shown how the approach can be utilized in a more complex situation of major practical relevance, when time-varying air ventilation rate and radon entry rate are to be estimated simultaneously from concurrent radon and CO measurements

Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

Science.gov (United States)

Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

2017-11-01

Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

Appplication of statistical mechanical methods to the modeling of social networks

Science.gov (United States)

Strathman, Anthony Robert

With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

Science.gov (United States)

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

Energy Technology Data Exchange (ETDEWEB)

Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

2012-03-15

Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

International Nuclear Information System (INIS)

Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

2012-01-01

Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

Statistical 3D damage accumulation model for ion implant simulators

CERN Document Server

Hernandez-Mangas, J M; Enriquez, L E; Bailon, L; Barbolla, J; Jaraiz, M

2003-01-01

A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided.

Statistical 3D damage accumulation model for ion implant simulators

International Nuclear Information System (INIS)

Hernandez-Mangas, J.M.; Lazaro, J.; Enriquez, L.; Bailon, L.; Barbolla, J.; Jaraiz, M.

2003-01-01

A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided

Statistical modelling of space-time processes with application to wind power

DEFF Research Database (Denmark)

Lenzi, Amanda

. This thesis aims at contributing to the wind power literature by building and evaluating new statistical techniques for producing forecasts at multiple locations and lead times using spatio-temporal information. By exploring the features of a rich portfolio of wind farms in western Denmark, we investigate...... propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial...

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Statistical power of model selection strategies for genome-wide association studies.

Directory of Open Access Journals (Sweden)

Zheyang Wu

2009-07-01

Full Text Available Genome-wide association studies (GWAS aim to identify genetic variants related to diseases by examining the associations between phenotypes and hundreds of thousands of genotyped markers. Because many genes are potentially involved in common diseases and a large number of markers are analyzed, it is crucial to devise an effective strategy to identify truly associated variants that have individual and/or interactive effects, while controlling false positives at the desired level. Although a number of model selection methods have been proposed in the literature, including marginal search, exhaustive search, and forward search, their relative performance has only been evaluated through limited simulations due to the lack of an analytical approach to calculating the power of these methods. This article develops a novel statistical approach for power calculation, derives accurate formulas for the power of different model selection strategies, and then uses the formulas to evaluate and compare these strategies in genetic model spaces. In contrast to previous studies, our theoretical framework allows for random genotypes, correlations among test statistics, and a false-positive control based on GWAS practice. After the accuracy of our analytical results is validated through simulations, they are utilized to systematically evaluate and compare the performance of these strategies in a wide class of genetic models. For a specific genetic model, our results clearly reveal how different factors, such as effect size, allele frequency, and interaction, jointly affect the statistical power of each strategy. An example is provided for the application of our approach to empirical research. The statistical approach used in our derivations is general and can be employed to address the model selection problems in other random predictor settings. We have developed an R package markerSearchPower to implement our formulas, which can be downloaded from the