Risk estimation using probability machines
2014-01-01
Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306
Probability based models for estimation of wildfire risk
Haiganoush Preisler; D. R. Brillinger; R. E. Burgan; John Benoit
2004-01-01
We present a probability-based model for estimating fire risk. Risk is defined using three probabilities: the probability of fire occurrence; the conditional probability of a large fire given ignition; and the unconditional probability of a large fire. The model is based on grouped data at the 1 kmÂ²-day cell level. We fit a spatially and temporally explicit non-...
The quantitative estimation of IT-related risk probabilities.
Herrmann, Andrea
2013-08-01
How well can people estimate IT-related risk? Although estimating risk is a fundamental activity in software management and risk is the basis for many decisions, little is known about how well IT-related risk can be estimated at all. Therefore, we executed a risk estimation experiment with 36 participants. They estimated the probabilities of IT-related risks and we investigated the effect of the following factors on the quality of the risk estimation: the estimator's age, work experience in computing, (self-reported) safety awareness and previous experience with this risk, the absolute value of the risk's probability, and the effect of knowing the estimates of the other participants (see: Delphi method). Our main findings are: risk probabilities are difficult to estimate. Younger and inexperienced estimators were not significantly worse than older and more experienced estimators, but the older and more experienced subjects better used the knowledge gained by knowing the other estimators' results. Persons with higher safety awareness tend to overestimate risk probabilities, but can better estimate ordinal ranks of risk probabilities. Previous own experience with a risk leads to an overestimation of its probability (unlike in other fields like medicine or disasters, where experience with a disease leads to more realistic probability estimates and nonexperience to an underestimation).
Estimating the concordance probability in a survival analysis with a discrete number of risk groups.
Heller, Glenn; Mo, Qianxing
2016-04-01
A clinical risk classification system is an important component of a treatment decision algorithm. A measure used to assess the strength of a risk classification system is discrimination, and when the outcome is survival time, the most commonly applied global measure of discrimination is the concordance probability. The concordance probability represents the pairwise probability of lower patient risk given longer survival time. The c-index and the concordance probability estimate have been used to estimate the concordance probability when patient-specific risk scores are continuous. In the current paper, the concordance probability estimate and an inverse probability censoring weighted c-index are modified to account for discrete risk scores. Simulations are generated to assess the finite sample properties of the concordance probability estimate and the weighted c-index. An application of these measures of discriminatory power to a metastatic prostate cancer risk classification system is examined.
Carr, D.B.; Tolley, H.D.
1982-12-01
This paper investigates procedures for univariate nonparametric estimation of tail probabilities. Extrapolated values for tail probabilities beyond the data are also obtained based on the shape of the density in the tail. Several estimators which use exponential weighting are described. These are compared in a Monte Carlo study to nonweighted estimators, to the empirical cdf, to an integrated kernel, to a Fourier series estimate, to a penalized likelihood estimate and a maximum likelihood estimate. Selected weighted estimators are shown to compare favorably to many of these standard estimators for the sampling distributions investigated.
Fall risk probability estimation based on supervised feature learning using public fall datasets.
Koshmak, Gregory A; Linden, Maria; Loutfi, Amy
2016-08-01
Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection system has increased drastically. However, there is still a lack of universal approach regarding the evaluation of developed algorithms. In the following study we make an attempt to find publicly available fall datasets and analyze similarities among them using supervised learning. After preforming similarity assessment based on multidimensional scaling we indicate the most representative feature vector corresponding to each specific dataset. This vector obtained from a real-life data is subsequently deployed to estimate fall risk probabilities for a statistical fall detection model. Finally, we conclude with some observations regarding the similarity assessment results and provide suggestions towards an efficient approach for evaluation of fall detection studies.
Estimation of the Probability of Labor Force Participation of the AFDC Population-At-Risk
1977-01-01
probability of labor force participation (LFP) of female family heads with dependent children present, the Aid to Families with Dependent Children (AFDC... female and if dependent children were present, which may be viewed as the AFDC pop- ulation-at-risk.l Only those family heads who were in the civilian
Faith, Daniel P
2008-12-01
New species conservation strategies, including the EDGE of Existence (EDGE) program, have expanded threatened species assessments by integrating information about species' phylogenetic distinctiveness. Distinctiveness has been measured through simple scores that assign shared credit among species for evolutionary heritage represented by the deeper phylogenetic branches. A species with a high score combined with a high extinction probability receives high priority for conservation efforts. Simple hypothetical scenarios for phylogenetic trees and extinction probabilities demonstrate how such scoring approaches can provide inefficient priorities for conservation. An existing probabilistic framework derived from the phylogenetic diversity measure (PD) properly captures the idea of shared responsibility for the persistence of evolutionary history. It avoids static scores, takes into account the status of close relatives through their extinction probabilities, and allows for the necessary updating of priorities in light of changes in species threat status. A hypothetical phylogenetic tree illustrates how changes in extinction probabilities of one or more species translate into changes in expected PD. The probabilistic PD framework provided a range of strategies that moved beyond expected PD to better consider worst-case PD losses. In another example, risk aversion gave higher priority to a conservation program that provided a smaller, but less risky, gain in expected PD. The EDGE program could continue to promote a list of top species conservation priorities through application of probabilistic PD and simple estimates of current extinction probability. The list might be a dynamic one, with all the priority scores updated as extinction probabilities change. Results of recent studies suggest that estimation of extinction probabilities derived from the red list criteria linked to changes in species range sizes may provide estimated probabilities for many different species
Kassteele, J van de; Hoogenveen, R T; Engelfriet, P M; Baal, P H M van; Boshuizen, H C
2012-03-15
A problem occurring in chronic disease modeling is the estimation of transition probabilities of moving from one state of a categorical risk factor to another. Transitions could be obtained from a cohort study, but often such data may not be available. However, under the assumption that transitions remain stable over time, age specific cross-sectional prevalence data could be used instead. Problems that then arise are parameter identifiability and the fact that age dependent cross-sectional data are often noisy or are given in age intervals. In this paper we propose a method to estimate so-called net annual transition probabilities from cross-sectional data, including their uncertainties. Net transitions only describe the net inflow or outflow into a certain risk factor state at a certain age. Our approach consists of two steps: first, smooth the data using multinomial P-splines, second, from these data estimate net transition probabilities. This second step can be formulated as a transportation problem, which is solved using the simplex algorithm from linear programming theory. A sensible specification of the cost matrix is crucial to get meaningful results. Uncertainties are assessed by parametric bootstrapping. We illustrate our method using data on body mass index. We conclude that this method provides a flexible way of estimating net transitions and that the use of net transitions has implications for model dynamics, for example when modeling interventions. Copyright © 2011 John Wiley & Sons, Ltd.
Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines
Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.
2011-01-01
Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433
PROBABILITY SURVEYS , CONDITIONAL PROBABILITIES AND ECOLOGICAL RISK ASSESSMENT
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...
PROBABILITY SURVEYS , CONDITIONAL PROBABILITIES AND ECOLOGICAL RISK ASSESSMENT
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...
Probability Surveys, Conditional Probability, and Ecological Risk Assessment
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency’s (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...
Probability Surveys, Conditional Probability, and Ecological Risk Assessment
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency’s (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...
Probability surveys, conditional probability, and ecological risk assessment.
Paul, John F; Munns, Wayne R
2011-06-01
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over broad geographic areas. Under certain conditions (including appropriate stratification of the sampled population, sufficient density of samples, and sufficient range of exposure levels paired with concurrent response values), this empirical approach provides estimates of risk using extant field-derived monitoring data. The monitoring data were used to prescribe the exposure field and to model the exposure-response relationship. We illustrate this approach by estimating risks to benthic communities from low dissolved oxygen (DO) in freshwater streams of the mid-Atlantic region and in estuaries of the Virginian Biogeographical Province of the United States. In both cases, the estimates of risk are consistent with the U.S. EPA's ambient water quality criteria for DO. Copyright © 2011 SETAC.
Annambhotla, Pallavi D; Gurbaxani, Brian M; Kuehnert, Matthew J; Basavaraju, Sridhar V
2017-04-01
In 2013, guidelines were released for reducing the risk of viral bloodborne pathogen transmission through organ transplantation. Eleven criteria were described that result in a donor being designated at increased infectious risk. Human immunodeficiency virus (HIV) and hepatitis C virus (HCV) transmission risk from an increased-risk donor (IRD), despite negative nucleic acid testing (NAT), likely varies based on behavior type and timing. We developed a Monte Carlo risk model to quantify probability of HIV among IRDs. The model included NAT performance, viral load dynamics, and per-act risk of acquiring HIV by each behavior. The model also quantifies the probability of HCV among IRDs by non-medical intravenous drug use (IVDU). Highest risk is among donors with history of unprotected, receptive anal male-to-male intercourse with partner of unknown HIV status (MSM), followed by sex with an HIV-infected partner, IVDU, and sex with a commercial sex worker. With NAT screening, the estimated risk of undetected HIV remains small even at 1 day following a risk behavior. The estimated risk for HCV transmission through IVDU is likewise small and decreases quicker with time owing to the faster viral growth dynamics of HCV compared with HIV. These findings may allow for improved organ allocation, utilization, and recipient informed consent. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Point estimates for probability moments
Rosenblueth, Emilio
1975-01-01
Given a well-behaved real function Y of a real random variable X and the first two or three moments of X, expressions are derived for the moments of Y as linear combinations of powers of the point estimates y(x+) and y(x-), where x+ and x- are specific values of X. Higher-order approximations and approximations for discontinuous Y using more point estimates are also given. Second-moment approximations are generalized to the case when Y is a function of several variables. PMID:16578731
Class probability estimation for medical studies.
Simon, Richard
2014-07-01
I provide a commentary on two papers "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. Those papers provide an up-to-date review of some popular machine learning methods for class probability estimation and compare those methods to logistic regression modeling in real and simulated datasets.
Optimizing Probability of Detection Point Estimate Demonstration
NASA Technical Reports Server (NTRS)
Koshti, Ajay M.
2017-01-01
Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-18231and associated mh18232POD software gives most common methods of POD analysis. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. The paper provides discussion on optimizing probability of detection (POD) demonstration experiments using Point Estimate Method. POD Point estimate method is used by NASA for qualifying special NDE procedures. The point estimate method uses binomial distribution for probability density. Normally, a set of 29 flaws of same size within some tolerance are used in the demonstration. The optimization is performed to provide acceptable value for probability of passing demonstration (PPD) and achieving acceptable value for probability of false (POF) calls while keeping the flaw sizes in the set as small as possible.
Estimating flood exceedance probabilities in estuarine regions
NASA Astrophysics Data System (ADS)
Westra, Seth; Leonard, Michael
2016-04-01
Flood events in estuarine regions can arise from the interaction of extreme rainfall and storm surge. Determining flood level exceedance probabilities in these regions is complicated by the dependence of these processes for extreme events. A comprehensive study of tide and rainfall gauges along the Australian coastline was conducted to determine the dependence of these extremes using a bivariate logistic threshold-excess model. The dependence strength is shown to vary as a function of distance over many hundreds of kilometres indicating that the dependence arises due to synoptic scale meteorological forcings. It is also shown to vary as a function of storm burst duration, time lag between the extreme rainfall and the storm surge event. The dependence estimates are then used with a bivariate design variable method to determine flood risk in estuarine regions for a number of case studies. Aspects of the method demonstrated in the case studies include, the resolution and range of the hydraulic response table, fitting of probability distributions, computational efficiency, uncertainty, potential variation in marginal distributions due to climate change, and application to two dimensional output from hydraulic models. Case studies are located on the Swan River (Western Australia), Nambucca River and Hawkesbury Nepean River (New South Wales).
Calibrating random forests for probability estimation.
Dankowski, Theresa; Ziegler, Andreas
2016-09-30
Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Crash probability estimation via quantifying driver hazard perception.
Li, Yang; Zheng, Yang; Wang, Jianqiang; Kodaka, Kenji; Li, Keqiang
2017-06-05
Crash probability estimation is an important method to predict the potential reduction of crash probability contributed by forward collision avoidance technologies (FCATs). In this study, we propose a practical approach to estimate crash probability, which combines a field operational test and numerical simulations of a typical rear-end crash model. To consider driver hazard perception characteristics, we define a novel hazard perception measure, called as driver risk response time, by considering both time-to-collision (TTC) and driver braking response to impending collision risk in a near-crash scenario. Also, we establish a driving database under mixed Chinese traffic conditions based on a CMBS (Collision Mitigation Braking Systems)-equipped vehicle. Applying the crash probability estimation in this database, we estimate the potential decrease in crash probability owing to use of CMBS. A comparison of the results with CMBS on and off shows a 13.7% reduction of crash probability in a typical rear-end near-crash scenario with a one-second delay of driver's braking response. These results indicate that CMBS is positive in collision prevention, especially in the case of inattentive drivers or ole drivers. The proposed crash probability estimation offers a practical way for evaluating the safety benefits in the design and testing of FCATs. Copyright © 2017 Elsevier Ltd. All rights reserved.
Assessing semantic coherence in conditional probability estimates
Fisher, Christopher R.
2013-01-01
Semantic coherence is a higher-order coherence benchmark that assesses whether a constellation of estimates—P(A), P(B), P(B | A), and P(A | B)—maps onto the relationship between sets implied by the description of a given problem. We present an automated method for evaluating semantic coherence in conditional probability estimates that efficiently reduces a large problem space into five meaningful patterns: identical sets, subsets, mutually exclusive sets, overlapping sets, and independent sets. It also identifies three theoretically interesting nonfallacious errors. We discuss unique issues in evaluating semantic coherence in conditional probabilities that are not present in joint probability judgments, such as errors resulting from dividing by zero and the use of a tolerance parameter to manage rounding errors. A spreadsheet implementing the methods described above can be downloaded as a supplement from www.springerlink.com. PMID:21512870
Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation
NASA Astrophysics Data System (ADS)
Nathan, Rory; Jordan, Phillip; Scorah, Matthew; Lang, Simon; Kuczera, George; Schaefer, Melvin; Weinmann, Erwin
2016-12-01
If risk-based criteria are used in the design of high hazard structures (such as dam spillways and nuclear power stations), then it is necessary to estimate the annual exceedance probability (AEP) of extreme rainfalls up to and including the Probable Maximum Precipitation (PMP). This paper describes the development and application of two largely independent methods to estimate the frequencies of such extreme rainfalls. One method is based on stochastic storm transposition (SST), which combines the ;arrival; and ;transposition; probabilities of an extreme storm using the total probability theorem. The second method, based on ;stochastic storm regression; (SSR), combines frequency curves of point rainfalls with regression estimates of local and transposed areal rainfalls; rainfall maxima are generated by stochastically sampling the independent variates, where the required exceedance probabilities are obtained using the total probability theorem. The methods are applied to two large catchments (with areas of 3550 km2 and 15,280 km2) located in inland southern Australia. Both methods were found to provide similar estimates of the frequency of extreme areal rainfalls for the two study catchments. The best estimates of the AEP of the PMP for the smaller and larger of the catchments were found to be 10-7 and 10-6, respectively, but the uncertainty of these estimates spans one to two orders of magnitude. Additionally, the SST method was applied to a range of locations within a meteorologically homogenous region to investigate the nature of the relationship between the AEP of PMP and catchment area.
Conflict Probability Estimation for Free Flight
NASA Technical Reports Server (NTRS)
Paielli, Russell A.; Erzberger, Heinz
1996-01-01
The safety and efficiency of free flight will benefit from automated conflict prediction and resolution advisories. Conflict prediction is based on trajectory prediction and is less certain the farther in advance the prediction, however. An estimate is therefore needed of the probability that a conflict will occur, given a pair of predicted trajectories and their levels of uncertainty. A method is developed in this paper to estimate that conflict probability. The trajectory prediction errors are modeled as normally distributed, and the two error covariances for an aircraft pair are combined into a single equivalent covariance of the relative position. A coordinate transformation is then used to derive an analytical solution. Numerical examples and Monte Carlo validation are presented.
Variance comparisons for unbiased estimators of probability of correct classification
NASA Technical Reports Server (NTRS)
Moore, D. S.; Landgrebe, D. A.; Whitsitt, S. J.
1976-01-01
Variance relationships among certain count estimators and posterior probability estimators of probability of correct classification are investigated. An estimator using posterior probabilities is presented for use in stratified sampling designs. A test case involving three normal classes is examined.
Model estimates hurricane wind speed probabilities
NASA Astrophysics Data System (ADS)
Mumane, Richard J.; Barton, Chris; Collins, Eric; Donnelly, Jeffrey; Eisner, James; Emanuel, Kerry; Ginis, Isaac; Howard, Susan; Landsea, Chris; Liu, Kam-biu; Malmquist, David; McKay, Megan; Michaels, Anthony; Nelson, Norm; O Brien, James; Scott, David; Webb, Thompson, III
In the United States, intense hurricanes (category 3, 4, and 5 on the Saffir/Simpson scale) with winds greater than 50 m s -1 have caused more damage than any other natural disaster [Pielke and Pielke, 1997]. Accurate estimates of wind speed exceedance probabilities (WSEP) due to intense hurricanes are therefore of great interest to (re)insurers, emergency planners, government officials, and populations in vulnerable coastal areas.The historical record of U.S. hurricane landfall is relatively complete only from about 1900, and most model estimates of WSEP are derived from this record. During the 1899-1998 period, only two category-5 and 16 category-4 hurricanes made landfall in the United States. The historical record therefore provides only a limited sample of the most intense hurricanes.
Actual and actuarial probabilities of competing risks: apples and lemons.
Grunkemeier, Gary L; Jin, Ruyun; Eijkemans, Marinus J C; Takkenberg, Johanna J M
2007-05-01
The probability of a type of failure that is not inevitable, but can be precluded by other events such as death, is given by the cumulative incidence function. In cardiac research articles, it has become known as the actual probability, in contrast to the actuarial methods of estimation, usually implemented by the Kaplan-Meier (KM) estimate. Unlike cumulative incidence, KM attempts to predict what the latent failure probability would be if death were eliminated. To do this, the KM method assumes that the risk of dying and the risk of failure are independent. But this assumption is not true for many cardiac applications in which the risks of failure and death are negatively correlated (ie, patients with a higher risk of dying have a lower risk of failure, and patients with a lower risk of death have a higher risk of failure, which is a condition called informative censoring). Recent editorials in two cardiac journals have promoted the use of the KM method (actuarial estimate) for competing risk events (specifically for heart valve performance) and criticized the use of the cumulative incidence (actual) estimates. This report has two aims: to explain the difference between these two estimates and to show why the KM is generally not appropriate. In the process we will rely on alternative representations of the KM estimator (using redistribution to the right and inverse probability weighting) to explain the difference between the two estimates and to show how it may be possible to adjust KM to overcome the informative censoring.
Holmberg, Christine; Waters, Erika A; Whitehouse, Katie; Daly, Mary; McCaskill-Stevens, Worta
2015-11-01
Decision-making experts emphasize that understanding and using probabilistic information are important for making informed decisions about medical treatments involving complex risk-benefit tradeoffs. Yet empirical research demonstrates that individuals may not use probabilities when making decisions. To explore decision making and the use of probabilities for decision making from the perspective of women who were risk-eligible to enroll in the Study of Tamoxifen and Raloxifene (STAR). We conducted narrative interviews with 20 women who agreed to participate in STAR and 20 women who declined. The project was based on a narrative approach. Analysis included the development of summaries of each narrative, and thematic analysis with developing a coding scheme inductively to code all transcripts to identify emerging themes. Interviewees explained and embedded their STAR decisions within experiences encountered throughout their lives. Such lived experiences included but were not limited to breast cancer family history, a personal history of breast biopsies, and experiences or assumptions about taking tamoxifen or medicines more generally. Women's explanations of their decisions about participating in a breast cancer chemoprevention trial were more complex than decision strategies that rely solely on a quantitative risk-benefit analysis of probabilities derived from populations In addition to precise risk information, clinicians and risk communicators should recognize the importance and legitimacy of lived experience in individual decision making. © The Author(s) 2015.
Probability density estimation using artificial neural networks
NASA Astrophysics Data System (ADS)
Likas, Aristidis
2001-04-01
We present an approach for the estimation of probability density functions (pdf) given a set of observations. It is based on the use of feedforward multilayer neural networks with sigmoid hidden units. The particular characteristic of the method is that the output of the network is not a pdf, therefore, the computation of the network's integral is required. When this integral cannot be performed analytically, one is forced to resort to numerical integration techniques. It turns out that this is quite tricky when coupled with subsequent training procedures. Several modifications of the original approach (Modha and Fainman, 1994) are proposed, most of them related to the numerical treatment of the integral and the employment of a preprocessing phase where the network parameters are initialized using supervised training. Experimental results using several test problems indicate that the proposed method is very effective and in most cases superior to the method of Gaussian mixtures.
Estimating the probability of rare events: addressing zero failure data.
Quigley, John; Revie, Matthew
2011-07-01
Traditional statistical procedures for estimating the probability of an event result in an estimate of zero when no events are realized. Alternative inferential procedures have been proposed for the situation where zero events have been realized but often these are ad hoc, relying on selecting methods dependent on the data that have been realized. Such data-dependent inference decisions violate fundamental statistical principles, resulting in estimation procedures whose benefits are difficult to assess. In this article, we propose estimating the probability of an event occurring through minimax inference on the probability that future samples of equal size realize no more events than that in the data on which the inference is based. Although motivated by inference on rare events, the method is not restricted to zero event data and closely approximates the maximum likelihood estimate (MLE) for nonzero data. The use of the minimax procedure provides a risk adverse inferential procedure where there are no events realized. A comparison is made with the MLE and regions of the underlying probability are identified where this approach is superior. Moreover, a comparison is made with three standard approaches to supporting inference where no event data are realized, which we argue are unduly pessimistic. We show that for situations of zero events the estimator can be simply approximated with 1/2.5n, where n is the number of trials.
Estimating Prior Model Probabilities Using an Entropy Principle
NASA Astrophysics Data System (ADS)
Ye, M.; Meyer, P. D.; Neuman, S. P.; Pohlmann, K.
2004-12-01
Considering conceptual model uncertainty is an important process in environmental uncertainty/risk analyses. Bayesian Model Averaging (BMA) (Hoeting et al., 1999) and its Maximum Likelihood version, MLBMA, (Neuman, 2003) jointly assess predictive uncertainty of competing alternative models to avoid bias and underestimation of uncertainty caused by relying on one single model. These methods provide posterior distribution (or, equivalently, leading moments) of quantities of interests for decision-making. One important step of these methods is to specify prior probabilities of alternative models for the calculation of posterior model probabilities. This problem, however, has not been satisfactorily resolved and equally likely prior model probabilities are usually accepted as a neutral choice. Ye et al. (2004) have shown that whereas using equally likely prior model probabilities has led to acceptable geostatistical estimates of log air permeability data from fractured unsaturated tuff at the Apache Leap Research Site (ALRS) in Arizona, identifying more accurate prior probabilities can improve these estimates. In this paper we present a new methodology to evaluate prior model probabilities by maximizing Shannon's entropy with restrictions postulated a priori based on model plausibility relationships. It yields optimum prior model probabilities conditional on prior information used to postulate the restrictions. The restrictions and corresponding prior probabilities can be modified as more information becomes available. The proposed method is relatively easy to use in practice as it is generally less difficult for experts to postulate relationships between models than to specify numerical prior model probability values. Log score, mean square prediction error (MSPE) and mean absolute predictive error (MAPE) criteria consistently show that applying our new method to the ALRS data reduces geostatistical estimation errors provided relationships between models are
Probabilities and health risks: a qualitative approach.
Heyman, B; Henriksen, M; Maughan, K
1998-11-01
Health risks, defined in terms of the probability that an individual will suffer a particular type of adverse health event within a given time period, can be understood as referencing either natural entities or complex patterns of belief which incorporate the observer's values and knowledge, the position adopted in the present paper. The subjectivity inherent in judgements about adversity and time frames can be easily recognised, but social scientists have tended to accept uncritically the objectivity of probability. Most commonly in health risk analysis, the term probability refers to rates established by induction, and so requires the definition of a numerator and denominator. Depending upon their specification, many probabilities may be reasonably postulated for the same event, and individuals may change their risks by deciding to seek or avoid information. These apparent absurdities can be understood if probability is conceptualised as the projection of expectation onto the external world. Probabilities based on induction from observed frequencies provide glimpses of the future at the price of acceptance of the simplifying heuristic that statistics derived from aggregate groups can be validly attributed to individuals within them. The paper illustrates four implications of this conceptualisation of probability with qualitative data from a variety of sources, particularly a study of genetic counselling for pregnant women in a U.K. hospital. Firstly, the official selection of a specific probability heuristic reflects organisational constraints and values as well as predictive optimisation. Secondly, professionals and service users must work to maintain the facticity of an established heuristic in the face of alternatives. Thirdly, individuals, both lay and professional, manage probabilistic information in ways which support their strategic objectives. Fourthly, predictively sub-optimum schema, for example the idea of AIDS as a gay plague, may be selected because
PROBABILITY SURVEYS, CONDITIONAL PROBABILITIES, AND ECOLOGICAL RISK ASSESSMENT
We show that probability-based environmental resource monitoring programs, such as U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Asscssment Program EMAP) can be analyzed with a conditional probability analysis (CPA) to conduct quantitative probabi...
Estimating the Probability of Negative Events
ERIC Educational Resources Information Center
Harris, Adam J. L.; Corner, Adam; Hahn, Ulrike
2009-01-01
How well we are attuned to the statistics of our environment is a fundamental question in understanding human behaviour. It seems particularly important to be able to provide accurate assessments of the probability with which negative events occur so as to guide rational choice of preventative actions. One question that arises here is whether or…
Nakai, Michikazu; Miyamoto, Yoshihiro; Higashiyama, Aya; Murakami, Yoshitaka; Nishimura, Kunihiro; Yatsuya, Hiroshi; Saitoh, Shigeyuki; Sakata, Kiyomi; Iso, Hiroyasu; Miura, Katsuyuki; Ueshima, Hirotsugu; Okamura, Tomonori
2016-01-01
In Japan Atherosclerosis Society guidelines for the prevention of atherosclerotic cardiovascular diseases 2012 (JAS2012), NIPPON DATA80 risk assessment chart (ND80RAC) was adopted to estimate the 10-year probability of coronary artery disease (CAD) mortality. However, there was no comparison between the estimated mortality calculated by ND80RAC and actual mortality in external populations. Accordingly, we used the large pooled database of cohorts in Japan, EPOCH-JAPAN, as an external population. The participants of EPOCH-JAPAN without a history of cardiovascular disease (15,091 men and 18,589 women aged 40-74 years) were analyzed based on sex. The probability of a 10-year risk of CAD/stroke mortality was estimated by ND80RAC. The participants were divided into both decile of their estimated mortality and three categories according to JAS2012. The calibration between the mean estimated mortality and the actual mortality was performed by the Hosmer and Lemeshow (H-L) test. In both sexes, the estimated CAD mortality was higher than the actual mortality, particularly in higher deciles of estimated mortality, and the estimated stroke mortality was almost concordant with the actual mortality in low/moderate deciles of estimated mortality. As for the categories according to JAS2012, the estimated CAD mortality was higher than the actual mortality in both sexes; actual mortality in Category III was lower than that in Category II in women. However, it increased in the ascending order of category when we excluded the presence of diabetes from Category III. The estimated CAD mortality by ND80RAC tended to be higher than the actual mortality in the population in which the baseline survey was more recently performed.
Probability model for estimating colorectal polyp progression rates.
Gopalappa, Chaitra; Aydogan-Cremaschi, Selen; Das, Tapas K; Orcun, Seza
2011-03-01
According to the American Cancer Society, colorectal cancer (CRC) is the third most common cause of cancer related deaths in the United States. Experts estimate that about 85% of CRCs begin as precancerous polyps, early detection and treatment of which can significantly reduce the risk of CRC. Hence, it is imperative to develop population-wide intervention strategies for early detection of polyps. Development of such strategies requires precise values of population-specific rates of incidence of polyp and its progression to cancerous stage. There has been a considerable amount of research in recent years on developing screening based CRC intervention strategies. However, these are not supported by population-specific mathematical estimates of progression rates. This paper addresses this need by developing a probability model that estimates polyp progression rates considering race and family history of CRC; note that, it is ethically infeasible to obtain polyp progression rates through clinical trials. We use the estimated rates to simulate the progression of polyps in the population of the State of Indiana, and also the population of a clinical trial conducted in the State of Minnesota, which was obtained from literature. The results from the simulations are used to validate the probability model.
VOLCANIC RISK ASSESSMENT - PROBABILITY AND CONSEQUENCES
G.A. Valentine; F.V. Perry; S. Dartevelle
2005-08-26
Risk is the product of the probability and consequences of an event. Both of these must be based upon sound science that integrates field data, experiments, and modeling, but must also be useful to decision makers who likely do not understand all aspects of the underlying science. We review a decision framework used in many fields such as performance assessment for hazardous and/or radioactive waste disposal sites that can serve to guide the volcanological community towards integrated risk assessment. In this framework the underlying scientific understanding of processes that affect probability and consequences drive the decision-level results, but in turn these results can drive focused research in areas that cause the greatest level of uncertainty at the decision level. We review two examples of the determination of volcanic event probability: (1) probability of a new volcano forming at the proposed Yucca Mountain radioactive waste repository, and (2) probability that a subsurface repository in Japan would be affected by the nearby formation of a new stratovolcano. We also provide examples of work on consequences of explosive eruptions, within the framework mentioned above. These include field-based studies aimed at providing data for ''closure'' of wall rock erosion terms in a conduit flow model, predictions of dynamic pressure and other variables related to damage by pyroclastic flow into underground structures, and vulnerability criteria for structures subjected to conditions of explosive eruption. Process models (e.g., multiphase flow) are important for testing the validity or relative importance of possible scenarios in a volcanic risk assessment. We show how time-dependent multiphase modeling of explosive ''eruption'' of basaltic magma into an open tunnel (drift) at the Yucca Mountain repository provides insight into proposed scenarios that include the development of secondary pathways to the Earth's surface. Addressing volcanic risk within a decision
A comparison of tail probability estimators for flood frequency analysis
NASA Astrophysics Data System (ADS)
Moon, Young-Il; Lall, Upmanu; Bosworth, Ken
1993-11-01
Selected techniques for estimating exceedance frequencies of annual maximum flood events at a gaged site are compared in this paper. Four tail probability estimators proposed by Hill (PT1), Hosking and Wallis (PT2) and by Breiman and Stone (ET and QT), and a variable kernel distribution function estimator (VK-C-AC) were compared for three situations — Gaussian data, skewed data (three-parameter gamma) and Gaussian mixture data. The performance of these estimators was compared with method of moment estimates of tail probabilities, using the Gaussian, Pearson Type III, and extreme value distributions. Since the results of the tail probability estimators (PT1, PT2, ET, QT) varied according to the situation, it is not easy to say which tail probability estimator is the best. However, the performance of the variable kernel estimator was relatively consistent across the estimation situations considered in terms of bias and r.m.s.e.
Estimating probability distributions of solar irradiance
NASA Astrophysics Data System (ADS)
Voskrebenzev, A.; Riechelmann, S.; Bais, A.; Slaper, H.; Seckmeyer, G.
2015-02-01
In the presence of clouds the ability to calculate instantaneous spectral irradiance values is limited by the ability to acquire appropriate input parameters for radiative transfer solvers. However, the knowledge of the statistical characteristics of spectral irradiance as a function of season and time of the day is relevant for solar energy and health applications. For this purpose a method to derive the wavelength dependent probability density functions (PDFs) and its seasonal site variability is presented. In contrast to the UVB range, the derived PDFS at three stations in Europe (Bilthoven, Garmisch-Partenkirchen and Thessaloniki) show only minor wavelength dependence above 315 nm. But there are major differences of the PDFs that are attributed to the site specific cloud climatology at these stations. Furthermore the results suggest that the previously described relationship between air mass and bimodality is the consequence of seasonal cloud variations. For Thessaloniki it is shown that the pyranometer sample spread around the cloudless value is proportional to the secant of the solar zenith angle and therefore scales according to air mass. Cloud amount observations are utilized to associate the local maxima of the multimodal PDFs with rough cloudiness states confirming the already established interpretation of broadband data for spectral data as well. As one application example the likelihood of irradiance enhancements over the clear sky case due to clouds is assessed.
Probability shapes perceptual precision: A study in orientation estimation.
Jabar, Syaheed B; Anderson, Britt
2015-12-01
Probability is known to affect perceptual estimations, but an understanding of mechanisms is lacking. Moving beyond binary classification tasks, we had naive participants report the orientation of briefly viewed gratings where we systematically manipulated contingent probability. Participants rapidly developed faster and more precise estimations for high-probability tilts. The shapes of their error distributions, as indexed by a kurtosis measure, also showed a distortion from Gaussian. This kurtosis metric was robust, capturing probability effects that were graded, contextual, and varying as a function of stimulus orientation. Our data can be understood as a probability-induced reduction in the variability or "shape" of estimation errors, as would be expected if probability affects the perceptual representations. As probability manipulations are an implicit component of many endogenous cuing paradigms, changes at the perceptual level could account for changes in performance that might have traditionally been ascribed to "attention." (c) 2015 APA, all rights reserved).
The Estimation of Tree Posterior Probabilities Using Conditional Clade Probability Distributions
Larget, Bret
2013-01-01
In this article I introduce the idea of conditional independence of separated subtrees as a principle by which to estimate the posterior probability of trees using conditional clade probability distributions rather than simple sample relative frequencies. I describe an algorithm for these calculations and software which implements these ideas. I show that these alternative calculations are very similar to simple sample relative frequencies for high probability trees but are substantially more accurate for relatively low probability trees. The method allows the posterior probability of unsampled trees to be calculated when these trees contain only clades that are in other sampled trees. Furthermore, the method can be used to estimate the total probability of the set of sampled trees which provides a measure of the thoroughness of a posterior sample. [Bayesian phylogenetics; conditional clade distributions; improved accuracy; posterior probabilities of trees.] PMID:23479066
A new method for estimating extreme rainfall probabilities
Harper, G.A.; O'Hara, T.F. ); Morris, D.I. )
1994-02-01
As part of an EPRI-funded research program, the Yankee Atomic Electric Company developed a new method for estimating probabilities of extreme rainfall. It can be used, along with other techniques, to improve the estimation of probable maximum precipitation values for specific basins or regions.
Fisher classifier and its probability of error estimation
NASA Technical Reports Server (NTRS)
Chittineni, C. B.
1979-01-01
Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.
The estimation of tree posterior probabilities using conditional clade probability distributions.
Larget, Bret
2013-07-01
In this article I introduce the idea of conditional independence of separated subtrees as a principle by which to estimate the posterior probability of trees using conditional clade probability distributions rather than simple sample relative frequencies. I describe an algorithm for these calculations and software which implements these ideas. I show that these alternative calculations are very similar to simple sample relative frequencies for high probability trees but are substantially more accurate for relatively low probability trees. The method allows the posterior probability of unsampled trees to be calculated when these trees contain only clades that are in other sampled trees. Furthermore, the method can be used to estimate the total probability of the set of sampled trees which provides a measure of the thoroughness of a posterior sample.
Context Based Prior Probability Estimation of Object Appearance
NASA Astrophysics Data System (ADS)
Suzuyama, Yuki; Hotta, Kazuhiro; Takahashi, Haruhisa
This paper presents a method to estimate the prior probability of object appearance and position from only context information. The context is extracted from a whole image by Gabor filters. The conventional method represented the context by mixture of Gaussian distributions. The prior probabilities of object appearance and position were estimated by generative model. However, we define the probability estimation of object appearance as the binary-classification problem whether an input image contains the specific object or not. The Support Vector Machine is used to classify them, and the distance from the hyperplane is transformed to the probability using a sigmoid function. We also define the estimation problem of object position in an image from only the context as the regression problem. The position of object in an image is estimated by Support Vector Regression. Experimental results show that the proposed method outperforms the conventional method.
Incorporating detection probability into northern Great Plains pronghorn population estimates
Jacques, Christopher N.; Jenks, Jonathan A.; Grovenburg, Troy W.; Klaver, Robert W.; DePerno, Christopher S.
2014-01-01
Pronghorn (Antilocapra americana) abundances commonly are estimated using fixed-wing surveys, but these estimates are likely to be negatively biased because of violations of key assumptions underpinning line-transect methodology. Reducing bias and improving precision of abundance estimates through use of detection probability and mark-resight models may allow for more responsive pronghorn management actions. Given their potential application in population estimation, we evaluated detection probability and mark-resight models for use in estimating pronghorn population abundance. We used logistic regression to quantify probabilities that detecting pronghorn might be influenced by group size, animal activity, percent vegetation, cover type, and topography. We estimated pronghorn population size by study area and year using mixed logit-normal mark-resight (MLNM) models. Pronghorn detection probability increased with group size, animal activity, and percent vegetation; overall detection probability was 0.639 (95% CI = 0.612–0.667) with 396 of 620 pronghorn groups detected. Despite model selection uncertainty, the best detection probability models were 44% (range = 8–79%) and 180% (range = 139–217%) greater than traditional pronghorn population estimates. Similarly, the best MLNM models were 28% (range = 3–58%) and 147% (range = 124–180%) greater than traditional population estimates. Detection probability of pronghorn was not constant but depended on both intrinsic and extrinsic factors. When pronghorn detection probability is a function of animal group size, animal activity, landscape complexity, and percent vegetation, traditional aerial survey techniques will result in biased pronghorn abundance estimates. Standardizing survey conditions, increasing resighting occasions, or accounting for variation in individual heterogeneity in mark-resight models will increase the accuracy and precision of pronghorn population estimates.
Estimating total suspended sediment yield with probability sampling
Robert B. Thomas
1985-01-01
The ""Selection At List Time"" (SALT) scheme controls sampling of concentration for estimating total suspended sediment yield. The probability of taking a sample is proportional to its estimated contribution to total suspended sediment discharge. This procedure gives unbiased estimates of total suspended sediment yield and the variance of the...
Estimating the probability for major gene Alzheimer disease
Farrer, L.A. Boston Univ. School of Public Health, Boston, MA ); Cupples, L.A. )
1994-02-01
Alzheimer disease (AD) is a neuropsychiatric illness caused by multiple etiologies. Prediction of whether AD is genetically based in a given family is problematic because of censoring bias among unaffected relatives as a consequence of the late onset of the disorder, diagnostic uncertainties, heterogeneity, and limited information in a single family. The authors have developed a method based on Bayesian probability to compute values for a continuous variable that ranks AD families as having a major gene form of AD (MGAD). In addition, they have compared the Bayesian method with a maximum-likelihood approach. These methods incorporate sex- and age-adjusted risk estimates and allow for phenocopies and familial clustering of age on onset. Agreement is high between the two approaches for ranking families as MGAD (Spearman rank [r] = .92). When either method is used, the numerical outcomes are sensitive to assumptions of the gene frequency and cumulative incidence of the disease in the population. Consequently, risk estimates should be used cautiously for counseling purposes; however, there are numerous valid applications of these procedures in genetic and epidemiological studies. 41 refs., 4 figs., 3 tabs.
Estimating cycle pregnancy probability with incomplete data in contraceptive studies.
Chen, Pai-Lien; Zhou, Haibo; Dominik, Rosalie
2003-08-01
In studies on the effectiveness of barrier method contraceptives, researchers need to estimate the risk of pregnancy during consistent use of these methods. However, participants may not use assigned methods consistently, and only consistent-use cycles are included in the estimates. Inconsistent-use cycles are considered missing intervals, and a subject's early discontinuation from the study or pregnancy during inconsistent use is censored from the analysis. In this article, we consider a semiparametric maximum likelihood approach to estimate survival probability for grouped survival data with missing and censored data. The method is flexible in that it is nonparametric with respect to the underlying survival function, yet it can be easily extended to accommodate the covariates in a parametric way. Results from our simulation study show that the proposed method works well in practical sample sizes. Our findings support the U.S. Food and Drug Administration's (FDA) sample size requirements for contraceptive studies. We use data from an effectiveness trial on vaginal contraceptive film (VCF) to illustrate the proposed methods.
Estimating Radiogenic Cancer Risks
This document presents a revised methodology for EPA's estimation of cancer risks due to low-LET radiation exposures developed in light of information that has become available, especially new information on the Japanese atomic bomb survivors.
Naive Probability: Model-Based Estimates of Unique Events.
Khemlani, Sangeet S; Lotstein, Max; Johnson-Laird, Philip N
2015-08-01
We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, for conjunctions of events, and for inclusive disjunctions of events, by taking a primitive average of non-numerical probabilities. It computes conditional probabilities in a tractable way, treating the given event as evidence that may be relevant to the probability of the dependent event. A deliberative system 2 maps the resulting representations into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates. Experiments corroborated the theory's predictions. Participants concurred in estimates of real possibilities. They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P(A), P(B), P(A and B), disjunctions: P(A), P(B), P(A or B or both), and conditional probabilities P(A), P(B), P(B|A). They were faster to estimate the probabilities of compound propositions when they had already estimated the probabilities of each of their components. We discuss the implications of these results for theories of probabilistic reasoning. © 2014 Cognitive Science Society, Inc.
Estimating the probability of failure when testing reveals no failures
NASA Technical Reports Server (NTRS)
Miller, Keith W.; Morell, Larry J.; Noonan, Robert E.; Park, Stephen K.; Nicol, David M.; Murrill, Branson W.; Voas, Jeffrey M.
1992-01-01
Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution, and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.
Estimating the probability of failure when testing reveals no failures
NASA Technical Reports Server (NTRS)
Miller, Keith W.; Morell, Larry J.; Noonan, Robert E.; Park, Stephen K.; Nicol, David M.; Murrill, Branson W.; Voas, Jeffrey M.
1992-01-01
Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution, and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.
27% Probable: Estimating Whether or Not Large Numbers Are Prime.
ERIC Educational Resources Information Center
Bosse, Michael J.
2001-01-01
This brief investigation exemplifies such considerations by relating concepts from number theory, set theory, probability, logic, and calculus. Satisfying the call for students to acquire skills in estimation, the following technique allows one to "immediately estimate" whether or not a number is prime. (MM)
Nonparametric probability density estimation by optimization theoretic techniques
NASA Technical Reports Server (NTRS)
Scott, D. W.
1976-01-01
Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.
Local estimation of posterior class probabilities to minimize classification errors.
Guerrero-Curieses, Alicia; Cid-Sueiro, Jesús; Alaiz-Rodríguez, Rocío; Figueiras-Vidal, Aníbal R
2004-03-01
Decision theory shows that the optimal decision is a function of the posterior class probabilities. More specifically, in binary classification, the optimal decision is based on the comparison of the posterior probabilities with some threshold. Therefore, the most accurate estimates of the posterior probabilities are required near these decision thresholds. This paper discusses the design of objective functions that provide more accurate estimates of the probability values, taking into account the characteristics of each decision problem. We propose learning algorithms based on the stochastic gradient minimization of these loss functions. We show that the performance of the classifier is improved when these algorithms behave like sample selectors: samples near the decision boundary are the most relevant during learning.
Estimation of transition probabilities of credit ratings for several companies
NASA Astrophysics Data System (ADS)
Peng, Gan Chew; Hin, Pooi Ah
2016-10-01
This paper attempts to estimate the transition probabilities of credit ratings for a number of companies whose ratings have a dependence structure. Binary codes are used to represent the index of a company together with its ratings in the present and next quarters. We initially fit the data on the vector of binary codes with a multivariate power-normal distribution. We next compute the multivariate conditional distribution for the binary codes of rating in the next quarter when the index of the company and binary codes of the company in the present quarter are given. From the conditional distribution, we compute the transition probabilities of the company's credit ratings in two consecutive quarters. The resulting transition probabilities tally fairly well with the maximum likelihood estimates for the time-independent transition probabilities.
Estimation of State Transition Probabilities: A Neural Network Model
NASA Astrophysics Data System (ADS)
Saito, Hiroshi; Takiyama, Ken; Okada, Masato
2015-12-01
Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.
Estimating the empirical probability of submarine landslide occurrence
Geist, Eric L.; Parsons, Thomas E.; Mosher, David C.; Shipp, Craig; Moscardelli, Lorena; Chaytor, Jason D.; Baxter, Christopher D. P.; Lee, Homa J.; Urgeles, Roger
2010-01-01
The empirical probability for the occurrence of submarine landslides at a given location can be estimated from age dates of past landslides. In this study, tools developed to estimate earthquake probability from paleoseismic horizons are adapted to estimate submarine landslide probability. In both types of estimates, one has to account for the uncertainty associated with age-dating individual events as well as the open time intervals before and after the observed sequence of landslides. For observed sequences of submarine landslides, we typically only have the age date of the youngest event and possibly of a seismic horizon that lies below the oldest event in a landslide sequence. We use an empirical Bayes analysis based on the Poisson-Gamma conjugate prior model specifically applied to the landslide probability problem. This model assumes that landslide events as imaged in geophysical data are independent and occur in time according to a Poisson distribution characterized by a rate parameter λ. With this method, we are able to estimate the most likely value of λ and, importantly, the range of uncertainty in this estimate. Examples considered include landslide sequences observed in the Santa Barbara Channel, California, and in Port Valdez, Alaska. We confirm that given the uncertainties of age dating that landslide complexes can be treated as single events by performing statistical test of age dates representing the main failure episode of the Holocene Storegga landslide complex.
Low-probability flood risk modeling for New York City.
Aerts, Jeroen C J H; Lin, Ning; Botzen, Wouter; Emanuel, Kerry; de Moel, Hans
2013-05-01
The devastating impact by Hurricane Sandy (2012) again showed New York City (NYC) is one of the most vulnerable cities to coastal flooding around the globe. The low-lying areas in NYC can be flooded by nor'easter storms and North Atlantic hurricanes. The few studies that have estimated potential flood damage for NYC base their damage estimates on only a single, or a few, possible flood events. The objective of this study is to assess the full distribution of hurricane flood risk in NYC. This is done by calculating potential flood damage with a flood damage model that uses many possible storms and surge heights as input. These storms are representative for the low-probability/high-impact flood hazard faced by the city. Exceedance probability-loss curves are constructed under different assumptions about the severity of flood damage. The estimated flood damage to buildings for NYC is between US$59 and 129 millions/year. The damage caused by a 1/100-year storm surge is within a range of US$2 bn-5 bn, while this is between US$5 bn and 11 bn for a 1/500-year storm surge. An analysis of flood risk in each of the five boroughs of NYC finds that Brooklyn and Queens are the most vulnerable to flooding. This study examines several uncertainties in the various steps of the risk analysis, which resulted in variations in flood damage estimations. These uncertainties include: the interpolation of flood depths; the use of different flood damage curves; and the influence of the spectra of characteristics of the simulated hurricanes. © 2013 Society for Risk Analysis.
An application of recurrent nets to phone probability estimation.
Robinson, A J
1994-01-01
This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed; a role for which the recurrent net appears suitable. An overview of early developments of recurrent nets for phone recognition is given along with the more recent improvements that include their integration with Markov models. Recognition results are presented for the DARPA TIMIT and Resource Management tasks, and it is concluded that recurrent nets are competitive with traditional means for performing phone probability estimation.
Bayesian Estimator of Protein-Protein Association Probabilities
2008-05-28
The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein LC-MS/MS affinity isolation experiments. BEPro3 is public domain software, has been tested on Windows XP and version 10.4 or newer of the Mac OS 10.4, and is freely available. A user guide, example dataset with analysis and additional documentation are included with the BEPro3 download.
Expected probability weighted moment estimator for censored flood data
NASA Astrophysics Data System (ADS)
Jeon, Jong-June; Kim, Young-Oh; Kim, Yongdai
2011-08-01
Two well-known methods for estimating statistical distributions in hydrology are the Method of Moments (MOMs) and the method of probability weighted moments (PWM). This paper is concerned with the case where a part of the sample is censored. One situation where this might occur is when systematic data (e.g. from gauges) are combined with historical data, since the latter are often only reported if they exceed a high threshold. For this problem, three previously derived estimators are the "B17B" estimator, which is a direct modification of MOM to allow for partial censoring; the "partial PWM estimator", which similarly modifies PWM; and the "expected moments algorithm" estimator, which improves on B17B by replacing a sample adjustment of the censored-data moments with a population adjustment. The present paper proposes a similar modification to the PWM estimator, resulting in the "expected probability weighted moments (EPWM)" estimator. Simulation comparisons of these four estimators and also the maximum likelihood estimator show that the EPWM method is at least competitive with the other four and in many cases the best of the five estimators.
Improving estimates of tree mortality probability using potential growth rate
Das, Adrian J.; Stephenson, Nathan L.
2015-01-01
Tree growth rate is frequently used to estimate mortality probability. Yet, growth metrics can vary in form, and the justification for using one over another is rarely clear. We tested whether a growth index (GI) that scales the realized diameter growth rate against the potential diameter growth rate (PDGR) would give better estimates of mortality probability than other measures. We also tested whether PDGR, being a function of tree size, might better correlate with the baseline mortality probability than direct measurements of size such as diameter or basal area. Using a long-term dataset from the Sierra Nevada, California, U.S.A., as well as existing species-specific estimates of PDGR, we developed growth–mortality models for four common species. For three of the four species, models that included GI, PDGR, or a combination of GI and PDGR were substantially better than models without them. For the fourth species, the models including GI and PDGR performed roughly as well as a model that included only the diameter growth rate. Our results suggest that using PDGR can improve our ability to estimate tree survival probability. However, in the absence of PDGR estimates, the diameter growth rate was the best empirical predictor of mortality, in contrast to assumptions often made in the literature.
Injury Risk Estimation Expertise
Petushek, Erich J.; Ward, Paul; Cokely, Edward T.; Myer, Gregory D.
2015-01-01
Background: Simple observational assessment of movement is a potentially low-cost method for anterior cruciate ligament (ACL) injury screening and prevention. Although many individuals utilize some form of observational assessment of movement, there are currently no substantial data on group skill differences in observational screening of ACL injury risk. Purpose/Hypothesis: The purpose of this study was to compare various groups’ abilities to visually assess ACL injury risk as well as the associated strategies and ACL knowledge levels. The hypothesis was that sports medicine professionals would perform better than coaches and exercise science academics/students and that these subgroups would all perform better than parents and other general population members. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 428 individuals, including physicians, physical therapists, athletic trainers, strength and conditioning coaches, exercise science researchers/students, athletes, parents, and members of the general public participated in the study. Participants completed the ACL Injury Risk Estimation Quiz (ACL-IQ) and answered questions related to assessment strategy and ACL knowledge. Results: Strength and conditioning coaches, athletic trainers, physical therapists, and exercise science students exhibited consistently superior ACL injury risk estimation ability (+2 SD) as compared with sport coaches, parents of athletes, and members of the general public. The performance of a substantial number of individuals in the exercise sciences/sports medicines (approximately 40%) was similar to or exceeded clinical instrument-based biomechanical assessment methods (eg, ACL nomogram). Parents, sport coaches, and the general public had lower ACL-IQ, likely due to their lower ACL knowledge and to rating the importance of knee/thigh motion lower and weight and jump height higher. Conclusion: Substantial cross-professional/group differences in visual ACL
Probability Estimation of CO2 Leakage Through Faults at Geologic Carbon Sequestration Sites
Zhang, Yingqi; Oldenburg, Curt; Finsterle, Stefan; Jordan, Preston; Zhang, Keni
2008-11-01
Leakage of CO{sub 2} and brine along faults at geologic carbon sequestration (GCS) sites is a primary concern for storage integrity. The focus of this study is on the estimation of the probability of leakage along faults or fractures. This leakage probability is controlled by the probability of a connected network of conduits existing at a given site, the probability of this network encountering the CO{sub 2} plume, and the probability of this network intersecting environmental resources that may be impacted by leakage. This work is designed to fit into a risk assessment and certification framework that uses compartments to represent vulnerable resources such as potable groundwater, health and safety, and the near-surface environment. The method we propose includes using percolation theory to estimate the connectivity of the faults, and generating fuzzy rules from discrete fracture network simulations to estimate leakage probability. By this approach, the probability of CO{sub 2} escaping into a compartment for a given system can be inferred from the fuzzy rules. The proposed method provides a quick way of estimating the probability of CO{sub 2} or brine leaking into a compartment. In addition, it provides the uncertainty range of the estimated probability.
Using Correlation to Compute Better Probability Estimates in Plan Graphs
NASA Technical Reports Server (NTRS)
Bryce, Daniel; Smith, David E.
2006-01-01
Plan graphs are commonly used in planning to help compute heuristic "distance" estimates between states and goals. A few authors have also attempted to use plan graphs in probabilistic planning to compute estimates of the probability that propositions can be achieved and actions can be performed. This is done by propagating probability information forward through the plan graph from the initial conditions through each possible action to the action effects, and hence to the propositions at the next layer of the plan graph. The problem with these calculations is that they make very strong independence assumptions - in particular, they usually assume that the preconditions for each action are independent of each other. This can lead to gross overestimates in probability when the plans for those preconditions interfere with each other. It can also lead to gross underestimates of probability when there is synergy between the plans for two or more preconditions. In this paper we introduce a notion of the binary correlation between two propositions and actions within a plan graph, show how to propagate this information within a plan graph, and show how this improves probability estimates for planning. This notion of correlation can be thought of as a continuous generalization of the notion of mutual exclusion (mutex) often used in plan graphs. At one extreme (correlation=0) two propositions or actions are completely mutex. With correlation = 1, two propositions or actions are independent, and with correlation > 1, two propositions or actions are synergistic. Intermediate values can and do occur indicating different degrees to which propositions and action interfere or are synergistic. We compare this approach with another recent approach by Bryce that computes probability estimates using Monte Carlo simulation of possible worlds in plan graphs.
Recursive estimation of prior probabilities using the mixture approach
NASA Technical Reports Server (NTRS)
Kazakos, D.
1974-01-01
The problem of estimating the prior probabilities q sub k of a mixture of known density functions f sub k(X), based on a sequence of N statistically independent observations is considered. It is shown that for very mild restrictions on f sub k(X), the maximum likelihood estimate of Q is asymptotically efficient. A recursive algorithm for estimating Q is proposed, analyzed, and optimized. For the M = 2 case, it is possible for the recursive algorithm to achieve the same performance with the maximum likelihood one. For M 2, slightly inferior performance is the price for having a recursive algorithm. However, the loss is computable and tolerable.
Methods for estimating drought streamflow probabilities for Virginia streams
Austin, Samuel H.
2014-01-01
Maximum likelihood logistic regression model equations used to estimate drought flow probabilities for Virginia streams are presented for 259 hydrologic basins in Virginia. Winter streamflows were used to estimate the likelihood of streamflows during the subsequent drought-prone summer months. The maximum likelihood logistic regression models identify probable streamflows from 5 to 8 months in advance. More than 5 million streamflow daily values collected over the period of record (January 1, 1900 through May 16, 2012) were compiled and analyzed over a minimum 10-year (maximum 112-year) period of record. The analysis yielded the 46,704 equations with statistically significant fit statistics and parameter ranges published in two tables in this report. These model equations produce summer month (July, August, and September) drought flow threshold probabilities as a function of streamflows during the previous winter months (November, December, January, and February). Example calculations are provided, demonstrating how to use the equations to estimate probable streamflows as much as 8 months in advance.
Estimating transition probabilities in unmarked populations --entropy revisited
Cooch, E.G.; Link, W.A.
1999-01-01
The probability of surviving and moving between 'states' is of great interest to biologists. Robust estimation of these transitions using multiple observations of individually identifiable marked individuals has received considerable attention in recent years. However, in some situations, individuals are not identifiable (or have a very low recapture rate), although all individuals in a sample can be assigned to a particular state (e.g. breeding or non-breeding) without error. In such cases, only aggregate data (number of individuals in a given state at each occasion) are available. If the underlying matrix of transition probabilities does not vary through time and aggregate data are available for several time periods, then it is possible to estimate these parameters using least-squares methods. Even when such data are available, this assumption of stationarity will usually be deemed overly restrictive and, frequently, data will only be available for two time periods. In these cases, the problem reduces to estimating the most likely matrix (or matrices) leading to the observed frequency distribution of individuals in each state. An entropy maximization approach has been previously suggested. In this paper, we show that the entropy approach rests on a particular limiting assumption, and does not provide estimates of latent population parameters (the transition probabilities), but rather predictions of realized rates.
Assessing semantic coherence and logical fallacies in joint probability estimates.
Wolfe, Christopher R; Reyna, Valerie F
2010-05-01
A constellation of joint probability estimates is semantically coherent when the quantitative relationship among estimates of P(A), P(B), P(A and B), and P(A or B) is consistent with the relationship among the sets described in the problem statement. The possible probability estimates can form an extremely large number of permutations. However, this entire problem space can be reduced to six theoretically meaningful patterns: logically fallacious (conjunction or disjunction fallacies), identical sets (e.g., water and H(2)O), mutually exclusive sets (e.g., horses and zebras), subsets (e.g., robins and birds), overlapping sets (e.g., accountants and musicians), and inconsistent overlapping sets. Determining which of these patterns describes any set of probability estimates has been automated using Excel spreadsheet formulae. Researchers may use the semantic coherence technique to examine the consequences of differently worded problems, individual differences, or experimental manipulations. The spreadsheet described above can be downloaded as a supplement from http://brm.psychonomic-journals.org/content/supplemental.
On estimating the fracture probability of nuclear graphite components
NASA Astrophysics Data System (ADS)
Srinivasan, Makuteswara
2008-10-01
The properties of nuclear grade graphites exhibit anisotropy and could vary considerably within a manufactured block. Graphite strength is affected by the direction of alignment of the constituent coke particles, which is dictated by the forming method, coke particle size, and the size, shape, and orientation distribution of pores in the structure. In this paper, a Weibull failure probability analysis for components is presented using the American Society of Testing Materials strength specification for nuclear grade graphites for core components in advanced high-temperature gas-cooled reactors. The risk of rupture (probability of fracture) and survival probability (reliability) of large graphite blocks are calculated for varying and discrete values of service tensile stresses. The limitations in these calculations are discussed from considerations of actual reactor environmental conditions that could potentially degrade the specification properties because of damage due to complex interactions between irradiation, temperature, stress, and variability in reactor operation.
Translating Climate-Change Probabilities into Impact Risks - Overcoming the Impact- Model Bottleneck
NASA Astrophysics Data System (ADS)
Dettinger, M.
2008-12-01
Projections of climate change in response to increasing greenhouse-gas concentrations are uncertain and likely to remain so for the foreseeable future. As more projections become available for analysts, we are increasingly able to characterize the probabilities of obtaining various levels of climate change in current projections. However, the probabilities of most interest in impact assessments are not the probabilities of climate changes, but rather the probabilities (or risks) of various levels and kinds of climate-change impact. These risks can be difficult to estimate even if the climate-change probabilities are well known. The difficulty arises because, frequently, impact models and assessments are computationally demanding or time consuming of hands-on, human expert analyses, so that severe limits are placed on the numbers of climate- change scenarios from which detailed impacts can be assessed. Estimation of risks of various impacts is generally difficult with the few resulting examples. However, real-world examples from the water-resources sector will be used to show that, by applying several different "derived distributions" approaches for estimating the risks of various impacts from known climate-change probabilities to just a few impact-model simulations, risks can be estimated along with indications of how accurate are the impact-risk estimates. The prospects for a priori selection of a few climate-change scenarios (from a larger ensemble of available projections) that will allow the best, most economical estimates of impact risks will be explored with a simple but real-world example.
Collective animal behavior from Bayesian estimation and probability matching.
Pérez-Escudero, Alfonso; de Polavieja, Gonzalo G
2011-11-01
Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.
Collective Animal Behavior from Bayesian Estimation and Probability Matching
Pérez-Escudero, Alfonso; de Polavieja, Gonzalo G.
2011-01-01
Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior. PMID:22125487
Conditional Probability Density Functions Arising in Bearing Estimation
1994-05-01
and a better known performance measure: the Cramer-Rao bound . 14. SUMECT TEm IL5 NUlMN OF PAMES Probability Density Function, bearing angle estimation...results obtained using the calculated density functions and a better known performance measure: the Cramer-Rao bound . The major results obtained are as...48 15. Sampling Inteval , Propagation Delay, and Covariance Singularities ....... 52 viii List of Figures (continued
Estimating the exceedance probability of rain rate by logistic regression
NASA Technical Reports Server (NTRS)
Chiu, Long S.; Kedem, Benjamin
1990-01-01
Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
Estimating the exceedance probability of rain rate by logistic regression
NASA Technical Reports Server (NTRS)
Chiu, Long S.; Kedem, Benjamin
1990-01-01
Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.
New method for estimating low-earth-orbit collision probabilities
NASA Technical Reports Server (NTRS)
Vedder, John D.; Tabor, Jill L.
1991-01-01
An unconventional but general method is described for estimating the probability of collision between an earth-orbiting spacecraft and orbital debris. This method uses a Monte Caralo simulation of the orbital motion of the target spacecraft and each discrete debris object to generate an empirical set of distances, each distance representing the separation between the spacecraft and the nearest debris object at random times. Using concepts from the asymptotic theory of extreme order statistics, an analytical density function is fitted to this set of minimum distances. From this function, it is possible to generate realistic collision estimates for the spacecraft.
New method for estimating low-earth-orbit collision probabilities
NASA Technical Reports Server (NTRS)
Vedder, John D.; Tabor, Jill L.
1991-01-01
An unconventional but general method is described for estimating the probability of collision between an earth-orbiting spacecraft and orbital debris. This method uses a Monte Caralo simulation of the orbital motion of the target spacecraft and each discrete debris object to generate an empirical set of distances, each distance representing the separation between the spacecraft and the nearest debris object at random times. Using concepts from the asymptotic theory of extreme order statistics, an analytical density function is fitted to this set of minimum distances. From this function, it is possible to generate realistic collision estimates for the spacecraft.
Estimating probable flaw distributions in PWR steam generator tubes
Gorman, J.A.; Turner, A.P.L.
1997-02-01
This paper describes methods for estimating the number and size distributions of flaws of various types in PWR steam generator tubes. These estimates are needed when calculating the probable primary to secondary leakage through steam generator tubes under postulated accidents such as severe core accidents and steam line breaks. The paper describes methods for two types of predictions: (1) the numbers of tubes with detectable flaws of various types as a function of time, and (2) the distributions in size of these flaws. Results are provided for hypothetical severely affected, moderately affected and lightly affected units. Discussion is provided regarding uncertainties and assumptions in the data and analyses.
Estimation of probable maximum precipitation for southwest basin (Iran)
NASA Astrophysics Data System (ADS)
Fattahi, E.
2009-04-01
The probable maximum precipitation (PMP) is the greatest depth of precipitation for a given duration that is physically possible over a given size storm area at a particular geographical location at a certain time of the year. Hydrologists use a PMP magnitude together with its spatial and temporal distributions for the catchments of a dam to calculate the probable maximum flood (PMF). In this study the synoptic (physical) method has been compared with statistical method (e. g. the Hershfield's) for calculate PMP in southwest stations of Iran.. In this study also PMP estimations were obtained by statistical analysis (Hershfield's Methods) of the series of annual maximum 24h precipitation amounts. The results of statistical method show a correlation between the point PMP and the mean annual precipitation which is significant. We found that PMP estimates by statistical method are well comparable with values of obtained by the synoptic (physical) method for different durations. Results also shows that limited transposition of statistical methods gives higher estimates, in comparisons with synoptic method. Keyword: Probable maximum precipitation, synoptic, Hershfield's method, Depth-Area-Duration (DAD), Dew point Temperature.
Risk preferences, probability weighting, and strategy tradeoffs in wildfire management
Michael S. Hand; Matthew J. Wibbenmeyer; Dave Calkin; Matthew P. Thompson
2015-01-01
Wildfires present a complex applied risk management environment, but relatively little attention has been paid to behavioral and cognitive responses to risk among public agency wildfire managers. This study investigates responses to risk, including probability weighting and risk aversion, in a wildfire management context using a survey-based experiment administered to...
Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging
Clark, G A
2004-09-21
The work reported here shows the proof of principle (using a small data set) for a suite of algorithms designed to estimate the probability density function of hyperspectral background data and compute the appropriate Constant False Alarm Rate (CFAR) matched filter decision threshold for a chemical plume detector. Future work will provide a thorough demonstration of the algorithms and their performance with a large data set. The LASI (Large Aperture Search Initiative) Project involves instrumentation and image processing for hyperspectral images of chemical plumes in the atmosphere. The work reported here involves research and development on algorithms for reducing the false alarm rate in chemical plume detection and identification algorithms operating on hyperspectral image cubes. The chemical plume detection algorithms to date have used matched filters designed using generalized maximum likelihood ratio hypothesis testing algorithms [1, 2, 5, 6, 7, 12, 10, 11, 13]. One of the key challenges in hyperspectral imaging research is the high false alarm rate that often results from the plume detector [1, 2]. The overall goal of this work is to extend the classical matched filter detector to apply Constant False Alarm Rate (CFAR) methods to reduce the false alarm rate, or Probability of False Alarm P{sub FA} of the matched filter [4, 8, 9, 12]. A detector designer is interested in minimizing the probability of false alarm while simultaneously maximizing the probability of detection P{sub D}. This is summarized by the Receiver Operating Characteristic Curve (ROC) [10, 11], which is actually a family of curves depicting P{sub D} vs. P{sub FA}parameterized by varying levels of signal to noise (or clutter) ratio (SNR or SCR). Often, it is advantageous to be able to specify a desired P{sub FA} and develop a ROC curve (P{sub D} vs. decision threshold r{sub 0}) for that case. That is the purpose of this work. Specifically, this work develops a set of algorithms and MATLAB
Estimation of the probability of success in petroleum exploration
Davis, J.C.
1977-01-01
A probabilistic model for oil exploration can be developed by assessing the conditional relationship between perceived geologic variables and the subsequent discovery of petroleum. Such a model includes two probabilistic components, the first reflecting the association between a geologic condition (structural closure, for example) and the occurrence of oil, and the second reflecting the uncertainty associated with the estimation of geologic variables in areas of limited control. Estimates of the conditional relationship between geologic variables and subsequent production can be found by analyzing the exploration history of a "training area" judged to be geologically similar to the exploration area. The geologic variables are assessed over the training area using an historical subset of the available data, whose density corresponds to the present control density in the exploration area. The success or failure of wells drilled in the training area subsequent to the time corresponding to the historical subset provides empirical estimates of the probability of success conditional upon geology. Uncertainty in perception of geological conditions may be estimated from the distribution of errors made in geologic assessment using the historical subset of control wells. These errors may be expressed as a linear function of distance from available control. Alternatively, the uncertainty may be found by calculating the semivariogram of the geologic variables used in the analysis: the two procedures will yield approximately equivalent results. The empirical probability functions may then be transferred to the exploration area and used to estimate the likelihood of success of specific exploration plays. These estimates will reflect both the conditional relationship between the geological variables used to guide exploration and the uncertainty resulting from lack of control. The technique is illustrated with case histories from the mid-Continent area of the U.S.A. ?? 1977 Plenum
Cost functions to estimate a posteriori probabilities in multiclass problems.
Cid-Sueiro, J; Arribas, J I; Urbán-Muñoz, S; Figueiras-Vidal, A R
1999-01-01
The problem of designing cost functions to estimate a posteriori probabilities in multiclass problems is addressed in this paper. We establish necessary and sufficient conditions that these costs must satisfy in one-class one-output networks whose outputs are consistent with probability laws. We focus our attention on a particular subset of the corresponding cost functions; those which verify two usually interesting properties: symmetry and separability (well-known cost functions, such as the quadratic cost or the cross entropy are particular cases in this subset). Finally, we present a universal stochastic gradient learning rule for single-layer networks, in the sense of minimizing a general version of these cost functions for a wide family of nonlinear activation functions.
Estimation of probability densities using scale-free field theories.
Kinney, Justin B
2014-07-01
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics. Previous work has argued that this problem can be addressed in a natural way using methods from statistical field theory. Here I describe results that allow this field-theoretic approach to be rapidly and deterministically computed in low dimensions, making it practical for use in day-to-day data analysis. Importantly, this approach does not impose a privileged length scale for smoothness of the inferred probability density, but rather learns a natural length scale from the data due to the tradeoff between goodness of fit and an Occam factor. Open source software implementing this method in one and two dimensions is provided.
Estimation of probability densities using scale-free field theories
NASA Astrophysics Data System (ADS)
Kinney, Justin B.
2014-07-01
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics. Previous work has argued that this problem can be addressed in a natural way using methods from statistical field theory. Here I describe results that allow this field-theoretic approach to be rapidly and deterministically computed in low dimensions, making it practical for use in day-to-day data analysis. Importantly, this approach does not impose a privileged length scale for smoothness of the inferred probability density, but rather learns a natural length scale from the data due to the tradeoff between goodness of fit and an Occam factor. Open source software implementing this method in one and two dimensions is provided.
Estimating transition probabilities among everglades wetland communities using multistate models
Hotaling, A.S.; Martin, J.; Kitchens, W.M.
2009-01-01
In this study we were able to provide the first estimates of transition probabilities of wet prairie and slough vegetative communities in Water Conservation Area 3A (WCA3A) of the Florida Everglades and to identify the hydrologic variables that determine these transitions. These estimates can be used in management models aimed at restoring proportions of wet prairie and slough habitats to historical levels in the Everglades. To determine what was driving the transitions between wet prairie and slough communities we evaluated three hypotheses: seasonality, impoundment, and wet and dry year cycles using likelihood-based multistate models to determine the main driver of wet prairie conversion in WCA3A. The most parsimonious model included the effect of wet and dry year cycles on vegetative community conversions. Several ecologists have noted wet prairie conversion in southern WCA3A but these are the first estimates of transition probabilities among these community types. In addition, to being useful for management of the Everglades we believe that our framework can be used to address management questions in other ecosystems. ?? 2009 The Society of Wetland Scientists.
Probability Distribution Estimation for Autoregressive Pixel-Predictive Image Coding.
Weinlich, Andreas; Amon, Peter; Hutter, Andreas; Kaup, André
2016-03-01
Pixelwise linear prediction using backward-adaptive least-squares or weighted least-squares estimation of prediction coefficients is currently among the state-of-the-art methods for lossless image compression. While current research is focused on mean intensity prediction of the pixel to be transmitted, best compression requires occurrence probability estimates for all possible intensity values. Apart from common heuristic approaches, we show how prediction error variance estimates can be derived from the (weighted) least-squares training region and how a complete probability distribution can be built based on an autoregressive image model. The analysis of image stationarity properties further allows deriving a novel formula for weight computation in weighted least-squares proofing and generalizing ad hoc equations from the literature. For sparse intensity distributions in non-natural images, a modified image model is presented. Evaluations were done in the newly developed C++ framework volumetric, artificial, and natural image lossless coder (Vanilc), which can compress a wide range of images, including 16-bit medical 3D volumes or multichannel data. A comparison with several of the best available lossless image codecs proofs that the method can achieve very competitive compression ratios. In terms of reproducible research, the source code of Vanilc has been made public.
Risks and probabilities of breast cancer: short-term versus lifetime probabilities.
Bryant, H E; Brasher, P M
1994-01-01
OBJECTIVE: To calculate age-specific short-term and lifetime probabilities of breast cancer among a cohort of Canadian women. DESIGN: Double decrement life table. SETTING: Alberta. SUBJECTS: Women with first invasive breast cancers registered with the Alberta Cancer Registry between 1985 and 1987. MAIN OUTCOME MEASURES: Lifetime probability of breast cancer from birth and for women at various ages; short-term (up to 10 years) probability of breast cancer for women at various ages. RESULTS: The lifetime probability of breast cancer is 10.17% at birth and peaks at 10.34% at age 25 years, after which it decreases owing to a decline in the number of years over which breast cancer risk will be experienced. However, the probability of manifesting breast cancer in the next year increases steadily from the age of 30 onward, reaching 0.36% at 85 years. The probability of manifesting the disease within the next 10 years peaks at 2.97% at age 70 and decreases thereafter, again owing to declining probabilities of surviving the interval. CONCLUSIONS: Given that the incidence of breast cancer among Albertan women during the study period was similar to the national average, we conclude that currently more than 1 in 10 women in Canada can expect to have breast cancer at some point during their life. However, risk varies considerably over a woman's lifetime, with most risk concentrated after age 49. On the basis of the shorter-term age-specific risks that we present, the clinician can put breast cancer risk into perspective for younger women and heighten awareness among women aged 50 years or more. PMID:8287343
Probabilities and statistics for backscatter estimates obtained by a scatterometer
NASA Technical Reports Server (NTRS)
Pierson, Willard J., Jr.
1989-01-01
Methods for the recovery of winds near the surface of the ocean from measurements of the normalized radar backscattering cross section must recognize and make use of the statistics (i.e., the sampling variability) of the backscatter measurements. Radar backscatter values from a scatterometer are random variables with expected values given by a model. A model relates backscatter to properties of the waves on the ocean, which are in turn generated by the winds in the atmospheric marine boundary layer. The effective wind speed and direction at a known height for a neutrally stratified atmosphere are the values to be recovered from the model. The probability density function for the backscatter values is a normal probability distribution with the notable feature that the variance is a known function of the expected value. The sources of signal variability, the effects of this variability on the wind speed estimation, and criteria for the acceptance or rejection of models are discussed. A modified maximum likelihood method for estimating wind vectors is described. Ways to make corrections for the kinds of errors found for the Seasat SASS model function are described, and applications to a new scatterometer are given.
Risk Preferences, Probability Weighting, and Strategy Tradeoffs in Wildfire Management.
Hand, Michael S; Wibbenmeyer, Matthew J; Calkin, David E; Thompson, Matthew P
2015-10-01
Wildfires present a complex applied risk management environment, but relatively little attention has been paid to behavioral and cognitive responses to risk among public agency wildfire managers. This study investigates responses to risk, including probability weighting and risk aversion, in a wildfire management context using a survey-based experiment administered to federal wildfire managers. Respondents were presented with a multiattribute lottery-choice experiment where each lottery is defined by three outcome attributes: expenditures for fire suppression, damage to private property, and exposure of firefighters to the risk of aviation-related fatalities. Respondents choose one of two strategies, each of which includes "good" (low cost/low damage) and "bad" (high cost/high damage) outcomes that occur with varying probabilities. The choice task also incorporates an information framing experiment to test whether information about fatality risk to firefighters alters managers' responses to risk. Results suggest that managers exhibit risk aversion and nonlinear probability weighting, which can result in choices that do not minimize expected expenditures, property damage, or firefighter exposure. Information framing tends to result in choices that reduce the risk of aviation fatalities, but exacerbates nonlinear probability weighting. © 2015 Society for Risk Analysis.
Automated estimation of rare event probabilities in biochemical systems
NASA Astrophysics Data System (ADS)
Daigle, Bernie J.; Roh, Min K.; Gillespie, Dan T.; Petzold, Linda R.
2011-01-01
In biochemical systems, the occurrence of a rare event can be accompanied by catastrophic consequences. Precise characterization of these events using Monte Carlo simulation methods is often intractable, as the number of realizations needed to witness even a single rare event can be very large. The weighted stochastic simulation algorithm (wSSA) [J. Chem. Phys. 129, 165101 (2008)] and its subsequent extension [J. Chem. Phys. 130, 174103 (2009)] alleviate this difficulty with importance sampling, which effectively biases the system toward the desired rare event. However, extensive computation coupled with substantial insight into a given system is required, as there is currently no automatic approach for choosing wSSA parameters. We present a novel modification of the wSSA—the doubly weighted SSA (dwSSA)—that makes possible a fully automated parameter selection method. Our approach uses the information-theoretic concept of cross entropy to identify parameter values yielding minimum variance rare event probability estimates. We apply the method to four examples: a pure birth process, a birth-death process, an enzymatic futile cycle, and a yeast polarization model. Our results demonstrate that the proposed method (1) enables probability estimation for a class of rare events that cannot be interrogated with the wSSA, and (2) for all examples tested, reduces the number of runs needed to achieve comparable accuracy by multiple orders of magnitude. For a particular rare event in the yeast polarization model, our method transforms a projected simulation time of 600 years to three hours. Furthermore, by incorporating information-theoretic principles, our approach provides a framework for the development of more sophisticated influencing schemes that should further improve estimation accuracy.
Automated estimation of rare event probabilities in biochemical systems
Daigle, Bernie J.; Roh, Min K.; Gillespie, Dan T.; Petzold, Linda R.
2011-01-01
In biochemical systems, the occurrence of a rare event can be accompanied by catastrophic consequences. Precise characterization of these events using Monte Carlo simulation methods is often intractable, as the number of realizations needed to witness even a single rare event can be very large. The weighted stochastic simulation algorithm (wSSA) [J. Chem. Phys. 129, 165101 (2008)] and its subsequent extension [J. Chem. Phys. 130, 174103 (2009)] alleviate this difficulty with importance sampling, which effectively biases the system toward the desired rare event. However, extensive computation coupled with substantial insight into a given system is required, as there is currently no automatic approach for choosing wSSA parameters. We present a novel modification of the wSSA—the doubly weighted SSA (dwSSA)—that makes possible a fully automated parameter selection method. Our approach uses the information-theoretic concept of cross entropy to identify parameter values yielding minimum variance rare event probability estimates. We apply the method to four examples: a pure birth process, a birth-death process, an enzymatic futile cycle, and a yeast polarization model. Our results demonstrate that the proposed method (1) enables probability estimation for a class of rare events that cannot be interrogated with the wSSA, and (2) for all examples tested, reduces the number of runs needed to achieve comparable accuracy by multiple orders of magnitude. For a particular rare event in the yeast polarization model, our method transforms a projected simulation time of 600 years to three hours. Furthermore, by incorporating information-theoretic principles, our approach provides a framework for the development of more sophisticated influencing schemes that should further improve estimation accuracy. PMID:21280690
Site Specific Probable Maximum Precipitation Estimates and Professional Judgement
NASA Astrophysics Data System (ADS)
Hayes, B. D.; Kao, S. C.; Kanney, J. F.; Quinlan, K. R.; DeNeale, S. T.
2015-12-01
State and federal regulatory authorities currently rely upon the US National Weather Service Hydrometeorological Reports (HMRs) to determine probable maximum precipitation (PMP) estimates (i.e., rainfall depths and durations) for estimating flooding hazards for relatively broad regions in the US. PMP estimates for the contributing watersheds upstream of vulnerable facilities are used to estimate riverine flooding hazards while site-specific estimates for small water sheds are appropriate for individual facilities such as nuclear power plants. The HMRs are often criticized due to their limitations on basin size, questionable applicability in regions affected by orographic effects, their lack of consist methods, and generally by their age. HMR-51 for generalized PMP estimates for the United States east of the 105th meridian, was published in 1978 and is sometimes perceived as overly conservative. The US Nuclear Regulatory Commission (NRC), is currently reviewing several flood hazard evaluation reports that rely on site specific PMP estimates that have been commercially developed. As such, NRC has recently investigated key areas of expert judgement via a generic audit and one in-depth site specific review as they relate to identifying and quantifying actual and potential storm moisture sources, determining storm transposition limits, and adjusting available moisture during storm transposition. Though much of the approach reviewed was considered a logical extension of HMRs, two key points of expert judgement stood out for further in-depth review. The first relates primarily to small storms and the use of a heuristic for storm representative dew point adjustment developed for the Electric Power Research Institute by North American Weather Consultants in 1993 in order to harmonize historic storms for which only 12 hour dew point data was available with more recent storms in a single database. The second issue relates to the use of climatological averages for spatially
[Estimating survival of thrushes: modeling capture-recapture probabilities].
Burskiî, O V
2011-01-01
The stochastic modeling technique serves as a way to correctly separate "return rate" of marked animals into survival rate (phi) and capture probability (p). The method can readily be used with the program MARK freely distributed through Internet (Cooch, White, 2009). Input data for the program consist of "capture histories" of marked animals--strings of units and zeros indicating presence or absence of the individual among captures (or sightings) along the set of consequent recapture occasions (e.g., years). Probability of any history is a product of binomial probabilities phi, p or their complements (1 - phi) and (1 - p) for each year of observation over the individual. Assigning certain values to parameters phi and p, one can predict the composition of all individual histories in the sample and assess the likelihood of the prediction. The survival parameters for different occasions and cohorts of individuals can be set either equal or different, as well as recapture parameters can be set in different ways. There is a possibility to constraint the parameters, according to the hypothesis being tested, in the form of a specific model. Within the specified constraints, the program searches for parameter values that describe the observed composition of histories with the maximum likelihood. It computes the parameter estimates along with confidence limits and the overall model likelihood. There is a set of tools for testing the model goodness-of-fit under assumption of equality of survival rates among individuals and independence of their fates. Other tools offer a proper selection among a possible variety of models, providing the best parity between details and precision in describing reality. The method was applied to 20-yr recapture and resighting data series on 4 thrush species (genera Turdus, Zoothera) breeding in the Yenisei River floodplain within the middle taiga subzone. The capture probabilities were quite independent of observational efforts fluctuations
Online Reinforcement Learning Using a Probability Density Estimation.
Agostini, Alejandro; Celaya, Enric
2017-01-01
Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods. This nonstationarity has a local profile, varying not only along the learning process but also along different regions of the state space. We propose to deal with these problems using an estimation of the probability density of samples represented with a gaussian mixture model. To deal with the nonstationarity problem, we use the common approach of introducing a forgetting factor in the updating formula. However, instead of using the same forgetting factor for the whole domain, we make it dependent on the local density of samples, which we use to estimate the nonstationarity of the function at any given input point. To address the biased sampling problem, the forgetting factor applied to each mixture component is modulated according to the new information provided in the updating, rather than forgetting depending only on time, thus avoiding undesired distortions of the approximation in less sampled regions.
Structural Reliability Using Probability Density Estimation Methods Within NESSUS
NASA Technical Reports Server (NTRS)
Chamis, Chrisos C. (Technical Monitor); Godines, Cody Ric
2003-01-01
A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been
Structural Reliability Using Probability Density Estimation Methods Within NESSUS
NASA Technical Reports Server (NTRS)
Chamis, Chrisos C. (Technical Monitor); Godines, Cody Ric
2003-01-01
A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been
Gray, David R
2010-12-01
As global trade increases so too does the probability of introduction of alien species to new locations. Estimating the probability of an alien species introduction and establishment following introduction is a necessary step in risk estimation (probability of an event times the consequences, in the currency of choice, of the event should it occur); risk estimation is a valuable tool for reducing the risk of biological invasion with limited resources. The Asian gypsy moth, Lymantria dispar (L.), is a pest species whose consequence of introduction and establishment in North America and New Zealand warrants over US$2 million per year in surveillance expenditure. This work describes the development of a two-dimensional phenology model (GLS-2d) that simulates insect development from source to destination and estimates: (1) the probability of introduction from the proportion of the source population that would achieve the next developmental stage at the destination and (2) the probability of establishment from the proportion of the introduced population that survives until a stable life cycle is reached at the destination. The effect of shipping schedule on the probabilities of introduction and establishment was examined by varying the departure date from 1 January to 25 December by weekly increments. The effect of port efficiency was examined by varying the length of time that invasion vectors (shipping containers and ship) were available for infection. The application of GLS-2d is demonstrated using three common marine trade routes (to Auckland, New Zealand, from Kobe, Japan, and to Vancouver, Canada, from Kobe and from Vladivostok, Russia).
Polynomial probability distribution estimation using the method of moments
Mattsson, Lars; Rydén, Jesper
2017-01-01
We suggest a procedure for estimating Nth degree polynomial approximations to unknown (or known) probability density functions (PDFs) based on N statistical moments from each distribution. The procedure is based on the method of moments and is setup algorithmically to aid applicability and to ensure rigor in use. In order to show applicability, polynomial PDF approximations are obtained for the distribution families Normal, Log-Normal, Weibull as well as for a bimodal Weibull distribution and a data set of anonymized household electricity use. The results are compared with results for traditional PDF series expansion methods of Gram–Charlier type. It is concluded that this procedure is a comparatively simple procedure that could be used when traditional distribution families are not applicable or when polynomial expansions of probability distributions might be considered useful approximations. In particular this approach is practical for calculating convolutions of distributions, since such operations become integrals of polynomial expressions. Finally, in order to show an advanced applicability of the method, it is shown to be useful for approximating solutions to the Smoluchowski equation. PMID:28394949
Classifier calibration using splined empirical probabilities in clinical risk prediction.
Gaudoin, René; Montana, Giovanni; Jones, Simon; Aylin, Paul; Bottle, Alex
2015-06-01
The aims of supervised machine learning (ML) applications fall into three broad categories: classification, ranking, and calibration/probability estimation. Many ML methods and evaluation techniques relate to the first two. Nevertheless, there are many applications where having an accurate probability estimate is of great importance. Deriving accurate probabilities from the output of a ML method is therefore an active area of research, resulting in several methods to turn a ranking into class probability estimates. In this manuscript we present a method, splined empirical probabilities, based on the receiver operating characteristic (ROC) to complement existing algorithms such as isotonic regression. Unlike most other methods it works with a cumulative quantity, the ROC curve, and as such can be tagged onto an ROC analysis with minor effort. On a diverse set of measures of the quality of probability estimates (Hosmer-Lemeshow, Kullback-Leibler divergence, differences in the cumulative distribution function) using simulated and real health care data, our approach compares favourably with the standard calibration method, the pool adjacent violators algorithm used to perform isotonic regression.
Impaired probability estimation and decision-making in pathological gambling poker players.
Linnet, Jakob; Frøslev, Mette; Ramsgaard, Stine; Gebauer, Line; Mouridsen, Kim; Wohlert, Victoria
2012-03-01
Poker has gained tremendous popularity in recent years, increasing the risk for some individuals to develop pathological gambling. Here, we investigated cognitive biases in a computerized two-player poker task against a fictive opponent, among 12 pathological gambling poker players (PGP), 10 experienced poker players (ExP), and 11 inexperienced poker players (InP). Players were compared on probability estimation and decision-making with the hypothesis that ExP would have significantly lower cognitive biases than PGP and InP, and that the groups could be differentiated based on their cognitive bias styles. The results showed that ExP had a significantly lower average error margin in probability estimation than PGP and InP, and that PGP played hands with lower winning probability than ExP. Binomial logistic regression showed perfect differentiation (100%) between ExP and PGP, and 90.5% classification accuracy between ExP and InP. Multinomial logistic regression showed an overall classification accuracy of 23 out of 33 (69.7%) between the three groups. The classification accuracy of ExP was higher than that of PGP and InP due to the similarities in probability estimation and decision-making between PGP and InP. These impairments in probability estimation and decision-making of PGP may have implications for assessment and treatment of cognitive biases in pathological gambling poker players.
The estimation of probable maximum precipitation: the case of Catalonia.
Casas, M Carmen; Rodríguez, Raül; Nieto, Raquel; Redaño, Angel
2008-12-01
A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.
Transition probability estimates for non-Markov multi-state models.
Titman, Andrew C
2015-12-01
Non-parametric estimation of the transition probabilities in multi-state models is considered for non-Markov processes. Firstly, a generalization of the estimator of Pepe et al., (1991) (Statistics in Medicine) is given for a class of progressive multi-state models based on the difference between Kaplan-Meier estimators. Secondly, a general estimator for progressive or non-progressive models is proposed based upon constructed univariate survival or competing risks processes which retain the Markov property. The properties of the estimators and their associated standard errors are investigated through simulation. The estimators are demonstrated on datasets relating to survival and recurrence in patients with colon cancer and prothrombin levels in liver cirrhosis patients.
Estimates of radiogenic cancer risks.
Puskin, J S; Nelson, C B
1995-07-01
A methodology recently developed by the U.S. EPA for estimating the carcinogenic risks from ionizing radiation is described. For most cancer sites, the risk model is one in which age-specific, relative risk coefficients are obtained by taking a geometric mean of the coefficients derived from the atomic bomb survivor data using two different methods for transporting risks from the Japanese to the U.S. population. The risk models are applied to estimate organ-specific risks per unit dose for a stationary population with mortality rates governed by 1980 U.S. vital statistics. With the exception of breast cancer, low-LET radiogenic cancer risk estimates are reduced by a factor of 2 at low doses and dose rates compared to acute high dose exposure conditions. For low dose (or dose rate) conditions, the risk of inducing a premature cancer death from uniform, whole body, low-LET irradiation is calculated to be 5.1 x 10(-2) Gy-1. Neglecting nonfatal skin cancers, the corresponding incidence risk is 7.6 x 10(-2) Gy-1. High-LET (alpha particle) risks are presumed to increase linearly with dose and to be independent of dose rate. High-LET risks are estimated to be 20 times the low-LET risks estimated under low dose rate conditions, except for leukemia and breast cancer where RBEs of 1 and 10 are adopted, respectively.
2007-12-01
Implementing Risk Management on Software Intensive Projects. IEEE Software, 14(3):83-89. Fairley , R . (1994). Risk Management for Software Projects...conditional probability and the Bayesian effect is preceded by an introduction to some basic concepts of probability. Though this discussion draws from R ...Engineering Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. Charette, R . N. (1991). The Risks with Risk Analysis
Biau, David Jean; Latouche, Aurélien; Porcher, Raphaël
2007-09-01
The Kaplan-Meier estimator is the current method for estimating the probability of an event to occur with time in orthopaedics. However, the Kaplan-Meier estimator was designed to estimate the probability of an event that eventually will occur for all patients, ie, death, and this does not hold for other outcomes. For example, not all patients will experience hip arthroplasty loosening because some may die first, and some may have their implant removed to treat infection or recurrent hip dislocation. Such events that preclude the observation of the event of interest are called competing events. We suggest the Kaplan-Meier estimator is inappropriate in the presence of competing events and show that it overestimates the probability of the event of interest to occur with time. The cumulative incidence estimator is an alternative approach to Kaplan-Meier in situations where competing risks are likely. Three common situations include revision for implant loosening in the long-term followup of arthroplasties or implant failure in the context of limb-salvage surgery or femoral neck fracture.
Structural health monitoring and probability of detection estimation
NASA Astrophysics Data System (ADS)
Forsyth, David S.
2016-02-01
Structural health monitoring (SHM) methods are often based on nondestructive testing (NDT) sensors and are often proposed as replacements for NDT to lower cost and/or improve reliability. In order to take advantage of SHM for life cycle management, it is necessary to determine the Probability of Detection (POD) of the SHM system just as for traditional NDT to ensure that the required level of safety is maintained. Many different possibilities exist for SHM systems, but one of the attractive features of SHM versus NDT is the ability to take measurements very simply after the SHM system is installed. Using a simple statistical model of POD, some authors have proposed that very high rates of SHM system data sampling can result in high effective POD even in situations where an individual test has low POD. In this paper, we discuss the theoretical basis for determining the effect of repeated inspections, and examine data from SHM experiments against this framework to show how the effective POD from multiple tests can be estimated.
Semi-supervised dimensionality reduction using estimated class membership probabilities
NASA Astrophysics Data System (ADS)
Li, Wei; Ruan, Qiuqi; Wan, Jun
2012-10-01
In solving pattern-recognition tasks with partially labeled training data, the semi-supervised dimensionality reduction method, which considers both labeled and unlabeled data, is preferable for improving the classification and generalization capability of the testing data. Among such techniques, graph-based semi-supervised learning methods have attracted a lot of attention due to their appealing properties in discovering discriminative structure and geometric structure of data points. Although they have achieved remarkable success, they cannot promise good performance when the size of the labeled data set is small, as a result of inaccurate class matrix variance approximated by insufficient labeled training data. In this paper, we tackle this problem by combining class membership probabilities estimated from unlabeled data and ground-truth class information associated with labeled data to more precisely characterize the class distribution. Therefore, it is expected to enhance performance in classification tasks. We refer to this approach as probabilistic semi-supervised discriminant analysis (PSDA). The proposed PSDA is applied to face and facial expression recognition tasks and is evaluated using the ORL, Extended Yale B, and CMU PIE face databases and the Cohn-Kanade facial expression database. The promising experimental results demonstrate the effectiveness of our proposed method.
Conditional Probabilities for Large Events Estimated by Small Earthquake Rate
NASA Astrophysics Data System (ADS)
Wu, Yi-Hsuan; Chen, Chien-Chih; Li, Hsien-Chi
2016-01-01
We examined forecasting quiescence and activation models to obtain the conditional probability that a large earthquake will occur in a specific time period on different scales in Taiwan. The basic idea of the quiescence and activation models is to use earthquakes that have magnitudes larger than the completeness magnitude to compute the expected properties of large earthquakes. We calculated the probability time series for the whole Taiwan region and for three subareas of Taiwan—the western, eastern, and northeastern Taiwan regions—using 40 years of data from the Central Weather Bureau catalog. In the probability time series for the eastern and northeastern Taiwan regions, a high probability value is usually yielded in cluster events such as events with foreshocks and events that all occur in a short time period. In addition to the time series, we produced probability maps by calculating the conditional probability for every grid point at the time just before a large earthquake. The probability maps show that high probability values are yielded around the epicenter before a large earthquake. The receiver operating characteristic (ROC) curves of the probability maps demonstrate that the probability maps are not random forecasts, but also suggest that lowering the magnitude of a forecasted large earthquake may not improve the forecast method itself. From both the probability time series and probability maps, it can be observed that the probability obtained from the quiescence model increases before a large earthquake and the probability obtained from the activation model increases as the large earthquakes occur. The results lead us to conclude that the quiescence model has better forecast potential than the activation model.
Akanda, Md Abdus Salam; Alpizar-Jara, Russell
2014-01-01
Modeling individual heterogeneity in capture probabilities has been one of the most challenging tasks in capture–recapture studies. Heterogeneity in capture probabilities can be modeled as a function of individual covariates, but correlation structure among capture occasions should be taking into account. A proposed generalized estimating equations (GEE) and generalized linear mixed modeling (GLMM) approaches can be used to estimate capture probabilities and population size for capture–recapture closed population models. An example is used for an illustrative application and for comparison with currently used methodology. A simulation study is also conducted to show the performance of the estimation procedures. Our simulation results show that the proposed quasi-likelihood based on GEE approach provides lower SE than partial likelihood based on either generalized linear models (GLM) or GLMM approaches for estimating population size in a closed capture–recapture experiment. Estimator performance is good if a large proportion of individuals are captured. For cases where only a small proportion of individuals are captured, the estimates become unstable, but the GEE approach outperforms the other methods. PMID:24772290
2005-01-01
preparedness by addressing unique planning, equipment, training, and exercise needs of large urban areas (DHS, 2004). Al- though there appears to be agreement ...reasonable minimum standards for community preparedness. Until these questions are answered, allocating home- land security resources based on risk is the...and threats are correlated with population density. There are practical benefits for using simple risk indicators such as those based upon population
Dental age estimation: the role of probability estimates at the 10 year threshold.
Lucas, Victoria S; McDonald, Fraser; Neil, Monica; Roberts, Graham
2014-08-01
The use of probability at the 18 year threshold has simplified the reporting of dental age estimates for emerging adults. The availability of simple to use widely available software has enabled the development of the probability threshold for individual teeth in growing children. Tooth development stage data from a previous study at the 10 year threshold were reused to estimate the probability of developing teeth being above or below the 10 year thresh-hold using the NORMDIST Function in Microsoft Excel. The probabilities within an individual subject are averaged to give a single probability that a subject is above or below 10 years old. To test the validity of this approach dental panoramic radiographs of 50 female and 50 male children within 2 years of the chronological age were assessed with the chronological age masked. Once the whole validation set of 100 radiographs had been assessed the masking was removed and the chronological age and dental age compared. The dental age was compared with chronological age to determine whether the dental age correctly or incorrectly identified a validation subject as above or below the 10 year threshold. The probability estimates correctly identified children as above or below on 94% of occasions. Only 2% of the validation group with a chronological age of less than 10 years were assigned to the over 10 year group. This study indicates the very high accuracy of assignment at the 10 year threshold. Further work at other legally important age thresholds is needed to explore the value of this approach to the technique of age estimation. Copyright © 2014. Published by Elsevier Ltd.
Exaggerated Risk: Prospect Theory and Probability Weighting in Risky Choice
ERIC Educational Resources Information Center
Kusev, Petko; van Schaik, Paul; Ayton, Peter; Dent, John; Chater, Nick
2009-01-01
In 5 experiments, we studied precautionary decisions in which participants decided whether or not to buy insurance with specified cost against an undesirable event with specified probability and cost. We compared the risks taken for precautionary decisions with those taken for equivalent monetary gambles. Fitting these data to Tversky and…
Exaggerated Risk: Prospect Theory and Probability Weighting in Risky Choice
ERIC Educational Resources Information Center
Kusev, Petko; van Schaik, Paul; Ayton, Peter; Dent, John; Chater, Nick
2009-01-01
In 5 experiments, we studied precautionary decisions in which participants decided whether or not to buy insurance with specified cost against an undesirable event with specified probability and cost. We compared the risks taken for precautionary decisions with those taken for equivalent monetary gambles. Fitting these data to Tversky and…
Lermer, Eva; Streicher, Bernhard; Sachs, Rainer; Raue, Martina; Frey, Dieter
2016-03-01
Recent findings on construal level theory (CLT) suggest that abstract thinking leads to a lower estimated probability of an event occurring compared to concrete thinking. We applied this idea to the risk context and explored the influence of construal level (CL) on the overestimation of small and underestimation of large probabilities for risk estimates concerning a vague target person (Study 1 and Study 3) and personal risk estimates (Study 2). We were specifically interested in whether the often-found overestimation of small probabilities could be reduced with abstract thinking, and the often-found underestimation of large probabilities was reduced with concrete thinking. The results showed that CL influenced risk estimates. In particular, a concrete mindset led to higher risk estimates compared to an abstract mindset for several adverse events, including events with small and large probabilities. This suggests that CL manipulation can indeed be used for improving the accuracy of lay people's estimates of small and large probabilities. Moreover, the results suggest that professional risk managers' risk estimates of common events (thus with a relatively high probability) could be improved by adopting a concrete mindset. However, the abstract manipulation did not lead managers to estimate extremely unlikely events more accurately. Potential reasons for different CL manipulation effects on risk estimates' accuracy between lay people and risk managers are discussed.
NASA Astrophysics Data System (ADS)
Wang, Q. J.
1990-12-01
Unbiased estimators of probability weighted moments (PWM) and partial probability weighted moments (PPWM) from systematic and historical flood information are derived. Applications are made to estimating parameters and quantiles of the generalized extreme value (GEV) distribution. The effect of lower bound censoring, which might be deliberately introduced in practice, is also considered.
Estimating the Probability of Elevated Nitrate Concentrations in Ground Water in Washington State
Frans, Lonna M.
2008-01-01
Logistic regression was used to relate anthropogenic (manmade) and natural variables to the occurrence of elevated nitrate concentrations in ground water in Washington State. Variables that were analyzed included well depth, ground-water recharge rate, precipitation, population density, fertilizer application amounts, soil characteristics, hydrogeomorphic regions, and land-use types. Two models were developed: one with and one without the hydrogeomorphic regions variable. The variables in both models that best explained the occurrence of elevated nitrate concentrations (defined as concentrations of nitrite plus nitrate as nitrogen greater than 2 milligrams per liter) were the percentage of agricultural land use in a 4-kilometer radius of a well, population density, precipitation, soil drainage class, and well depth. Based on the relations between these variables and measured nitrate concentrations, logistic regression models were developed to estimate the probability of nitrate concentrations in ground water exceeding 2 milligrams per liter. Maps of Washington State were produced that illustrate these estimated probabilities for wells drilled to 145 feet below land surface (median well depth) and the estimated depth to which wells would need to be drilled to have a 90-percent probability of drawing water with a nitrate concentration less than 2 milligrams per liter. Maps showing the estimated probability of elevated nitrate concentrations indicated that the agricultural regions are most at risk followed by urban areas. The estimated depths to which wells would need to be drilled to have a 90-percent probability of obtaining water with nitrate concentrations less than 2 milligrams per liter exceeded 1,000 feet in the agricultural regions; whereas, wells in urban areas generally would need to be drilled to depths in excess of 400 feet.
Chen, Xinguang; Lin, Feng
2013-01-01
Background and objective New analytical tools are needed to advance tobacco research, tobacco control planning and tobacco use prevention practice. In this study, we validated a method to extract information from cross-sectional survey for quantifying population dynamics of adolescent smoking behavior progression. Methods With a 3-stage 7-path model, probabilities of smoking behavior progression were estimated employing the Probabilistic Discrete Event System (PDES) method and the cross-sectional data from 1997-2006 National Survey on Drug Use and Health (NSDUH). Validity of the PDES method was assessed using data from the National Longitudinal Survey of Youth 1997 and trends in smoking transition covering the period during which funding for tobacco control was cut substantively in 2003 in the United States. Results Probabilities for all seven smoking progression paths were successfully estimated with the PDES method and the NSDUH data. The absolute difference in the estimated probabilities between the two approaches varied from 0.002 to 0.076 (p>0.05 for all) and were highly correlated with each other (R2=0.998, p<0.01). Changes in the estimated transitional probabilities across the 1997-2006 reflected the 2003 funding cut for tobacco control. Conclusions The PDES method has validity in quantifying population dynamics of smoking behavior progression with cross-sectional survey data. The estimated transitional probabilities add new evidence supporting more advanced tobacco research, tobacco control planning and tobacco use prevention practice. This method can be easily extended to study other health risk behaviors. PMID:25279247
Estimating earthquake-induced failure probability and downtime of critical facilities.
Porter, Keith; Ramer, Kyle
2012-01-01
Fault trees have long been used to estimate failure risk in earthquakes, especially for nuclear power plants (NPPs). One interesting application is that one can assess and manage the probability that two facilities - a primary and backup - would be simultaneously rendered inoperative in a single earthquake. Another is that one can calculate the probabilistic time required to restore a facility to functionality, and the probability that, during any given planning period, the facility would be rendered inoperative for any specified duration. A large new peer-reviewed library of component damageability and repair-time data for the first time enables fault trees to be used to calculate the seismic risk of operational failure and downtime for a wide variety of buildings other than NPPs. With the new library, seismic risk of both the failure probability and probabilistic downtime can be assessed and managed, considering the facility's unique combination of structural and non-structural components, their seismic installation conditions, and the other systems on which the facility relies. An example is offered of real computer data centres operated by a California utility. The fault trees were created and tested in collaboration with utility operators, and the failure probability and downtime results validated in several ways.
A Non-Parametric Probability Density Estimator and Some Applications.
1984-05-01
ESTIMATOR AND SOME APPLICATIONS Ronald P. Fuchs, B.S., M.S. Major, USAF Approved: oe Jt / 6 ’.°, Accep ted: Dean, School of Engineering .-7% Preface...4. Sensitivity to Support Estimation 35 5. Estimate of Density Function With No Subsampling 45 6 . Density Estimate Generated from Subsample One 46 7...Comparison of Distribution Function Average Square Errors (n-100) 61 6 . ASE for Basic and Parameterized Estimates 84 7. Distribution Function Method
[Medical insurance estimation of risks].
Dunér, H
1975-11-01
The purpose of insurance medicine is to make a prognostic estimate of medical risk-factors in persons who apply for life, health, or accident insurance. Established risk-groups with a calculated average mortality and morbidity form the basis for premium rates and insurance terms. In most cases the applicant is accepted for insurance after a self-assessment of his health. Only around one per cent of the applications are refused, but there are cases in which the premium is raised, temporarily or permanently. It is often a matter of rough estimate, since the knowlege of the long-term prognosis for many diseases is incomplete. The insurance companies' rules for estimate of risk are revised at intervals of three or four years. The estimate of risk as regards life insurance has been gradually liberalised, while the medical conditions for health insurance have become stricter owing to an increase in the claims rate.
Remediating Non-Positive Definite State Covariances for Collision Probability Estimation
NASA Technical Reports Server (NTRS)
Hall, Doyle T.; Hejduk, Matthew D.; Johnson, Lauren C.
2017-01-01
The NASA Conjunction Assessment Risk Analysis team estimates the probability of collision (Pc) for a set of Earth-orbiting satellites. The Pc estimation software processes satellite position+velocity states and their associated covariance matri-ces. On occasion, the software encounters non-positive definite (NPD) state co-variances, which can adversely affect or prevent the Pc estimation process. Inter-polation inaccuracies appear to account for the majority of such covariances, alt-hough other mechanisms contribute also. This paper investigates the origin of NPD state covariance matrices, three different methods for remediating these co-variances when and if necessary, and the associated effects on the Pc estimation process.
Assessing Risks through the Determination of Rare Event Probabilities.
1980-07-01
independent consultants as to the risk posed by proposed LNG Tanker movement in the New York harbor. Table 1 (taken from Fairley (1977)) represents a summary of...Consistency Analysis for LNG Tanker Movements In a critique of the study concerning the safety of LNG tanker movements, Fairley (1977) notes that the...probabilities of most factors could possibly be upwardly corrected. The upward correc- tions are summarized in the following table taken from Fairley (1974, p
Exaggerated risk: prospect theory and probability weighting in risky choice.
Kusev, Petko; van Schaik, Paul; Ayton, Peter; Dent, John; Chater, Nick
2009-11-01
In 5 experiments, we studied precautionary decisions in which participants decided whether or not to buy insurance with specified cost against an undesirable event with specified probability and cost. We compared the risks taken for precautionary decisions with those taken for equivalent monetary gambles. Fitting these data to Tversky and Kahneman's (1992) prospect theory, we found that the weighting function required to model precautionary decisions differed from that required for monetary gambles. This result indicates a failure of the descriptive invariance axiom of expected utility theory. For precautionary decisions, people overweighted small, medium-sized, and moderately large probabilities-they exaggerated risks. This effect is not anticipated by prospect theory or experience-based decision research (Hertwig, Barron, Weber, & Erev, 2004). We found evidence that exaggerated risk is caused by the accessibility of events in memory: The weighting function varies as a function of the accessibility of events. This suggests that people's experiences of events leak into decisions even when risk information is explicitly provided. Our findings highlight a need to investigate how variation in decision content produces variation in preferences for risk.
NASA Technical Reports Server (NTRS)
Havens, K. A.; Minster, T. C.; Thadani, S. G.
1976-01-01
The probability of error or, alternatively, the probability of correct classification (PCC) is an important criterion in analyzing the performance of a classifier. Labeled samples (those with ground truth) are usually employed to evaluate the performance of a classifier. Occasionally, the numbers of labeled samples are inadequate, or no labeled samples are available to evaluate a classifier's performance; for example, when crop signatures from one area from which ground truth is available are used to classify another area from which no ground truth is available. This paper reports the results of an experiment to estimate the probability of error using unlabeled test samples (i.e., without the aid of ground truth).
Naive Probability: Model-based Estimates of Unique Events
2014-05-04
1. Introduction Probabilistic thinking is ubiquitous in both numerate and innumerate cultures. Aristotle ...wrote: “A probability is a thing that happens for the most part” ( Aristotle , Rhetoric, Book I, 1357a35, see Barnes, 1984). His account, as Franklin...1984). The complete works of Aristotle . Princeton, NJ: Princeton University Press
New approach to probability estimate of femoral neck fracture by fall (Slovak regression model).
Wendlova, J
2009-01-01
3,216 Slovak women with primary or secondary osteoporosis or osteopenia, aged 20-89 years, were examined with the bone densitometer DXA (dual energy X-ray absorptiometry, GE, Prodigy - Primo), x = 58.9, 95% C.I. (58.42; 59.38). The values of the following variables for each patient were measured: FSI (femur strength index), T-score total hip left, alpha angle - left, theta angle - left, HAL (hip axis length) left, BMI (body mass index) was calculated from the height and weight of the patients. Regression model determined the following order of independent variables according to the intensity of their influence upon the occurrence of values of dependent FSI variable: 1. BMI, 2. theta angle, 3. T-score total hip, 4. alpha angle, 5. HAL. The regression model equation, calculated from the variables monitored in the study, enables a doctor in praxis to determine the probability magnitude (absolute risk) for the occurrence of pathological value of FSI (FSI < 1) in the femoral neck area, i. e., allows for probability estimate of a femoral neck fracture by fall for Slovak women. 1. The Slovak regression model differs from regression models, published until now, in chosen independent variables and a dependent variable, belonging to biomechanical variables, characterising the bone quality. 2. The Slovak regression model excludes the inaccuracies of other models, which are not able to define precisely the current and past clinical condition of tested patients (e.g., to define the length and dose of exposure to risk factors). 3. The Slovak regression model opens the way to a new method of estimating the probability (absolute risk) or the odds for a femoral neck fracture by fall, based upon the bone quality determination. 4. It is assumed that the development will proceed by improving the methods enabling to measure the bone quality, determining the probability of fracture by fall (Tab. 6, Fig. 3, Ref. 22). Full Text (Free, PDF) www.bmj.sk.
NASA Astrophysics Data System (ADS)
Huang, Q. Z.; Hsu, S. Y.; Li, M. H.
2016-12-01
The long-term streamflow prediction is important not only to estimate water-storage of a reservoir but also to the surface water intakes, which supply people's livelihood, agriculture, and industry. Climatology forecasts of streamflow have been traditionally used for calculating the exceedance probability curve of streamflow and water resource management. In this study, we proposed a stochastic approach to predict the exceedance probability curve of long-term streamflow with the seasonal weather outlook from Central Weather Bureau (CWB), Taiwan. The approach incorporates a statistical downscale weather generator and a catchment-scale hydrological model to convert the monthly outlook into daily rainfall and temperature series and to simulate the streamflow based on the outlook information. Moreover, we applied Bayes' theorem to derive a method for calculating the exceedance probability curve of the reservoir inflow based on the seasonal weather outlook and its imperfection. The results show that our approach can give the exceedance probability curves reflecting the three-month weather outlook and its accuracy. We also show how the improvement of the weather outlook affects the predicted exceedance probability curves of the streamflow. Our approach should be useful for the seasonal planning and management of water resource and their risk assessment.
A new parametric method of estimating the joint probability density
NASA Astrophysics Data System (ADS)
Alghalith, Moawia
2017-04-01
We present simple parametric methods that overcome major limitations of the literature on joint/marginal density estimation. In doing so, we do not assume any form of marginal or joint distribution. Furthermore, using our method, a multivariate density can be easily estimated if we know only one of the marginal densities. We apply our methods to financial data.
Estimating Terrorist Risk with Possibility Theory
J.L. Darby
2004-11-30
This report summarizes techniques that use possibility theory to estimate the risk of terrorist acts. These techniques were developed under the sponsorship of the Department of Homeland Security (DHS) as part of the National Infrastructure Simulation Analysis Center (NISAC) project. The techniques have been used to estimate the risk of various terrorist scenarios to support NISAC analyses during 2004. The techniques are based on the Logic Evolved Decision (LED) methodology developed over the past few years by Terry Bott and Steve Eisenhawer at LANL. [LED] The LED methodology involves the use of fuzzy sets, possibility theory, and approximate reasoning. LED captures the uncertainty due to vagueness and imprecision that is inherent in the fidelity of the information available for terrorist acts; probability theory cannot capture these uncertainties. This report does not address the philosophy supporting the development of nonprobabilistic approaches, and it does not discuss possibility theory in detail. The references provide a detailed discussion of these subjects. [Shafer] [Klir and Yuan] [Dubois and Prade] Suffice to say that these approaches were developed to address types of uncertainty that cannot be addressed by a probability measure. An earlier report discussed in detail the problems with using a probability measure to evaluate terrorist risk. [Darby Methodology]. Two related techniques are discussed in this report: (1) a numerical technique, and (2) a linguistic technique. The numerical technique uses traditional possibility theory applied to crisp sets, while the linguistic technique applies possibility theory to fuzzy sets. Both of these techniques as applied to terrorist risk for NISAC applications are implemented in software called PossibleRisk. The techniques implemented in PossibleRisk were developed specifically for use in estimating terrorist risk for the NISAC program. The LEDTools code can be used to perform the same linguistic evaluation as
The effect of framing actuarial risk probabilities on involuntary civil commitment decisions.
Scurich, Nicholas; John, Richard S
2011-04-01
Despite a proliferation of actuarial risk assessment instruments, empirical research on the communication of violence risk is scant and there is virtually no research on the consumption of actuarial risk assessment. Using a 2 × 3 Latin Square factorial design, this experiment tested whether decision-makers are sensitive to varying levels of risk expressed probabilistically and whether the framing of actuarial risk probabilities is consequential for commitment decisions. Consistent with research on attribute framing, in which describing an attribute in terms of its complement leads to different conclusions, this experiment found that the way actuarial risk estimates are framed leads to disparate commitment decisions. For example, risk framed as 26% probability of violence generally led decision-makers to authorize commitment, whereas the same risk framed in the complement, a 74% probability of no violence, generally led decision-makers to release. This result was most pronounced for moderate risk levels. Implications for the risk communication format debate, forensic practice and research are discussed.
A CONDITIONAL PROBABILITY APPROACH FOR ANALYZING SURVEY DATA TO ESTIMATE PROBABILITY OF IMPAIRMENT
A question that arises is how can survey data, collected with a random design, provide an initial screening for identifying unsampled areas that are likely to have biological impairment? A random sampling design provides estimates of relative fraction of the population of interes...
NASA Astrophysics Data System (ADS)
Baer, P.; Mastrandrea, M.
2006-12-01
Simple probabilistic models which attempt to estimate likely transient temperature change from specified CO2 emissions scenarios must make assumptions about at least six uncertain aspects of the causal chain between emissions and temperature: current radiative forcing (including but not limited to aerosols), current land use emissions, carbon sinks, future non-CO2 forcing, ocean heat uptake, and climate sensitivity. Of these, multiple PDFs (probability density functions) have been published for the climate sensitivity, a couple for current forcing and ocean heat uptake, one for future non-CO2 forcing, and none for current land use emissions or carbon cycle uncertainty (which are interdependent). Different assumptions about these parameters, as well as different model structures, will lead to different estimates of likely temperature increase from the same emissions pathway. Thus policymakers will be faced with a range of temperature probability distributions for the same emissions scenarios, each described by a central tendency and spread. Because our conventional understanding of uncertainty and probability requires that a probabilistically defined variable of interest have only a single mean (or median, or modal) value and a well-defined spread, this "multidimensional" uncertainty defies straightforward utilization in policymaking. We suggest that there are no simple solutions to the questions raised. Crucially, we must dispel the notion that there is a "true" probability probabilities of this type are necessarily subjective, and reasonable people may disagree. Indeed, we suggest that what is at stake is precisely the question, what is it reasonable to believe, and to act as if we believe? As a preliminary suggestion, we demonstrate how the output of a simple probabilistic climate model might be evaluated regarding the reasonableness of the outputs it calculates with different input PDFs. We suggest further that where there is insufficient evidence to clearly
NASA Astrophysics Data System (ADS)
Fung, D. C. N.; Wang, J. P.; Chang, S. H.; Chang, S. C.
2014-12-01
Using a revised statistical model built on past seismic probability models, the probability of different magnitude earthquakes occurring within variable timespans can be estimated. The revised model is based on Poisson distribution and includes the use of best-estimate values of the probability distribution of different magnitude earthquakes recurring from a fault from literature sources. Our study aims to apply this model to the Taipei metropolitan area with a population of 7 million, which lies in the Taipei Basin and is bounded by two normal faults: the Sanchaio and Taipei faults. The Sanchaio fault is suggested to be responsible for previous large magnitude earthquakes, such as the 1694 magnitude 7 earthquake in northwestern Taipei (Cheng et. al., 2010). Based on a magnitude 7 earthquake return period of 543 years, the model predicts the occurrence of a magnitude 7 earthquake within 20 years at 1.81%, within 79 years at 6.77% and within 300 years at 21.22%. These estimates increase significantly when considering a magnitude 6 earthquake; the chance of one occurring within the next 20 years is estimated to be 3.61%, 79 years at 13.54% and 300 years at 42.45%. The 79 year period represents the average lifespan of the Taiwan population. In contrast, based on data from 2013, the probability of Taiwan residents experiencing heart disease or malignant neoplasm is 11.5% and 29%. The inference of this study is that the calculated risk that the Taipei population is at from a potentially damaging magnitude 6 or greater earthquake occurring within their lifetime is just as great as of suffering from a heart attack or other health ailments.
Estimating risks of perinatal death.
Smith, Gordon C S
2005-01-01
The relative and absolute risks of perinatal death that are estimated from observational studies are used frequently in counseling about obstetric intervention. The statistical basis for these estimates therefore is crucial, but many studies are seriously flawed. In this review, a number of aspects of the approach to the estimation of the risk of perinatal death are addressed. Key factors in the analysis include (1) the definition of the cause of the death, (2) differentiation between antepartum and intrapartum events, (3) the use of the appropriate denominator for the given cause of death, (4) the assessment of the cumulative risk where appropriate, (5) the use of appropriate statistical tests, (6) the stratification of analysis of delivery-related deaths by gestational age, and (7) the specific features of multiple pregnancy, which include the correct determination of the timing of antepartum stillbirth and the use of paired statistical tests when outcomes are compared in relation to the birth order of twin pairs.
Estimating Risk: Stereotype Amplification and the Perceived Risk of Criminal Victimization
ERIC Educational Resources Information Center
Quillian, Lincoln; Pager, Devah
2010-01-01
This paper considers the process by which individuals estimate the risk of adverse events, with particular attention to the social context in which risk estimates are formed. We compare subjective probability estimates of crime victimization to actual victimization experiences among respondents from the 1994 to 2002 waves of the Survey of Economic…
Estimating the posterior probabilities using the k-nearest neighbor rule.
Atiya, Amir F
2005-03-01
In many pattern classification problems, an estimate of the posterior probabilities (rather than only a classification) is required. This is usually the case when some confidence measure in the classification is needed. In this article, we propose a new posterior probability estimator. The proposed estimator considers the K-nearest neighbors. It attaches a weight to each neighbor that contributes in an additive fashion to the posterior probability estimate. The weights corresponding to the K-nearest-neighbors (which add to 1) are estimated from the data using a maximum likelihood approach. Simulation studies confirm the effectiveness of the proposed estimator.
Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation
NASA Astrophysics Data System (ADS)
Demir, Uygar; Toker, Cenk; Çenet, Duygu
2016-07-01
Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent
Compositional cokriging for mapping the probability risk of groundwater contamination by nitrates.
Pardo-Igúzquiza, Eulogio; Chica-Olmo, Mario; Luque-Espinar, Juan A; Rodríguez-Galiano, Víctor
2015-11-01
Contamination by nitrates is an important cause of groundwater pollution and represents a potential risk to human health. Management decisions must be made using probability maps that assess the nitrate concentration potential of exceeding regulatory thresholds. However these maps are obtained with only a small number of sparse monitoring locations where the nitrate concentrations have been measured. It is therefore of great interest to have an efficient methodology for obtaining those probability maps. In this paper, we make use of the fact that the discrete probability density function is a compositional variable. The spatial discrete probability density function is estimated by compositional cokriging. There are several advantages in using this approach: (i) problems of classical indicator cokriging, like estimates outside the interval (0,1) and order relations, are avoided; (ii) secondary variables (e.g. aquifer parameters) can be included in the estimation of the probability maps; (iii) uncertainty maps of the probability maps can be obtained; (iv) finally there are modelling advantages because the variograms and cross-variograms of real variables that do not have the restrictions of indicator variograms and indicator cross-variograms. The methodology was applied to the Vega de Granada aquifer in Southern Spain and the advantages of the compositional cokriging approach were demonstrated.
Estimating the Probability of Electrical Short Circuits from Tin Whiskers. Part 2
NASA Technical Reports Server (NTRS)
Courey, Karim J.; Asfour, Shihab S.; Onar, Arzu; Bayliss, Jon A.; Ludwig, Larry L.; Wright, Maria C.
2010-01-01
To comply with lead-free legislation, many manufacturers have converted from tin-lead to pure tin finishes of electronic components. However, pure tin finishes have a greater propensity to grow tin whiskers than tin-lead finishes. Since tin whiskers present an electrical short circuit hazard in electronic components, simulations have been developed to quantify the risk of said short circuits occurring. Existing risk simulations make the assumption that when a free tin whisker has bridged two adjacent exposed electrical conductors, the result is an electrical short circuit. This conservative assumption is made because shorting is a random event that had an unknown probability associated with it. Note however that due to contact resistance electrical shorts may not occur at lower voltage levels. In our first article we developed an empirical probability model for tin whisker shorting. In this paper, we develop a more comprehensive empirical model using a refined experiment with a larger sample size, in which we studied the effect of varying voltage on the breakdown of the contact resistance which leads to a short circuit. From the resulting data we estimated the probability distribution of an electrical short, as a function of voltage. In addition, the unexpected polycrystalline structure seen in the focused ion beam (FIB) cross section in the first experiment was confirmed in this experiment using transmission electron microscopy (TEM). The FIB was also used to cross section two card guides to facilitate the measurement of the grain size of each card guide's tin plating to determine its finish .
Estimating the Probability of Electrical Short Circuits from Tin Whiskers. Part 2
NASA Technical Reports Server (NTRS)
Courey, Karim J.; Asfour, Shihab S.; Onar, Arzu; Bayliss, Jon A.; Ludwig, Larry L.; Wright, Maria C.
2009-01-01
To comply with lead-free legislation, many manufacturers have converted from tin-lead to pure tin finishes of electronic components. However, pure tin finishes have a greater propensity to grow tin whiskers than tin-lead finishes. Since tin whiskers present an electrical short circuit hazard in electronic components, simulations have been developed to quantify the risk of said short circuits occurring. Existing risk simulations make the assumption that when a free tin whisker has bridged two adjacent exposed electrical conductors, the result is an electrical short circuit. This conservative assumption is made because shorting is a random event that had an unknown probability associated with it. Note however that due to contact resistance electrical shorts may not occur at lower voltage levels. In our first article we developed an empirical probability model for tin whisker shorting. In this paper, we develop a more comprehensive empirical model using a refined experiment with a larger sample size, in which we studied the effect of varying voltage on the breakdown of the contact resistance which leads to a short circuit. From the resulting data we estimated the probability distribution of an electrical short, as a function of voltage.
Estimating background and threshold nitrate concentrations using probability graphs
Panno, S.V.; Kelly, W.R.; Martinsek, A.T.; Hackley, Keith C.
2006-01-01
Because of the ubiquitous nature of anthropogenic nitrate (NO 3-) in many parts of the world, determining background concentrations of NO3- in shallow ground water from natural sources is probably impossible in most environments. Present-day background must now include diffuse sources of NO3- such as disruption of soils and oxidation of organic matter, and atmospheric inputs from products of combustion and evaporation of ammonia from fertilizer and livestock waste. Anomalies can be defined as NO3- derived from nitrogen (N) inputs to the environment from anthropogenic activities, including synthetic fertilizers, livestock waste, and septic effluent. Cumulative probability graphs were used to identify threshold concentrations separating background and anomalous NO3-N concentrations and to assist in the determination of sources of N contamination for 232 spring water samples and 200 well water samples from karst aquifers. Thresholds were 0.4, 2.5, and 6.7 mg/L for spring water samples, and 0.1, 2.1, and 17 mg/L for well water samples. The 0.4 and 0.1 mg/L values are assumed to represent thresholds for present-day precipitation. Thresholds at 2.5 and 2.1 mg/L are interpreted to represent present-day background concentrations of NO3-N. The population of spring water samples with concentrations between 2.5 and 6.7 mg/L represents an amalgam of all sources of NO3- in the ground water basins that feed each spring; concentrations >6.7 mg/L were typically samples collected soon after springtime application of synthetic fertilizer. The 17 mg/L threshold (adjusted to 15 mg/L) for well water samples is interpreted as the level above which livestock wastes dominate the N sources. Copyright ?? 2006 The Author(s).
METAPHOR: Probability density estimation for machine learning based photometric redshifts
NASA Astrophysics Data System (ADS)
Amaro, V.; Cavuoti, S.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.
2017-06-01
We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).
Estimation of post-test probabilities by residents: Bayesian reasoning versus heuristics?
Hall, Stacey; Phang, Sen Han; Schaefer, Jeffrey P; Ghali, William; Wright, Bruce; McLaughlin, Kevin
2014-08-01
Although the process of diagnosing invariably begins with a heuristic, we encourage our learners to support their diagnoses by analytical cognitive processes, such as Bayesian reasoning, in an attempt to mitigate the effects of heuristics on diagnosing. There are, however, limited data on the use ± impact of Bayesian reasoning on the accuracy of disease probability estimates. In this study our objective was to explore whether Internal Medicine residents use a Bayesian process to estimate disease probabilities by comparing their disease probability estimates to literature-derived Bayesian post-test probabilities. We gave 35 Internal Medicine residents four clinical vignettes in the form of a referral letter and asked them to estimate the post-test probability of the target condition in each case. We then compared these to literature-derived probabilities. For each vignette the estimated probability was significantly different from the literature-derived probability. For the two cases with low literature-derived probability our participants significantly overestimated the probability of these target conditions being the correct diagnosis, whereas for the two cases with high literature-derived probability the estimated probability was significantly lower than the calculated value. Our results suggest that residents generate inaccurate post-test probability estimates. Possible explanations for this include ineffective application of Bayesian reasoning, attribute substitution whereby a complex cognitive task is replaced by an easier one (e.g., a heuristic), or systematic rater bias, such as central tendency bias. Further studies are needed to identify the reasons for inaccuracy of disease probability estimates and to explore ways of improving accuracy.
Estimation of probability of failure for damage-tolerant aerospace structures
NASA Astrophysics Data System (ADS)
Halbert, Keith
The majority of aircraft structures are designed to be damage-tolerant such that safe operation can continue in the presence of minor damage. It is necessary to schedule inspections so that minor damage can be found and repaired. It is generally not possible to perform structural inspections prior to every flight. The scheduling is traditionally accomplished through a deterministic set of methods referred to as Damage Tolerance Analysis (DTA). DTA has proven to produce safe aircraft but does not provide estimates of the probability of failure of future flights or the probability of repair of future inspections. Without these estimates maintenance costs cannot be accurately predicted. Also, estimation of failure probabilities is now a regulatory requirement for some aircraft. The set of methods concerned with the probabilistic formulation of this problem are collectively referred to as Probabilistic Damage Tolerance Analysis (PDTA). The goal of PDTA is to control the failure probability while holding maintenance costs to a reasonable level. This work focuses specifically on PDTA for fatigue cracking of metallic aircraft structures. The growth of a crack (or cracks) must be modeled using all available data and engineering knowledge. The length of a crack can be assessed only indirectly through evidence such as non-destructive inspection results, failures or lack of failures, and the observed severity of usage of the structure. The current set of industry PDTA tools are lacking in several ways: they may in some cases yield poor estimates of failure probabilities, they cannot realistically represent the variety of possible failure and maintenance scenarios, and they do not allow for model updates which incorporate observed evidence. A PDTA modeling methodology must be flexible enough to estimate accurately the failure and repair probabilities under a variety of maintenance scenarios, and be capable of incorporating observed evidence as it becomes available. This
Effect of Prior Probability Quality on Biased Time-Delay Estimation
Byram, Brett C.; Trahey, Gregg E.; Palmeri, Mark L.
2012-01-01
When properly constructed, biased estimators are known to produce lower mean-square errors than unbiased estimators. A biased estimator for the problem of ultrasound time-delay estimation was recently proposed. The proposed estimator incorporates knowledge of adjacent displacement estimates into the final estimate of a displacement. This is accomplished by using adjacent estimates to create a prior probability on the current estimate. Theory and simulations are used to investigate how the prior probability impacts the final estimate. The results show that with estimation quality on the order of the Cramer-Rao lower bound at adjacent locations, the local estimate in question should generally exceed the Cramer-Rao lower-bound limitations on performance of an unbiased estimator. The results as a whole provide additional confidence for the proposed estimator. PMID:22724313
The Estimation of Probability of Extreme Events for Small Samples
NASA Astrophysics Data System (ADS)
Pisarenko, V. F.; Rodkin, M. V.
2017-02-01
The most general approach to the study of rare extreme events is based on the extreme value theory. The fundamental General Extreme Value Distribution lies in the basis of this theory serving as the limit distribution for normalized maxima. It depends on three parameters. Usually the method of maximum likelihood (ML) is used for the estimation that possesses well-known optimal asymptotic properties. However, this method works efficiently only when sample size is large enough ( 200-500), whereas in many applications the sample size does not exceed 50-100. For such sizes, the advantage of the ML method in efficiency is not guaranteed. We have found that for this situation the method of statistical moments (SM) works more efficiently over other methods. The details of the estimation for small samples are studied. The SM is applied to the study of extreme earthquakes in three large virtual seismic zones, representing the regime of seismicity in subduction zones, intracontinental regime of seismicity, and the regime in mid-ocean ridge zones. The 68%-confidence domains for pairs of parameter (ξ, σ) and (σ, μ) are derived.
Thomas B. Lynch; Jean Nkouka; Michael M. Huebschmann; James M. Guldin
2003-01-01
A logistic equation is the basis for a model that predicts the probability of obtaining regeneration at specified densities. The density of regeneration (trees/ha) for which an estimate of probability is desired can be specified by means of independent variables in the model. When estimating parameters, the dependent variable is set to 1 if the regeneration density (...
Estimating The Probability Of Achieving Shortleaf Pine Regeneration At Variable Specified Levels
Thomas B. Lynch; Jean Nkouka; Michael M. Huebschmann; James M. Guldin
2002-01-01
A model was developed that can be used to estimate the probability of achieving regeneration at a variety of specified stem density levels. The model was fitted to shortleaf pine (Pinus echinata Mill.) regeneration data, and can be used to estimate the probability of achieving desired levels of regeneration between 300 and 700 stems per acre 9-l 0...
Almost efficient estimation of relative risk regression
Fitzmaurice, Garrett M.; Lipsitz, Stuart R.; Arriaga, Alex; Sinha, Debajyoti; Greenberg, Caprice; Gawande, Atul A.
2014-01-01
Relative risks (RRs) are often considered the preferred measures of association in prospective studies, especially when the binary outcome of interest is common. In particular, many researchers regard RRs to be more intuitively interpretable than odds ratios. Although RR regression is a special case of generalized linear models, specifically with a log link function for the binomial (or Bernoulli) outcome, the resulting log-binomial regression does not respect the natural parameter constraints. Because log-binomial regression does not ensure that predicted probabilities are mapped to the [0,1] range, maximum likelihood (ML) estimation is often subject to numerical instability that leads to convergence problems. To circumvent these problems, a number of alternative approaches for estimating RR regression parameters have been proposed. One approach that has been widely studied is the use of Poisson regression estimating equations. The estimating equations for Poisson regression yield consistent, albeit inefficient, estimators of the RR regression parameters. We consider the relative efficiency of the Poisson regression estimator and develop an alternative, almost efficient estimator for the RR regression parameters. The proposed method uses near-optimal weights based on a Maclaurin series (Taylor series expanded around zero) approximation to the true Bernoulli or binomial weight function. This yields an almost efficient estimator while avoiding convergence problems. We examine the asymptotic relative efficiency of the proposed estimator for an increase in the number of terms in the series. Using simulations, we demonstrate the potential for convergence problems with standard ML estimation of the log-binomial regression model and illustrate how this is overcome using the proposed estimator. We apply the proposed estimator to a study of predictors of pre-operative use of beta blockers among patients undergoing colorectal surgery after diagnosis of colon cancer. PMID
Betrie, Getnet D; Sadiq, Rehan; Nichol, Craig; Morin, Kevin A; Tesfamariam, Solomon
2016-01-15
Acid rock drainage (ARD) is a major environmental problem that poses significant environmental risks during and after mining activities. A new methodology for environmental risk assessment based on probability bounds and a geochemical speciation model (PHREEQC) is presented. The methodology provides conservative and non-conservative ways of estimating risk of heavy metals posed to selected endpoints probabilistically, while propagating data and parameter uncertainties throughout the risk assessment steps. The methodology is demonstrated at a minesite located in British Columbia, Canada. The result of the methodology for the case study minesite shows the fate-and-transport of heavy metals is well simulated in the mine environment. In addition, the results of risk characterization for the case study show that there is risk due to transport of heavy metals into the environment.
Estimating the Probability of a Diffusing Target Encountering a Stationary Sensor.
1985-07-01
7 RD-R1577 6- 44 ESTIMATING THE PROBABILITY OF A DIFFUSING TARGET i/i ENCOUNTERING R STATIONARY SENSOR(U) NAVAL POSTGRADUATE U SCHOOL MONTEREY CA...8217,: *.:.; - -*.. ,’.-,:;;’.’.. ’,. ,. .*.’.- 4 6 6- ..- .-,,.. : .-.;.- -. NPS55-85-013 NAVAL POSTGRADUATE SCHOOL Monterey, California ESTIMATING THE PROBABILITY OF A DIFFUSING TARGET...PROBABILITY OF A DIFFUSING Technical TARGET ENCOUNTERING A STATIONARY SENSOR S. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(@) S. CONTRACT OR GRANT NUMBER(a
Estimation of the size of a closed population when capture probabilities vary among animals
Burnham, K.P.; Overton, W.S.
1978-01-01
A model which allows capture probabilities to vary by individuals is introduced for multiple recapture studies n closed populations. The set of individual capture probabilities is modelled as a random sample from an arbitrary probability distribution over the unit interval. We show that the capture frequencies are a sufficient statistic. A nonparametric estimator of population size is developed based on the generalized jackknife; this estimator is found to be a linear combination of the capture frequencies. Finally, tests of underlying assumptions are presented.
A simulation model for estimating probabilities of defects in welds
Chapman, O.J.V.; Khaleel, M.A.; Simonen, F.A.
1996-12-01
In recent work for the US Nuclear Regulatory Commission in collaboration with Battelle Pacific Northwest National Laboratory, Rolls-Royce and Associates, Ltd., has adapted an existing model for piping welds to address welds in reactor pressure vessels. This paper describes the flaw estimation methodology as it applies to flaws in reactor pressure vessel welds (but not flaws in base metal or flaws associated with the cladding process). Details of the associated computer software (RR-PRODIGAL) are provided. The approach uses expert elicitation and mathematical modeling to simulate the steps in manufacturing a weld and the errors that lead to different types of weld defects. The defects that may initiate in weld beads include center cracks, lack of fusion, slag, pores with tails, and cracks in heat affected zones. Various welding processes are addressed including submerged metal arc welding. The model simulates the effects of both radiographic and dye penetrant surface inspections. Output from the simulation gives occurrence frequencies for defects as a function of both flaw size and flaw location (surface connected and buried flaws). Numerical results are presented to show the effects of submerged metal arc versus manual metal arc weld processes.
Overfitting, generalization, and MSE in class probability estimation with high-dimensional data.
Kim, Kyung In; Simon, Richard
2014-03-01
Accurate class probability estimation is important for medical decision making but is challenging, particularly when the number of candidate features exceeds the number of cases. Special methods have been developed for nonprobabilistic classification, but relatively little attention has been given to class probability estimation with numerous candidate variables. In this paper, we investigate overfitting in the development of regularized class probability estimators. We investigate the relation between overfitting and accurate class probability estimation in terms of mean square error. Using simulation studies based on real datasets, we found that some degree of overfitting can be desirable for reducing mean square error. We also introduce a mean square error decomposition for class probability estimation that helps clarify the relationship between overfitting and prediction accuracy.
Bayesian estimation of the probability of asbestos exposure from lung fiber counts.
Weichenthal, Scott; Joseph, Lawrence; Bélisle, Patrick; Dufresne, André
2010-06-01
Asbestos exposure is a well-known risk factor for various lung diseases, and when they occur, workmen's compensation boards need to make decisions concerning the probability the cause is work related. In the absence of a definitive work history, measures of short and long asbestos fibers as well as counts of asbestos bodies in the lung can be used as diagnostic tests for asbestos exposure. Typically, data from one or more lung samples are available to estimate the probability of asbestos exposure, often by comparing the values with those from a reference nonexposed population. As there is no gold standard measure, we explore a variety of latent class models that take into account the mixed discrete/continuous nature of the data, that each subject may provide data from more than one lung sample, and that the within-subject results across different samples may be correlated. Our methods can be useful to compensation boards in providing individual level probabilities of exposure based on available data, to researchers who are studying the test properties for the various measures used in this area, and more generally, to other test situations with similar data structure.
Regambal, Marci J; Alden, Lynn E
2012-09-01
Individuals with posttraumatic stress disorder (PTSD) are hypothesized to have a "sense of current threat." Perceived threat from the environment (i.e., external threat), can lead to overestimating the probability of the traumatic event reoccurring (Ehlers & Clark, 2000). However, it is unclear if external threat judgments are a pre-existing vulnerability for PTSD or a consequence of trauma exposure. We used trauma analog methodology to prospectively measure probability estimates of a traumatic event, and investigate how these estimates were related to cognitive processes implicated in PTSD development. 151 participants estimated the probability of being in car-accident related situations, watched a movie of a car accident victim, and then completed a measure of data-driven processing during the movie. One week later, participants re-estimated the probabilities, and completed measures of reexperiencing symptoms and symptom appraisals/reactions. Path analysis revealed that higher pre-existing probability estimates predicted greater data-driven processing which was associated with negative appraisals and responses to intrusions. Furthermore, lower pre-existing probability estimates and negative responses to intrusions were both associated with a greater change in probability estimates. Reexperiencing symptoms were predicted by negative responses to intrusions and, to a lesser degree, by greater changes in probability estimates. The undergraduate student sample may not be representative of the general public. The reexperiencing symptoms are less severe than what would be found in a trauma sample. Threat estimates present both a vulnerability and a consequence of exposure to a distressing event. Furthermore, changes in these estimates are associated with cognitive processes implicated in PTSD. Copyright © 2012 Elsevier Ltd. All rights reserved.
Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas
2014-07-01
Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077).
Walsh, Michael G; Haseeb, M A
2014-01-01
Toxocariasis is increasingly recognized as an important neglected infection of poverty (NIP) in developed countries, and may constitute the most important NIP in the United States (US) given its association with chronic sequelae such as asthma and poor cognitive development. Its potential public health burden notwithstanding, toxocariasis surveillance is minimal throughout the US and so the true burden of disease remains uncertain in many areas. The Third National Health and Nutrition Examination Survey conducted a representative serologic survey of toxocariasis to estimate the prevalence of infection in diverse US subpopulations across different regions of the country. Using the NHANES III surveillance data, the current study applied the predicted probabilities of toxocariasis to the sociodemographic composition of New York census tracts to estimate the local probability of infection across the city. The predicted probability of toxocariasis ranged from 6% among US-born Latino women with a university education to 57% among immigrant men with less than a high school education. The predicted probability of toxocariasis exhibited marked spatial variation across the city, with particularly high infection probabilities in large sections of Queens, and smaller, more concentrated areas of Brooklyn and northern Manhattan. This investigation is the first attempt at small-area estimation of the probability surface of toxocariasis in a major US city. While this study does not define toxocariasis risk directly, it does provide a much needed tool to aid the development of toxocariasis surveillance in New York City.
2014-01-01
Background Data on HCV-related cirrhosis progression are scarce in developing countries in general, and in Egypt in particular. The objective of this study was to estimate the probability of death and transition between different health stages of HCV (compensated cirrhosis, decompensated cirrhosis and hepatocellular carcinoma) for an Egyptian population of patients with HCV-related cirrhosis. Methods We used the “elicitation of expert opinions” method to obtain collective knowledge from a panel of 23 Egyptian experts (among whom 17 were hepatologists or gastroenterologists and 2 were infectiologists). The questionnaire was based on virtual medical cases and asked the experts to assess probability of death or probability of various cirrhosis complications. The design was a Delphi study: we attempted to obtain a consensus between experts via a series of questionnaires interspersed with group response feedback. Results We found substantial disparity between experts’ answers, and no consensus was reached at the end of the process. Moreover, we obtained high death probability and high risk of hepatocellular carcinoma. The annual transition probability to death was estimated at between 10.1% and 61.5% and the annual probability of occurrence of hepatocellular carcinoma was estimated at between 16.8% and 58.9% (depending on age, gender, time spent in cirrhosis and cirrhosis severity). Conclusions Our results show that eliciting expert opinions is not suited for determining the natural history of diseases due to practitioners’ difficulties in evaluating quantities. Cognitive bias occurring during this type of study might explain our results. PMID:24635942
Some considerations on the definition of risk based on concepts of systems theory and probability.
Andretta, Massimo
2014-07-01
The concept of risk has been applied in many modern science and technology fields. Despite its successes in many applicative fields, there is still not a well-established vision and universally accepted definition of the principles and fundamental concepts of the risk assessment discipline. As emphasized recently, the risk fields suffer from a lack of clarity on their scientific bases that can define, in a unique theoretical framework, the general concepts in the different areas of application. The aim of this article is to make suggestions for another perspective of risk definition that could be applied and, in a certain sense, generalize some of the previously known definitions (at least in the fields of technical and scientific applications). By drawing on my experience of risk assessment in different applicative situations (particularly in the risk estimation for major industrial accidents, and in the health and ecological risk assessment for contaminated sites), I would like to revise some general and foundational concepts of risk analysis in as consistent a manner as possible from the axiomatic/deductive point of view. My proposal is based on the fundamental concepts of the systems theory and of the probability. In this way, I try to frame, in a single, broad, and general theoretical context some fundamental concepts and principles applicable in many different fields of risk assessment. I hope that this article will contribute to the revitalization and stimulation of useful discussions and new insights into the key issues and theoretical foundations of risk assessment disciplines.
Easy probability estimation of the diagnosis of early axial spondyloarthritis by summing up scores.
Feldtkeller, Ernst; Rudwaleit, Martin; Zeidler, Henning
2013-09-01
Several sets of criteria for the diagnosis of axial SpA (including non-radiographic axial spondyloarthritis) have been proposed in the literature in which scores were attributed to relevant findings and the diagnosis requests a minimal sum of these scores. To quantitatively estimate the probability of axial SpA, multiplying the likelihood ratios of all relevant findings was proposed by Rudwaleit et al. in 2004. The objective of our proposal is to combine the advantages of both, i.e. to estimate the probability by summing up scores instead of multiplying likelihood ratios. An easy way to estimate the probability of axial spondyloarthritis is to use the logarithms of the likelihood ratios as scores attributed to relevant findings and to use the sum of these scores for the probability estimation. A list of whole-numbered scores for relevant findings is presented, and also threshold sum values necessary for a definite and for a probable diagnosis of axial SpA as well as a threshold below which the diagnosis of axial spondyloarthritis can be excluded. In a diagram, the probability of axial spondyloarthritis is given for sum values between these thresholds. By the method proposed, the advantages of both, the easy summing up of scores and the quantitative calculation of the diagnosis probability, are combined. Our method also makes it easier to estimate which additional tests are necessary to come to a definite diagnosis.
Shin, Seung Jun; Wu, Yichao
2014-07-01
This is a discussion of the papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.
Morise, A.P.; Duval, R.D. )
1989-11-15
To determine whether recent refinements in Bayesian methods have led to improved diagnostic ability, 3 methods using Bayes' theorem and the independence assumption for estimating posttest probability after exercise stress testing were compared. Each method differed in the number of variables considered in the posttest probability estimate (method A = 5, method B = 6 and method C = 15). Method C is better known as CADENZA. There were 436 patients (250 men and 186 women) who underwent stress testing (135 had concurrent thallium scintigraphy) followed within 2 months by coronary arteriography. Coronary artery disease ((CAD), at least 1 vessel with greater than or equal to 50% diameter narrowing) was seen in 169 (38%). Mean pretest probabilities using each method were not different. However, the mean posttest probabilities for CADENZA were significantly greater than those for method A or B (p less than 0.0001). Each decile of posttest probability was compared to the actual prevalence of CAD in that decile. At posttest probabilities less than or equal to 20%, there was underestimation of CAD. However, at posttest probabilities greater than or equal to 60%, there was overestimation of CAD by all methods, especially CADENZA. Comparison of sensitivity and specificity at every fifth percentile of posttest probability revealed that CADENZA was significantly more sensitive and less specific than methods A and B. Therefore, at lower probability thresholds, CADENZA was a better screening method. However, methods A or B still had merit as a means to confirm higher probabilities generated by CADENZA (especially greater than or equal to 60%).
Assessment of Methods for Estimating Risk to Birds from ...
The U.S. EPA Ecological Risk Assessment Support Center (ERASC) announced the release of the final report entitled, Assessment of Methods for Estimating Risk to Birds from Ingestion of Contaminated Grit Particles. This report evaluates approaches for estimating the probability of ingestion by birds of contaminated particles such as pesticide granules or lead particles (i.e. shot or bullet fragments). In addition, it presents an approach for using this information to estimate the risk of mortality to birds from ingestion of lead particles. Response to ERASC Request #16
Hubig, Michael; Muggenthaler, Holger; Mall, Gita
2014-05-01
Bayesian estimation applied to temperature based death time estimation was recently introduced as conditional probability distribution or CPD-method by Biermann and Potente. The CPD-method is useful, if there is external information that sets the boundaries of the true death time interval (victim last seen alive and found dead). CPD allows computation of probabilities for small time intervals of interest (e.g. no-alibi intervals of suspects) within the large true death time interval. In the light of the importance of the CPD for conviction or acquittal of suspects the present study identifies a potential error source. Deviations in death time estimates will cause errors in the CPD-computed probabilities. We derive formulae to quantify the CPD error as a function of input error. Moreover we observed the paradox, that in cases, in which the small no-alibi time interval is located at the boundary of the true death time interval, adjacent to the erroneous death time estimate, CPD-computed probabilities for that small no-alibi interval will increase with increasing input deviation, else the CPD-computed probabilities will decrease. We therefore advise not to use CPD if there is an indication of an error or a contra-empirical deviation in the death time estimates, that is especially, if the death time estimates fall out of the true death time interval, even if the 95%-confidence intervals of the estimate still overlap the true death time interval.
Seaver, D.A.; Stillwell, W.G.
1983-03-01
This report describes and evaluates several procedures for using expert judgment to estimate human-error probabilities (HEPs) in nuclear power plant operations. These HEPs are currently needed for several purposes, particularly for probabilistic risk assessments. Data do not exist for estimating these HEPs, so expert judgment can provide these estimates in a timely manner. Five judgmental procedures are described here: paired comparisons, ranking and rating, direct numerical estimation, indirect numerical estimation and multiattribute utility measurement. These procedures are evaluated in terms of several criteria: quality of judgments, difficulty of data collection, empirical support, acceptability, theoretical justification, and data processing. Situational constraints such as the number of experts available, the number of HEPs to be estimated, the time available, the location of the experts, and the resources available are discussed in regard to their implications for selecting a procedure for use.
The development of posterior probability models in risk-based integrity modeling.
Thodi, Premkumar N; Khan, Faisal I; Haddara, Mahmoud R
2010-03-01
There is a need for accurate modeling of mechanisms causing material degradation of equipment in process installation, to ensure safety and reliability of the equipment. Degradation mechanisms are stochastic processes. They can be best described using risk-based approaches. Risk-based integrity assessment quantifies the level of risk to which the individual components are subjected and provides means to mitigate them in a safe and cost-effective manner. The uncertainty and variability in structural degradations can be best modeled by probability distributions. Prior probability models provide initial description of the degradation mechanisms. As more inspection data become available, these prior probability models can be revised to obtain posterior probability models, which represent the current system and can be used to predict future failures. In this article, a rejection sampling-based Metropolis-Hastings (M-H) algorithm is used to develop posterior distributions. The M-H algorithm is a Markov chain Monte Carlo algorithm used to generate a sequence of posterior samples without actually knowing the normalizing constant. Ignoring the transient samples in the generated Markov chain, the steady state samples are rejected or accepted based on an acceptance criterion. To validate the estimated parameters of posterior models, analytical Laplace approximation method is used to compute the integrals involved in the posterior function. Results of the M-H algorithm and Laplace approximations are compared with conjugate pair estimations of known prior and likelihood combinations. The M-H algorithm provides better results and hence it is used for posterior development of the selected priors for corrosion and cracking.
Impact of probability estimation on frequency of urine culture requests in ambulatory settings.
Gul, Naheed; Quadri, Mujtaba
2012-07-01
To determine the perceptions of the medical community about urine culture in diagnosing urinary tract infections. The cross-sectional survey based of consecutive sampling was conducted at Shifa International Hospital, Islamabad, on 200 doctors, including medical students of the Shifa College of Medicine, from April to October 2010. A questionnaire with three common clinical scenarios of low, intermediate and high pre-test probability for urinary tract infection was used to assess the behaviour of the respondents to make a decision for urine culture test. The differences between the reference estimates and the respondents' estimates of pre- and post-test probability were assessed. The association of estimated probabilities with the number of tests ordered was also evaluated. The respondents were also asked about the cost effectiveness and safety of urine culture and sensitivity. Data was analysed using SPSS version 15. In low pre-test probability settings, the disease probability was over-estimated, suggesting the participants' inability to rule out the disease. The post-test probabilities were, however, under-estimated by the doctors as compared to the students. In intermediate and high pre-test probability settings, both over- and underestimation of probabilities were noticed. Doctors were more likely to consider ordering the test as the disease probability increased. Most of the respondents were of the opinion that urine culture was a cost-effective test and there was no associated potential harm. The wide variation in the clinical use of urine culture necessitates the formulation of appropriate guidelines for the diagnostic use of urine culture, and application of Bayesian probabilistic thinking to real clinical situations.
A double-observer approach for estimating detection probability and abundance from point counts
Nichols, J.D.; Hines, J.E.; Sauer, J.R.; Fallon, F.W.; Fallon, J.E.; Heglund, P.J.
2000-01-01
Although point counts are frequently used in ornithological studies, basic assumptions about detection probabilities often are untested. We apply a double-observer approach developed to estimate detection probabilities for aerial surveys (Cook and Jacobson 1979) to avian point counts. At each point count, a designated 'primary' observer indicates to another ('secondary') observer all birds detected. The secondary observer records all detections of the primary observer as well as any birds not detected by the primary observer. Observers alternate primary and secondary roles during the course of the survey. The approach permits estimation of observer-specific detection probabilities and bird abundance. We developed a set of models that incorporate different assumptions about sources of variation (e.g. observer, bird species) in detection probability. Seventeen field trials were conducted, and models were fit to the resulting data using program SURVIV. Single-observer point counts generally miss varying proportions of the birds actually present, and observer and bird species were found to be relevant sources of variation in detection probabilities. Overall detection probabilities (probability of being detected by at least one of the two observers) estimated using the double-observer approach were very high (>0.95), yielding precise estimates of avian abundance. We consider problems with the approach and recommend possible solutions, including restriction of the approach to fixed-radius counts to reduce the effect of variation in the effective radius of detection among various observers and to provide a basis for using spatial sampling to estimate bird abundance on large areas of interest. We believe that most questions meriting the effort required to carry out point counts also merit serious attempts to estimate detection probabilities associated with the counts. The double-observer approach is a method that can be used for this purpose.
On the estimation of risk associated with an attenuation prediction
NASA Technical Reports Server (NTRS)
Crane, R. K.
1992-01-01
Viewgraphs from a presentation on the estimation of risk associated with an attenuation prediction is presented. Topics covered include: link failure - attenuation exceeding a specified threshold for a specified time interval or intervals; risk - the probability of one or more failures during the lifetime of the link or during a specified accounting interval; the problem - modeling the probability of attenuation by rainfall to provide a prediction of the attenuation threshold for a specified risk; and an accounting for the inadequacy of a model or models.
Generalizations and Extensions of the Probability of Superiority Effect Size Estimator
ERIC Educational Resources Information Center
Ruscio, John; Gera, Benjamin Lee
2013-01-01
Researchers are strongly encouraged to accompany the results of statistical tests with appropriate estimates of effect size. For 2-group comparisons, a probability-based effect size estimator ("A") has many appealing properties (e.g., it is easy to understand, robust to violations of parametric assumptions, insensitive to outliers). We review…
Generalizations and Extensions of the Probability of Superiority Effect Size Estimator
ERIC Educational Resources Information Center
Ruscio, John; Gera, Benjamin Lee
2013-01-01
Researchers are strongly encouraged to accompany the results of statistical tests with appropriate estimates of effect size. For 2-group comparisons, a probability-based effect size estimator ("A") has many appealing properties (e.g., it is easy to understand, robust to violations of parametric assumptions, insensitive to outliers). We review…
Improving quality of sample entropy estimation for continuous distribution probability functions
NASA Astrophysics Data System (ADS)
Miśkiewicz, Janusz
2016-05-01
Entropy is a one of the key parameters characterizing state of system in statistical physics. Although, the entropy is defined for systems described by discrete and continuous probability distribution function (PDF), in numerous applications the sample entropy is estimated by a histogram, which, in fact, denotes that the continuous PDF is represented by a set of probabilities. Such a procedure may lead to ambiguities and even misinterpretation of the results. Within this paper, two possible general algorithms based on continuous PDF estimation are discussed in the application to the Shannon and Tsallis entropies. It is shown that the proposed algorithms may improve entropy estimation, particularly in the case of small data sets.
A removal model for estimating detection probabilities from point-count surveys
Farnsworth, G.L.; Pollock, K.H.; Nichols, J.D.; Simons, T.R.; Hines, J.E.; Sauer, J.R.
2000-01-01
We adapted a removal model to estimate detection probability during point count surveys. The model assumes one factor influencing detection during point counts is the singing frequency of birds. This may be true for surveys recording forest songbirds when most detections are by sound. The model requires counts to be divided into several time intervals. We used time intervals of 2, 5, and 10 min to develop a maximum-likelihood estimator for the detectability of birds during such surveys. We applied this technique to data from bird surveys conducted in Great Smoky Mountains National Park. We used model selection criteria to identify whether detection probabilities varied among species, throughout the morning, throughout the season, and among different observers. The overall detection probability for all birds was 75%. We found differences in detection probability among species. Species that sing frequently such as Winter Wren and Acadian Flycatcher had high detection probabilities (about 90%) and species that call infrequently such as Pileated Woodpecker had low detection probability (36%). We also found detection probabilities varied with the time of day for some species (e.g. thrushes) and between observers for other species. This method of estimating detectability during point count surveys offers a promising new approach to using count data to address questions of the bird abundance, density, and population trends.
Estimating Risk: Stereotype Amplification and the Perceived Risk of Criminal Victimization
QUILLIAN, LINCOLN; PAGER, DEVAH
2010-01-01
This paper considers the process by which individuals estimate the risk of adverse events, with particular attention to the social context in which risk estimates are formed. We compare subjective probability estimates of crime victimization to actual victimization experiences among respondents from the 1994 to 2002 waves of the Survey of Economic Expectations (Dominitz and Manski 2002). Using zip code identifiers, we then match these survey data to local area characteristics from the census. The results show that: (1) the risk of criminal victimization is significantly overestimated relative to actual rates of victimization or other negative events; (2) neighborhood racial composition is strongly associated with perceived risk of victimization, whereas actual victimization risk is driven by nonracial neighborhood characteristics; and (3) white respondents appear more strongly affected by racial composition than nonwhites in forming their estimates of risk. We argue these results support a model of stereotype amplification in the formation of risk estimates. Implications for persistent racial inequality are considered. PMID:20686631
Patrick L. Zimmerman; Greg C. Liknes
2010-01-01
Dot grids are often used to estimate the proportion of land cover belonging to some class in an aerial photograph. Interpreter misclassification is an often-ignored source of error in dot-grid sampling that has the potential to significantly bias proportion estimates. For the case when the true class of items is unknown, we present a maximum-likelihood estimator of...
Nichols, J.D.; Sauer, J.R.; Pollock, K.H.; Hestbeck, J.B.
1992-01-01
In stage-based demography, animals are often categorized into size (or mass) classes, and size-based probabilities of surviving and changing mass classes must be estimated before demographic analyses can be conducted. In this paper, we develop two procedures for the estimation of mass transition probabilities from capture-recapture data. The first approach uses a multistate capture-recapture model that is parameterized directly with the transition probabilities of interest. Maximum likelihood estimates are then obtained numerically using program SURVIV. The second approach involves a modification of Pollock's robust design. Estimation proceeds by conditioning on animals caught in a particualr class at time i, and then using closed models to estimate the number of these that are alive in other classes at i + 1. Both methods are illustrated by application to meadow vole, Microtus pennsylvanicus, capture-recapture data. The two methods produced reasonable estimates that were similar. Advantages of these two approaches include the directness of estimation, the absence of need for restrictive assumptions about the independence of survival and growth, the testability of assumptions, and the testability of related hypotheses of ecological interest (e.g., the hypothesis of temporal variation in transition probabilities).
Simon, N V; Levisky, J S; Shearer, D M; Morris, K C; Hansberry, P A
1988-06-01
We evaluated the predictiveness of sonographically estimated fetal weight as a function of the estimation of probability of having intrauterine growth retardation (IUGR) before obtaining an ultrasound scan (prior probability). The value of the estimated fetal weight resided more in its high specificity than in its sensitivity, hence in its ability to confirm that the fetus is normal. The predictiveness of the method was further enhanced when the fetal weight estimation was placed in the context of the prior probability of IUGR. In particular, the positive predictive value of the test as well as the likelihood of having a growth-retarded infant in spite of an estimated fetal weight within the normal range were considerably higher as the prior probability of IUGR increased. Since the obstetrician using all available evidence is likely to form a rather good estimate of the possibility of IUGR before ordering a scan, this improvement in the predictiveness of estimated fetal weight through a Bayesian approach can be advantageously applied to ultrasound analysis and can effectively support clinical decision making.
Climate-informed flood risk estimation
NASA Astrophysics Data System (ADS)
Troy, T.; Devineni, N.; Lima, C.; Lall, U.
2013-12-01
Currently, flood risk assessments are typically tied to a peak flow event that has an associated return period and inundation extent. This method is convenient: based on a historical record of annual maximum flows, a return period can be calculated with some assumptions about the probability distribution and stationarity. It is also problematic in its stationarity assumption, reliance on relatively short records, and treating flooding as a random event disconnected from large-scale climate processes. Recognizing these limitations, we have developed a new approach to flood risk assessment that connects climate variability, precipitation dynamics, and flood modeling to estimate the likelihood of flooding. To provide more robust, long time series of precipitation, we used stochastic weather generator models to simulate the rainfall fields. The method uses a k-nearest neighbor resampling algorithm in conjunction with a non-parametric empirical copulas based simulation strategy to reproduce the temporal and spatial dynamics, respectively. Climate patterns inform the likelihood of heavy rainfall in the model. For example, ENSO affects the likelihood of wet or dry years in Australia, and this is incorporated in the model. The stochastic simulations are then used to drive a cascade of models to predict flood inundation. Runoff is generated by the Variable Infiltration Capacity (VIC) model, fed into a full kinematic wave routing model at high resolution, and the kinematic wave is used as a boundary condition to predict flood inundation using a coupled storage cell model. Combining the strengths of a stochastic model for rainfall and a physical model for flood prediction allows us to overcome the limitations of traditional flood risk assessment and provide robust estimates of flood risk.
Nonparametric maximum likelihood estimation of probability densities by penalty function methods
NASA Technical Reports Server (NTRS)
Demontricher, G. F.; Tapia, R. A.; Thompson, J. R.
1974-01-01
When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates.
Hoblitt, Richard P.; Scott, William E.
2011-01-01
In response to a request from the U.S. Department of Energy, we estimate the thickness of tephra accumulation that has an annual probability of 1 in 10,000 of being equaled or exceeded at the Hanford Site in south-central Washington State, where a project to build the Tank Waste Treatment and Immobilization Plant is underway. We follow the methodology of a 1987 probabilistic assessment of tephra accumulation in the Pacific Northwest. For a given thickness of tephra, we calculate the product of three probabilities: (1) the annual probability of an eruption producing 0.1 km3 (bulk volume) or more of tephra, (2) the probability that the wind will be blowing toward the Hanford Site, and (3) the probability that tephra accumulations will equal or exceed the given thickness at a given distance. Mount St. Helens, which lies about 200 km upwind from the Hanford Site, has been the most prolific source of tephra fallout among Cascade volcanoes in the recent geologic past and its annual eruption probability based on this record (0.008) dominates assessment of future tephra falls at the site. The probability that the prevailing wind blows toward Hanford from Mount St. Helens is 0.180. We estimate exceedance probabilities of various thicknesses of tephra fallout from an analysis of 14 eruptions of the size expectable from Mount St. Helens and for which we have measurements of tephra fallout at 200 km. The result is that the estimated thickness of tephra accumulation that has an annual probability of 1 in 10,000 of being equaled or exceeded is about 10 centimeters. It is likely that this thickness is a maximum estimate because we used conservative estimates of eruption and wind probabilities and because the 14 deposits we used probably provide an over-estimate. The use of deposits in this analysis that were mostly compacted by the time they were studied and measured implies that the bulk density of the tephra fallout we consider here is in the range of 1,000-1,250 kg/m3. The
A removal model for estimating detection probabilities from point-count surveys
Farnsworth, G.L.; Pollock, K.H.; Nichols, J.D.; Simons, T.R.; Hines, J.E.; Sauer, J.R.
2002-01-01
Use of point-count surveys is a popular method for collecting data on abundance and distribution of birds. However, analyses of such data often ignore potential differences in detection probability. We adapted a removal model to directly estimate detection probability during point-count surveys. The model assumes that singing frequency is a major factor influencing probability of detection when birds are surveyed using point counts. This may be appropriate for surveys in which most detections are by sound. The model requires counts to be divided into several time intervals. Point counts are often conducted for 10 min, where the number of birds recorded is divided into those first observed in the first 3 min, the subsequent 2 min, and the last 5 min. We developed a maximum-likelihood estimator for the detectability of birds recorded during counts divided into those intervals. This technique can easily be adapted to point counts divided into intervals of any length. We applied this method to unlimited-radius counts conducted in Great Smoky Mountains National Park. We used model selection criteria to identify whether detection probabilities varied among species, throughout the morning, throughout the season, and among different observers. We found differences in detection probability among species. Species that sing frequently such as Winter Wren (Troglodytes troglodytes) and Acadian Flycatcher (Empidonax virescens) had high detection probabilities (~90%) and species that call infrequently such as Pileated Woodpecker (Dryocopus pileatus) had low detection probability (36%). We also found detection probabilities varied with the time of day for some species (e.g. thrushes) and between observers for other species. We used the same approach to estimate detection probability and density for a subset of the observations with limited-radius point counts.
Estimating probabilities of reservoir storage for the upper Delaware River basin
Hirsch, Robert M.
1981-01-01
A technique for estimating conditional probabilities of reservoir system storage is described and applied to the upper Delaware River Basin. The results indicate that there is a 73 percent probability that the three major New York City reservoirs (Pepacton, Cannonsville, and Neversink) would be full by June 1, 1981, and only a 9 percent probability that storage would return to the ' drought warning ' sector of the operations curve sometime in the next year. In contrast, if restrictions are lifted and there is an immediate return to normal operating policies, the probability of the reservoir system being full by June 1 is 37 percent and the probability that storage would return to the ' drought warning ' sector in the next year is 30 percent. (USGS)
Multifractals embedded in short time series: An unbiased estimation of probability moment
NASA Astrophysics Data System (ADS)
Qiu, Lu; Yang, Tianguang; Yin, Yanhua; Gu, Changgui; Yang, Huijie
2016-12-01
An exact estimation of probability moments is the base for several essential concepts, such as the multifractals, the Tsallis entropy, and the transfer entropy. By means of approximation theory we propose a new method called factorial-moment-based estimation of probability moments. Theoretical prediction and computational results show that it can provide us an unbiased estimation of the probability moments of continuous order. Calculations on probability redistribution model verify that it can extract exactly multifractal behaviors from several hundred recordings. Its powerfulness in monitoring evolution of scaling behaviors is exemplified by two empirical cases, i.e., the gait time series for fast, normal, and slow trials of a healthy volunteer, and the closing price series for Shanghai stock market. By using short time series with several hundred lengths, a comparison with the well-established tools displays significant advantages of its performance over the other methods. The factorial-moment-based estimation can evaluate correctly the scaling behaviors in a scale range about three generations wider than the multifractal detrended fluctuation analysis and the basic estimation. The estimation of partition function given by the wavelet transform modulus maxima has unacceptable fluctuations. Besides the scaling invariance focused in the present paper, the proposed factorial moment of continuous order can find its various uses, such as finding nonextensive behaviors of a complex system and reconstructing the causality relationship network between elements of a complex system.
Flint, Alexander C; Rao, Vivek A; Chan, Sheila L; Cullen, Sean P; Faigeles, Bonnie S; Smith, Wade S; Bath, Philip M; Wahlgren, Nils; Ahmed, Niaz; Donnan, Geoff A; Johnston, S Claiborne
2015-08-01
The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of outcome probability should be possible by computing the logistic equation with patient-specific continuous variables. We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke - Monitoring Study to develop and validate the performance of a model-derived estimation of outcome probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test. The THRIVE-c model performed similarly in the randomly chosen development cohort (n = 6194, area under the curve = 0·786, 95% confidence interval 0·774-0·798) and validation cohort (n = 6013, area under the curve = 0·784, 95% confidence interval 0·772-0·796) (P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777-0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737-0·755) (P < 0·001). By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, outcomes at the individual patient level are more accurately estimated. Given the widespread
Moderate- and Large- Deviation Probabilities in Actuarial Risk Theory,
1988-06-01
Introduction to the Theory of Large Deviations, Springer-Verlag, New York. Thorin, 0. (1982), Probabilities of ruin, Scand. Actuar . Jour. 65-102. 25 *% 0 I S 0 S 3 9 I bJ\\~ S S I’ C..., S L2JI1C S ~
Estimating nest detection probabilities for white-winged dove nest transects in Tamaulipas, Mexico
Nichols, J.D.; Tomlinson, R.E.; Waggerman, G.
1986-01-01
Nest transects in nesting colonies provide one source of information on White-winged Dove (Zenaida asiatica asiatica) population status and reproduction. Nests are counted along transects using standardized field methods each year in Texas and northeastern Mexico by personnel associated with Mexico's Office of Flora and Fauna, the Texas Parks and Wildlife Department, and the U.S. Fish and Wildlife Service. Nest counts on transects are combined with information on the size of nesting colonies to estimate total numbers of nests in sampled colonies. Historically, these estimates have been based on the actual nest counts on transects and thus have required the assumption that all nests lying within transect boundaries are detected (seen) with a probability of one. Our objectives were to test the hypothesis that nest detection probability is one and, if rejected, to estimate this probability.
Estimating the Probability of Asteroid Collision with the Earth by the Monte Carlo Method
NASA Astrophysics Data System (ADS)
Chernitsov, A. M.; Tamarov, V. A.; Barannikov, E. A.
2016-09-01
The commonly accepted method of estimating the probability of asteroid collision with the Earth is investigated on an example of two fictitious asteroids one of which must obviously collide with the Earth and the second must pass by at a dangerous distance from the Earth. The simplest Kepler model of motion is used. Confidence regions of asteroid motion are estimated by the Monte Carlo method. Two variants of constructing the confidence region are considered: in the form of points distributed over the entire volume and in the form of points mapped onto the boundary surface. The special feature of the multidimensional point distribution in the first variant of constructing the confidence region that can lead to zero probability of collision for bodies that collide with the Earth is demonstrated. The probability estimates obtained for even considerably smaller number of points in the confidence region determined by its boundary surface are free from this disadvantage.
The estimated lifetime probability of acquiring human papillomavirus in the United States.
Chesson, Harrell W; Dunne, Eileen F; Hariri, Susan; Markowitz, Lauri E
2014-11-01
Estimates of the lifetime probability of acquiring human papillomavirus (HPV) can help to quantify HPV incidence, illustrate how common HPV infection is, and highlight the importance of HPV vaccination. We developed a simple model, based primarily on the distribution of lifetime numbers of sex partners across the population and the per-partnership probability of acquiring HPV, to estimate the lifetime probability of acquiring HPV in the United States in the time frame before HPV vaccine availability. We estimated the average lifetime probability of acquiring HPV among those with at least 1 opposite sex partner to be 84.6% (range, 53.6%-95.0%) for women and 91.3% (range, 69.5%-97.7%) for men. Under base case assumptions, more than 80% of women and men acquire HPV by age 45 years. Our results are consistent with estimates in the existing literature suggesting a high lifetime probability of HPV acquisition and are supported by cohort studies showing high cumulative HPV incidence over a relatively short period, such as 3 to 5 years.
Austin, Peter C
2016-12-30
Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three variance estimation methods: (i) a naïve model-based variance estimator; (ii) a robust sandwich-type variance estimator; and (iii) a bootstrap variance estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Using of bayesian networks to estimate the probability of "NATECH" scenario occurrence
NASA Astrophysics Data System (ADS)
Dobes, Pavel; Dlabka, Jakub; Jelšovská, Katarína; Polorecká, Mária; Baudišová, Barbora; Danihelka, Pavel
2015-04-01
In the twentieth century, implementation of Bayesian statistics and probability was not much used (may be it wasn't a preferred approach) in the area of natural and industrial risk analysis and management. Neither it was used within analysis of so called NATECH accidents (chemical accidents triggered by natural events, such as e.g. earthquakes, floods, lightning etc.; ref. E. Krausmann, 2011, doi:10.5194/nhess-11-921-2011). Main role, from the beginning, played here so called "classical" frequentist probability (ref. Neyman, 1937), which rely up to now especially on the right/false results of experiments and monitoring and didn't enable to count on expert's beliefs, expectations and judgements (which is, on the other hand, one of the once again well known pillars of Bayessian approach to probability). In the last 20 or 30 years, there is possible to observe, through publications and conferences, the Renaissance of Baysssian statistics into many scientific disciplines (also into various branches of geosciences). The necessity of a certain level of trust in expert judgment within risk analysis is back? After several decades of development on this field, it could be proposed following hypothesis (to be checked): "We couldn't estimate probabilities of complex crisis situations and their TOP events (many NATECH events could be classified as crisis situations or emergencies), only by classical frequentist approach, but also by using of Bayessian approach (i.e. with help of prestaged Bayessian Network including expert belief and expectation as well as classical frequentist inputs). Because - there is not always enough quantitative information from monitoring of historical emergencies, there could be several dependant or independant variables necessary to consider and in generally - every emergency situation always have a little different run." In this topic, team of authors presents its proposal of prestaged typized Bayessian network model for specified NATECH scenario
Methodology for estimating extreme winds for probabilistic risk assessments
Ramsdell, J.V.; Elliott, D.L.; Holladay, C.G.; Hubbe, J.M.
1986-10-01
The US Nuclear Reguulatory Commission (NRC) assesses the risks associated with nuclear faciliies using techniques that fall under a generic name of Probabilistic Risk Assessment. In these assessments, potential accident sequences are traced from initiating event to final outcome. At each step of the sequence, a probability of occurrence is assigned to each available alternative. Ultimately, the probability of occurrence of each possible outcome is determined from the probabilities assigned to the initiating events and the alternative paths. Extreme winds are considered in these sequences. As a result, it is necessary to estimate extreme wind probabilities as low as 10/sup -7/yr/sup -1/. When the NRC staff is called on to provide extreme wind estimates, the staff is likely to be subjected to external time and funding constraints. These constraints dictate that the estimates be based on readily available wind data. In general, readily available data will be limited to the data provided by the facility applicant or licensee and the data archived at the National Climatic Data Center in Asheville, North Carolina. This report describes readily available data that can be used in estimating extreme wind probabilities, procedures of screening the data to eliminate erroneous values and for adjusting data to compensate for differences in data collection methods, and statistical methods for making extreme wind estimates. Supporting technical details are presented in several appendices. Estimation of extreme wind probabilities at a given location involves many subjective decisions. The procedures described do not eliminate all of the subjectivity, but they do increase the reproducibility of the analysis. They provide consistent methods for determining probabilities given a set of subjective decisions. By following these procedures, subjective decisions can be identified and documented.
NASA Technical Reports Server (NTRS)
Long, Jacquelyn E.
1992-01-01
Software reliability has become increasingly important, especially in life-critical situations. The ability to measure the results of testing and to quantify software reliability is needed. If this is accomplished, a certain minimum amount of reliability for a piece of software can be specified, and testing and/or other analysis may be done until that minimum number has been attained. There are many models for estimating software reliability. The accuracy of these models has been challenged and many revisions for the models and recalibration techniques have been devised. Of particular interest is the method of estimating the probability of failure of software when no failures have yet occurred in its current version as described by Miller. This model uses black box testing with formulae based on Bayesian estimation. The focus is on three interrelated issues: estimating the probability of failure when testing has revealed no errors; modifying this estimation when the input use distribution does not match the test distribution; and combining the results from random testing with other relevant information to obtain a possibly more accurate estimate of the probability of failure. Obtaining relevant information about the software and combining the results for a better estimate for the Miller model are discussed.
Delavande, Adeline; Rohwedder, Susann
2013-01-01
Cross-country comparisons of differential survival by socioeconomic status (SES) are useful in many domains. Yet, to date, such studies have been rare. Reliably estimating differential survival in a single country has been challenging because it requires rich panel data with a large sample size. Cross-country estimates have proven even more difficult because the measures of SES need to be comparable internationally. We present an alternative method for acquiring information on differential survival by SES. Rather than using observations of actual survival, we relate individuals’ subjective probabilities of survival to SES variables in cross section. To show that subjective survival probabilities are informative proxies for actual survival when estimating differential survival, we compare estimates of differential survival based on actual survival with estimates based on subjective probabilities of survival for the same sample. The results are remarkably similar. We then use this approach to compare differential survival by SES for 10 European countries and the United States. Wealthier people have higher survival probabilities than those who are less wealthy, but the strength of the association differs across countries. Nations with a smaller gradient appear to be Belgium, France, and Italy, while the United States, England, and Sweden appear to have a larger gradient. PMID:22042664
Ambresin, Anne-Emmanuelle; Otjes, Christiaan P; Patton, George C; Sawyer, Susan M; Thuraisingam, Sharmala; English, Dallas R; Haller, Dagmar M; Sanci, Lena A
2017-09-01
The purpose of the study is to investigate whether a training intervention increases general practitioners' (GPs) detection sensitivity for probable mental disorders in young people. Forty general practices were randomized to an intervention (29 GPs) or comparison arm (49 GPs). Intervention GPs participated in 9 hours of interactive training on youth-friendly care, psychosocial health risk screening, and responding to risk-taking behavior with motivational interviewing approaches, followed by practice visits assisting with integration of screening processes and tools. Youth aged 14-24 years attending GPs underwent a computer-assisted telephone interview about their consultation and psychosocial health risks. Having a "probable mental disorder" was defined as either scoring high on Kessler's scale of psychological distress (K10) or self-perceived mental illness. Other definitions tested were high K10; self-perceived mental illness; and high K10 and self-perceived mental illness. Psychosocial health risk screening rates, detection sensitivity, and other accuracy parameters (specificity, positive predictive value, and negative predictive value) were estimated. GPs' detection sensitivity improved after the intervention if having probable mental disorder was defined as high K10 score and self-perceived mental illness (odds ratio: 2.81; 95% confidence interval: 1.23-6.42). There was no significant difference in sensitivity of GPs' detection for our preferred definition, high K10 or self-perceived mental illness (.37 in both; odds ratio: .93; 95% confidence interval: .47-1.83), and detection accuracy was comparable (specificity: .84 vs. .87, positive predictive values: .54 vs. .60, and negative predictive values: .72 vs. .72). Improving recognition of mental disorder among young people attending primary care is likely to require a multifaceted approach targeting young people and GPs. Copyright © 2017 Society for Adolescent Health and Medicine. Published by Elsevier Inc
NASA Astrophysics Data System (ADS)
Anuwar, Muhammad Hafidz; Jaffar, Maheran Mohd
2017-08-01
This paper provides an overview for the assessment of credit risk specific to the banks. In finance, risk is a term to reflect the potential of financial loss. The risk of default on loan may increase when a company does not make a payment on that loan when the time comes. Hence, this framework analyses the KMV-Merton model to estimate the probabilities of default for Malaysian listed companies. In this way, banks can verify the ability of companies to meet their loan commitments in order to overcome bad investments and financial losses. This model has been applied to all Malaysian listed companies in Bursa Malaysia for estimating the credit default probabilities of companies and compare with the rating given by the rating agency, which is RAM Holdings Berhad to conform to reality. Then, the significance of this study is a credit risk grade is proposed by using the KMV-Merton model for the Malaysian listed companies.
van der Hoop, Julie M; Vanderlaan, Angelia S M; Taggart, Christopher T
2012-10-01
Vessel strikes are the primary source of known mortality for the endangered North Atlantic right whale (Eubalaena glacialis). Multi-institutional efforts to reduce mortality associated with vessel strikes include vessel-routing amendments such as the International Maritime Organization voluntary "area to be avoided" (ATBA) in the Roseway Basin right whale feeding habitat on the southwestern Scotian Shelf. Though relative probabilities of lethal vessel strikes have been estimated and published, absolute probabilities remain unknown. We used a modeling approach to determine the regional effect of the ATBA, by estimating reductions in the expected number of lethal vessel strikes. This analysis differs from others in that it explicitly includes a spatiotemporal analysis of real-time transits of vessels through a population of simulated, swimming right whales. Combining automatic identification system (AIS) vessel navigation data and an observationally based whale movement model allowed us to determine the spatial and temporal intersection of vessels and whales, from which various probability estimates of lethal vessel strikes are derived. We estimate one lethal vessel strike every 0.775-2.07 years prior to ATBA implementation, consistent with and more constrained than previous estimates of every 2-16 years. Following implementation, a lethal vessel strike is expected every 41 years. When whale abundance is held constant across years, we estimate that voluntary vessel compliance with the ATBA results in an 82% reduction in the per capita rate of lethal strikes; very similar to a previously published estimate of 82% reduction in the relative risk of a lethal vessel strike. The models we developed can inform decision-making and policy design, based on their ability to provide absolute, population-corrected, time-varying estimates of lethal vessel strikes, and they are easily transported to other regions and situations.
Estimating probability densities from short samples: A parametric maximum likelihood approach
NASA Astrophysics Data System (ADS)
Dudok de Wit, T.; Floriani, E.
1998-10-01
A parametric method similar to autoregressive spectral estimators is proposed to determine the probability density function (PDF) of a random set. The method proceeds by maximizing the likelihood of the PDF, yielding estimates that perform equally well in the tails as in the bulk of the distribution. It is therefore well suited for the analysis of short sets drawn from smooth PDF's and stands out by the simplicity of its computational scheme. Its advantages and limitations are discussed.
Crawford, John R; Garthwaite, Paul H; Betkowska, Karolina
2009-05-01
Most neuropsychologists are aware that, given the specificity and sensitivity of a test and an estimate of the base rate of a disorder, Bayes' theorem can be used to provide a post-test probability for the presence of the disorder given a positive test result (and a post-test probability for the absence of a disorder given a negative result). However, in the standard application of Bayes' theorem the three quantities (sensitivity, specificity, and the base rate) are all treated as fixed, known quantities. This is very unrealistic as there may be considerable uncertainty over these quantities and therefore even greater uncertainty over the post-test probability. Methods of obtaining interval estimates on the specificity and sensitivity of a test are set out. In addition, drawing and extending upon work by Mossman and Berger (2001), a Monte Carlo method is used to obtain interval estimates for post-test probabilities. All the methods have been implemented in a computer program, which is described and made available (www.abdn.ac.uk/~psy086/dept/BayesPTP.htm). When objective data on the base rate are lacking (or have limited relevance to the case at hand) the program elicits opinion for the pre-test probability.
Alvares, N; Lambert, H
2007-02-07
The Federal Aviation Administration (FAA) identified 17 accidents that may have resulted from fuel tank explosions on commercial aircraft from 1959 to 2001. Seven events involved JP 4 or JP 4/Jet A mixtures that are no longer used for commercial aircraft fuel. The remaining 10 events involved Jet A or Jet A1 fuels that are in current use by the commercial aircraft industry. Four fuel tank explosions occurred in center wing tanks (CWTs) where on-board appliances can potentially transfer heat to the tank. These tanks are designated as ''Heated Center Wing Tanks'' (HCWT). Since 1996, the FAA has significantly increased the rate at which it has mandated airworthiness directives (ADs) directed at elimination of ignition sources. This effort includes the adoption, in 2001, of Special Federal Aviation Regulation 88 of 14 CFR part 21 (SFAR 88 ''Fuel Tank System Fault Tolerance Evaluation Requirements''). This paper addresses SFAR 88 effectiveness in reducing HCWT ignition source probability. Our statistical analysis, relating the occurrence of both on-ground and in-flight HCWT explosions to the cumulative flight hours of commercial passenger aircraft containing HCWT's reveals that the best estimate of HCWT explosion rate is 1 explosion in 1.4 x 10{sup 8} flight hours. Based on an analysis of SFAR 88 by Sandia National Laboratories and our independent analysis, SFAR 88 reduces current risk of historical HCWT explosion by at least a factor of 10, thus meeting an FAA risk criteria of 1 accident in billion flight hours. This paper also surveys and analyzes parameters for Jet A fuel ignition in HCWT's. Because of the paucity of in-flight HCWT explosions, we conclude that the intersection of the parameters necessary and sufficient to result in an HCWT explosion with sufficient overpressure to rupture the HCWT is extremely rare.
Estimating stage-specific daily survival probabilities of nests when nest age is unknown
Stanley, T.R.
2004-01-01
Estimation of daily survival probabilities of nests is common in studies of avian populations. Since the introduction of Mayfield's (1961, 1975) estimator, numerous models have been developed to relax Mayfield's assumptions and account for biologically important sources of variation. Stanley (2000) presented a model for estimating stage-specific (e.g. incubation stage, nestling stage) daily survival probabilities of nests that conditions on “nest type” and requires that nests be aged when they are found. Because aging nests typically requires handling the eggs, there may be situations where nests can not or should not be aged and the Stanley (2000) model will be inapplicable. Here, I present a model for estimating stage-specific daily survival probabilities that conditions on nest stage for active nests, thereby obviating the need to age nests when they are found. Specifically, I derive the maximum likelihood function for the model, evaluate the model's performance using Monte Carlo simulations, and provide software for estimating parameters (along with an example). For sample sizes as low as 50 nests, bias was small and confidence interval coverage was close to the nominal rate, especially when a reduced-parameter model was used for estimation.
Brand, Matthias; Schiebener, Johannes; Pertl, Marie-Theres; Delazer, Margarete
2014-01-01
Recent models on decision making under risk conditions have suggested that numerical abilities are important ingredients of advantageous decision-making performance, but empirical evidence is still limited. The results of our first study show that logical reasoning and basic mental calculation capacities predict ratio processing and that ratio processing predicts decision making under risk. In the second study, logical reasoning together with executive functions predicted probability processing (numeracy and probability knowledge), and probability processing predicted decision making under risk. These findings suggest that increasing an individual's understanding of ratios and probabilities should lead to more advantageous decisions under risk conditions.
Duchesne, Simon; Mouiha, Abderazzak
2011-01-01
We propose a novel morphological factor estimate from structural MRI for disease state evaluation. We tested this methodology in the context of Alzheimer's disease (AD) with 349 subjects. The method consisted in (a) creating a reference MRI feature eigenspace using intensity and local volume change data from 149 healthy, young subjects; (b) projecting MRI data from 75 probable AD, 76 controls (CTRL), and 49 Mild Cognitive Impairment (MCI) in that space; (c) extracting high-dimensional discriminant functions; (d) calculating a single morphological factor based on various models. We used this methodology in leave-one-out experiments to (1) confirm the superiority of an inverse-squared model over other approaches; (2) obtain accuracy estimates for the discrimination of probable AD from CTRL (90%) and the prediction of conversion of MCI subjects to probable AD (79.4%). PMID:21755033
Mediators of the Availability Heuristic in Probability Estimates of Future Events.
ERIC Educational Resources Information Center
Levi, Ariel S.; Pryor, John B.
Individuals often estimate the probability of future events by the ease with which they can recall or cognitively construct relevant instances. Previous research has not precisely identified the cognitive processes mediating this "availability heuristic." Two potential mediators (imagery of the event, perceived reasons or causes for the…
Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices
Haiganoush K. Preisler; Shyh-Chin Chen; Francis Fujioka; John W. Benoit; Anthony L. Westerling
2008-01-01
The National Fire Danger Rating System indices deduced from a regional simulation weather model were used to estimate probabilities and numbers of large fire events on monthly and 1-degree grid scales. The weather model simulations and forecasts are ongoing experimental products from the Experimental Climate Prediction Center at the Scripps Institution of Oceanography...
2012-09-01
incorporates macro economic and policy level information. In the first step the conditional probabilities of staying or leaving the Navy are estimated...accommodates time dependent information, cohort information, censoring problems with the data as well as incorporating macro economic and policy level ...1 Introducing the Individual Level Information (Covariates
Waits, L P; Luikart, G; Taberlet, P
2001-01-01
Individual identification using DNA fingerprinting methods is emerging as a critical tool in conservation genetics and molecular ecology. Statistical methods that estimate the probability of sampling identical genotypes using theoretical equations generally assume random associations between alleles within and among loci. These calculations are probably inaccurate for many animal and plant populations due to population substructure. We evaluated the accuracy of a probability of identity (P(ID)) estimation by comparing the observed and expected P(ID), using large nuclear DNA microsatellite data sets from three endangered species: the grey wolf (Canis lupus), the brown bear (Ursus arctos), and the Australian northern hairy-nosed wombat (Lasiorinyus krefftii). The theoretical estimates of P(ID) were consistently lower than the observed P(ID), and can differ by as much as three orders of magnitude. To help researchers and managers avoid potential problems associated with this bias, we introduce an equation for P(ID) between sibs. This equation provides an estimator that can be used as a conservative upper bound for the probability of observing identical multilocus genotypes between two individuals sampled from a population. We suggest computing the actual observed P(ID) when possible and give general guidelines for the number of codominant and dominant marker loci required to achieve a reasonably low P(ID) (e.g. 0.01-0.0001).
2013-03-01
Presented to the Faculty Department of Operational Sciences Graduate School of Engineering and Management Air Force Institute of Technology Air...University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Science in Operations ...to estimate these unknown multinomial success probabilities, , for each of the systems [17]. Bechhofer and Sobel [18] made use of multinomial
NASA Astrophysics Data System (ADS)
Frigm, R.; Johnson, L.
The Probability of Collision (Pc) has become a universal metric and statement of on-orbit collision risk. Although several flavors of the computation exist and are well-documented in the literature, the basic calculation requires the same input: estimates for the position, position uncertainty, and sizes of the two objects involved. The Pc is used operationally to make decisions on whether a given conjunction poses significant collision risk to the primary object (or space asset of concern). It is also used to determine necessity and degree of mitigative action (typically in the form of an orbital maneuver) to be performed. The predicted post-maneuver Pc also informs the maneuver planning process into regarding the timing, direction, and magnitude of the maneuver needed to mitigate the collision risk. Although the data sources, techniques, decision calculus, and workflows vary for different agencies and organizations, they all have a common thread. The standard conjunction assessment and collision risk concept of operations (CONOPS) predicts conjunctions, assesses the collision risk (typically, via the Pc), and plans and executes avoidance activities for conjunctions as a discrete events. As the space debris environment continues to increase and improvements are made to remote sensing capabilities and sensitivities to detect, track, and predict smaller debris objects, the number of conjunctions will in turn continue to increase. The expected order-of-magnitude increase in the number of predicted conjunctions will challenge the paradigm of treating each conjunction as a discrete event. The challenge will not be limited to workload issues, such as manpower and computing performance, but also the ability for satellite owner/operators to successfully execute their mission while also managing on-orbit collision risk. Executing a propulsive maneuver occasionally can easily be absorbed into the mission planning and operations tempo; whereas, continuously planning evasive
NASA Astrophysics Data System (ADS)
Haigh, Ivan D.; MacPherson, Leigh R.; Mason, Matthew S.; Wijeratne, E. M. S.; Pattiaratchi, Charitha B.; Crompton, Ryan P.; George, Steve
2014-01-01
The incidence of major storm surges in the last decade have dramatically emphasized the immense destructive capabilities of extreme water level events, particularly when driven by severe tropical cyclones. Given this risk, it is vitally important that the exceedance probabilities of extreme water levels are accurately evaluated to inform risk-based flood and erosion management, engineering and for future land-use planning and to ensure the risk of catastrophic structural failures due to under-design or expensive wastes due to over-design are minimised. Australia has a long history of coastal flooding from tropical cyclones. Using a novel integration of two modeling techniques, this paper provides the first estimates of present day extreme water level exceedance probabilities around the whole coastline of Australia, and the first estimates that combine the influence of astronomical tides, storm surges generated by both extra-tropical and tropical cyclones, and seasonal and inter-annual variations in mean sea level. Initially, an analysis of tide gauge records has been used to assess the characteristics of tropical cyclone-induced surges around Australia. However, given the dearth (temporal and spatial) of information around much of the coastline, and therefore the inability of these gauge records to adequately describe the regional climatology, an observationally based stochastic tropical cyclone model has been developed to synthetically extend the tropical cyclone record to 10,000 years. Wind and pressure fields derived for these synthetically generated events have then been used to drive a hydrodynamic model of the Australian continental shelf region with annual maximum water levels extracted to estimate exceedance probabilities around the coastline. To validate this methodology, selected historic storm surge events have been simulated and resultant storm surges compared with gauge records. Tropical cyclone induced exceedance probabilities have been combined with
Mossman, Douglas
2015-03-01
Probability plays a ubiquitous role in decision-making through a process in which we use data from groups of past outcomes to make inferences about new situations. Yet in recent years, many forensic mental health professionals have become persuaded that overly wide confidence intervals render actuarial risk assessment instruments virtually useless in individual assessments. If this were true, the mathematical properties of probabilistic judgments would preclude forensic clinicians from applying group-based findings about risk to individuals. As a consequence, actuarially based risk estimates might be barred from use in legal proceedings. Using a fictional scenario, I seek to show how group data have an obvious application to individual decisions. I also explain how misunderstanding the aims of risk assessment has led to mistakes about how, when, and why group data apply to individual instances. Although actuarially based statements about individuals' risk have many pitfalls, confidence intervals pose no barrier to using actuarial tools derived from group data to improve decision-making about individual instances.
Empirical estimation of the conditional probability of natech events within the United States.
Santella, Nicholas; Steinberg, Laura J; Aguirra, Gloria Andrea
2011-06-01
Natural disasters are the cause of a sizeable number of hazmat releases, referred to as "natechs." An enhanced understanding of natech probability, allowing for predictions of natech occurrence, is an important step in determining how industry and government should mitigate natech risk. This study quantifies the conditional probabilities of natechs at TRI/RMP and SICS 1311 facilities given the occurrence of hurricanes, earthquakes, tornadoes, and floods. During hurricanes, a higher probability of releases was observed due to storm surge (7.3 releases per 100 TRI/RMP facilities exposed vs. 6.2 for SIC 1311) compared to category 1-2 hurricane winds (5.6 TRI, 2.6 SIC 1311). Logistic regression confirms the statistical significance of the greater propensity for releases at RMP/TRI facilities, and during some hurricanes, when controlling for hazard zone. The probability of natechs at TRI/RMP facilities during earthquakes increased from 0.1 releases per 100 facilities at MMI V to 21.4 at MMI IX. The probability of a natech at TRI/RMP facilities within 25 miles of a tornado was small (∼0.025 per 100 facilities), reflecting the limited area directly affected by tornadoes. Areas inundated during flood events had a probability of 1.1 releases per 100 facilities but demonstrated widely varying natech occurrence during individual events, indicating that factors not quantified in this study such as flood depth and speed are important for predicting flood natechs. These results can inform natech risk analysis, aid government agencies responsible for planning response and remediation after natural disasters, and should be useful in raising awareness of natech risk within industry. © 2011 Society for Risk Analysis.
Waters, Martha; McKernan, Lauralynn; Maier, Andrew; Jayjock, Michael; Schaeffer, Val; Brosseau, Lisa
2015-01-01
The fundamental goal of this article is to describe, define, and analyze the components of the risk characterization process for occupational exposures. Current methods are described for the probabilistic characterization of exposure, including newer techniques that have increasing applications for assessing data from occupational exposure scenarios. In addition, since the probability of health effects reflects variability in the exposure estimate as well as the dose-response curve—the integrated considerations of variability surrounding both components of the risk characterization provide greater information to the occupational hygienist. Probabilistic tools provide a more informed view of exposure as compared to use of discrete point estimates for these inputs to the risk characterization process. Active use of such tools for exposure and risk assessment will lead to a scientifically supported worker health protection program. Understanding the bases for an occupational risk assessment, focusing on important sources of variability and uncertainty enables characterizing occupational risk in terms of a probability, rather than a binary decision of acceptable risk or unacceptable risk. A critical review of existing methods highlights several conclusions: (1) exposure estimates and the dose-response are impacted by both variability and uncertainty and a well-developed risk characterization reflects and communicates this consideration; (2) occupational risk is probabilistic in nature and most accurately considered as a distribution, not a point estimate; and (3) occupational hygienists have a variety of tools available to incorporate concepts of risk characterization into occupational health and practice. PMID:26302336
O'Connell, Allan F.; Talancy, Neil W.; Bailey, Larissa L.; Sauer, John R.; Cook, Robert; Gilbert, Andrew T.
2006-01-01
Large-scale, multispecies monitoring programs are widely used to assess changes in wildlife populations but they often assume constant detectability when documenting species occurrence. This assumption is rarely met in practice because animal populations vary across time and space. As a result, detectability of a species can be influenced by a number of physical, biological, or anthropogenic factors (e.g., weather, seasonality, topography, biological rhythms, sampling methods). To evaluate some of these influences, we estimated site occupancy rates using species-specific detection probabilities for meso- and large terrestrial mammal species on Cape Cod, Massachusetts, USA. We used model selection to assess the influence of different sampling methods and major environmental factors on our ability to detect individual species. Remote cameras detected the most species (9), followed by cubby boxes (7) and hair traps (4) over a 13-month period. Estimated site occupancy rates were similar among sampling methods for most species when detection probabilities exceeded 0.15, but we question estimates obtained from methods with detection probabilities between 0.05 and 0.15, and we consider methods with lower probabilities unacceptable for occupancy estimation and inference. Estimated detection probabilities can be used to accommodate variation in sampling methods, which allows for comparison of monitoring programs using different protocols. Vegetation and seasonality produced species-specific differences in detectability and occupancy, but differences were not consistent within or among species, which suggests that our results should be considered in the context of local habitat features and life history traits for the target species. We believe that site occupancy is a useful state variable and suggest that monitoring programs for mammals using occupancy data consider detectability prior to making inferences about species distributions or population change.
Langtimm, C.A.; O'Shea, T.J.; Pradel, R.; Beck, C.A.
1998-01-01
The population dynamics of large, long-lived mammals are particularly sensitive to changes in adult survival. Understanding factors affecting survival patterns is therefore critical for developing and testing theories of population dynamics and for developing management strategies aimed at preventing declines or extinction in such taxa. Few studies have used modern analytical approaches for analyzing variation and testing hypotheses about survival probabilities in large mammals. This paper reports a detailed analysis of annual adult survival in the Florida manatee (Trichechus manatus latirostris), an endangered marine mammal, based on a mark-recapture approach. Natural and boat-inflicted scars distinctively 'marked' individual manatees that were cataloged in a computer-based photographic system. Photo-documented resightings provided 'recaptures.' Using open population models, annual adult-survival probabilities were estimated for manatees observed in winter in three areas of Florida: Blue Spring, Crystal River, and the Atlantic coast. After using goodness-of-fit tests in Program RELEASE to search for violations of the assumptions of mark-recapture analysis, survival and sighting probabilities were modeled under several different biological hypotheses with Program SURGE. Estimates of mean annual probability of sighting varied from 0.948 for Blue Spring to 0.737 for Crystal River and 0.507 for the Atlantic coast. At Crystal River and Blue Spring, annual survival probabilities were best estimated as constant over the study period at 0.96 (95% CI = 0.951-0.975 and 0.900-0.985, respectively). On the Atlantic coast, where manatees are impacted more by human activities, annual survival probabilities had a significantly lower mean estimate of 0.91 (95% CI = 0.887-0.926) and varied unpredictably over the study period. For each study area, survival did not differ between sexes and was independent of relative adult age. The high constant adult-survival probabilities estimated
Gerhardt, Andrea; Scharf, Rüdiger E; Greer, Ian A; Zotz, Rainer B
2016-09-09
Venous thromboembolism (VTE) is a leading cause of maternal mortality. Few studies have evaluated the individual risk of gestational VTE associated with heritable thrombophilia and current recommendations for antenatal thromboprophylaxis in women with severe thrombophilia such as homozygous factor V Leiden mutation (FVL) depend on a positive family history of VTE. To better stratify thromboprophylaxis in pregnancy, we aimed to estimate the individual probability (absolute risk) of gestational VTE associated with thrombophilia and whether these risk factors are independent of a family history of VTE in first-degree relatives. We studied 243 women with first VTE during pregnancy and the puerperium, and 243 age-matched normal women. Baseline incidence of VTE of 1:483 pregnancies in women ≥35 years and 1:741 deliveries in women <35 years was assumed, according to a recent population-based study. In women ≥35 years [<35 years], the individual probability of gestational VTE was: 0.7% [0.5%] for heterozygous FVL; 3.4% [2.2%], for homozygous FVL; 0.6% [0.4%], for heterozygous prothrombin G20210A; 8.2% [5.5%] for compound heterozygotes for FVL and prothrombin G20210A; 9.0% [6.1%] for antithrombin deficiency; 1.1% [0.7%] for protein C deficiency; and 1.0% [0.7%] for protein S deficiency These results were independent of a positive family history of VTE. We provide evidence that unselected women with these thrombophilias have an increased risk of gestational VTE independent of a positive family history of VTE. In contrast to current guidelines, these data suggest that women with high-risk thrombophilia should be considered for antenatal thromboprophylaxis regardless of family history of VTE.
PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling
2015-01-01
Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments. We develop a hybrid approach (PIGS), which combines the computational efficiency of pairwise methods with the power of multiway methods. It leverages the IBD graph structure to compute the probability of IBD conditional on all pairwise estimates simultaneously. We show via extensive simulations and analysis of real data that our method produces a substantial increase in the number of identified small IBD segments. PMID:25860540
Estimating site occupancy rates when detection probabilities are less than one
MacKenzie, D.I.; Nichols, J.D.; Lachman, G.B.; Droege, S.; Royle, J. Andrew; Langtimm, C.A.
2002-01-01
Nondetection of a species at a site does not imply that the species is absent unless the probability of detection is 1. We propose a model and likelihood-based method for estimating site occupancy rates when detection probabilities are 0.3). We estimated site occupancy rates for two anuran species at 32 wetland sites in Maryland, USA, from data collected during 2000 as part of an amphibian monitoring program, Frogwatch USA. Site occupancy rates were estimated as 0.49 for American toads (Bufo americanus), a 44% increase over the proportion of sites at which they were actually observed, and as 0.85 for spring peepers (Pseudacris crucifer), slightly above the observed proportion of 0.83.
Benzene risk estimation using radiation equivalent coefficients.
Nakayama, Aki; Isono, Tomomi; Kikuchi, Takuro; Ohnishi, Iichiro; Igarashi, Junichiro; Yoneda, Minoru; Morisawa, Shinsuke
2009-03-01
We estimated benzene risk using a novel framework of risk assessment that employed the measurement of radiation dose equivalents to benzene metabolites and a PBPK model. The highest risks for 1 microg/m(3) and 3.2 mg/m(3) life time exposure of benzene estimated with a linear regression were 5.4 x 10(-7) and 1.3 x 10(-3), respectively. Even though these estimates were based on in vitro chromosome aberration test data, they were about one-sixth to one-fourteenth that from other studies and represent a fairly good estimate by using radiation equivalent coefficient as an "internal standard."
On the Estimation of Detection Probabilities for Sampling Stream-Dwelling Fishes.
Peterson, James T.
1999-11-01
To examine the adequacy of fish probability of detection estimates, I examined distributional properties of survey and monitoring data for bull trout (Salvelinus confluentus), brook trout (Salvelinus fontinalis), westslope cutthroat trout (Oncorhynchus clarki lewisi), chinook salmon parr (Oncorhynchus tshawytscha), and steelhead /redband trout (Oncorhynchus mykiss spp.), from 178 streams in the Interior Columbia River Basin. Negative binomial dispersion parameters varied considerably among species and streams, but were significantly (P<0.05) positively related to fish density. Across streams, the variances in fish abundances differed greatly among species and indicated that the data for all species were overdispersed with respect to the Poisson (i.e., the variances exceeded the means). This significantly affected Poisson probability of detection estimates, which were the highest across species and were, on average, 3.82, 2.66, and 3.47 times greater than baseline values. Required sample sizes for species detection at the 95% confidence level were also lowest for the Poisson, which underestimated sample size requirements an average of 72% across species. Negative binomial and Poisson-gamma probability of detection and sample size estimates were more accurate than the Poisson and generally less than 10% from baseline values. My results indicate the Poisson and binomial assumptions often are violated, which results in probability of detection estimates that are biased high and sample size estimates that are biased low. To increase the accuracy of these estimates, I recommend that future studies use predictive distributions than can incorporate multiple sources of uncertainty or excess variance and that all distributional assumptions be explicitly tested.
Michael, Andrew J.
2012-01-01
Estimates of the probability that an ML 4.8 earthquake, which occurred near the southern end of the San Andreas fault on 24 March 2009, would be followed by an M 7 mainshock over the following three days vary from 0.0009 using a Gutenberg–Richter model of aftershock statistics (Reasenberg and Jones, 1989) to 0.04 using a statistical model of foreshock behavior and long‐term estimates of large earthquake probabilities, including characteristic earthquakes (Agnew and Jones, 1991). I demonstrate that the disparity between the existing approaches depends on whether or not they conform to Gutenberg–Richter behavior. While Gutenberg–Richter behavior is well established over large regions, it could be violated on individual faults if they have characteristic earthquakes or over small areas if the spatial distribution of large‐event nucleations is disproportional to the rate of smaller events. I develop a new form of the aftershock model that includes characteristic behavior and combines the features of both models. This new model and the older foreshock model yield the same results when given the same inputs, but the new model has the advantage of producing probabilities for events of all magnitudes, rather than just for events larger than the initial one. Compared with the aftershock model, the new model has the advantage of taking into account long‐term earthquake probability models. Using consistent parameters, the probability of an M 7 mainshock on the southernmost San Andreas fault is 0.0001 for three days from long‐term models and the clustering probabilities following the ML 4.8 event are 0.00035 for a Gutenberg–Richter distribution and 0.013 for a characteristic‐earthquake magnitude–frequency distribution. Our decisions about the existence of characteristic earthquakes and how large earthquakes nucleate have a first‐order effect on the probabilities obtained from short‐term clustering models for these large events.
A maximum a posteriori probability time-delay estimation for seismic signals
NASA Astrophysics Data System (ADS)
Carrier, A.; Got, J.-L.
2014-09-01
Cross-correlation and cross-spectral time delays often exhibit strong outliers due to ambiguities or cycle jumps in the correlation function. Their number increases when signal-to-noise, signal similarity or spectral bandwidth decreases. Such outliers heavily determine the time-delay probability density function and the results of further computations (e.g. double-difference location and tomography) using these time delays. In the present research we expressed cross-correlation as a function of the squared difference between signal amplitudes and show that they are closely related. We used this difference as a cost function whose minimum is reached when signals are aligned. Ambiguities may be removed in this function by using a priori information. We propose using the traveltime difference as a priori time-delay information. By modelling the probability density function of the traveltime difference by a Cauchy distribution and the probability density function of the data (differences of seismic signal amplitudes) by a Laplace distribution we were able to find explicitly the time-delay a posteriori probability density function. The location of the maximum of this a posteriori probability density function is the maximum a posteriori time-delay estimation for earthquake signals. Using this estimation to calculate time delays for earthquakes on the south flank of Kilauea statistically improved the cross-correlation time-delay estimation for these data and resulted in successful double-difference relocation for an increased number of earthquakes. This robust time-delay estimation improves the spatiotemporal resolution of seismicity rates in the south flank of Kilauea.
Optimal estimation for regression models on τ-year survival probability.
Kwak, Minjung; Kim, Jinseog; Jung, Sin-Ho
2015-01-01
A logistic regression method can be applied to regressing the [Formula: see text]-year survival probability to covariates, if there are no censored observations before time [Formula: see text]. But if some observations are incomplete due to censoring before time [Formula: see text], then the logistic regression cannot be applied. Jung (1996) proposed to modify the score function for logistic regression to accommodate the right-censored observations. His modified score function, motivated for a consistent estimation of regression parameters, becomes a regular logistic score function if no observations are censored before time [Formula: see text]. In this article, we propose a modification of Jung's estimating function for an optimal estimation for the regression parameters in addition to consistency. We prove that the optimal estimator is more efficient than Jung's estimator. This theoretical comparison is illustrated with a real example data analysis and simulations.
Simplified Computation for Nonparametric Windows Method of Probability Density Function Estimation.
Joshi, Niranjan; Kadir, Timor; Brady, Michael
2011-08-01
Recently, Kadir and Brady proposed a method for estimating probability density functions (PDFs) for digital signals which they call the Nonparametric (NP) Windows method. The method involves constructing a continuous space representation of the discrete space and sampled signal by using a suitable interpolation method. NP Windows requires only a small number of observed signal samples to estimate the PDF and is completely data driven. In this short paper, we first develop analytical formulae to obtain the NP Windows PDF estimates for 1D, 2D, and 3D signals, for different interpolation methods. We then show that the original procedure to calculate the PDF estimate can be significantly simplified and made computationally more efficient by a judicious choice of the frame of reference. We have also outlined specific algorithmic details of the procedures enabling quick implementation. Our reformulation of the original concept has directly demonstrated a close link between the NP Windows method and the Kernel Density Estimator.
Dodd, C.K.; Dorazio, R.M.
2004-01-01
A critical variable in both ecological and conservation field studies is determining how many individuals of a species are present within a defined sampling area. Labor intensive techniques such as capture-mark-recapture and removal sampling may provide estimates of abundance, but there are many logistical constraints to their widespread application. Many studies on terrestrial and aquatic salamanders use counts as an index of abundance, assuming that detection remains constant while sampling. If this constancy is violated, determination of detection probabilities is critical to the accurate estimation of abundance. Recently, a model was developed that provides a statistical approach that allows abundance and detection to be estimated simultaneously from spatially and temporally replicated counts. We adapted this model to estimate these parameters for salamanders sampled over a six vear period in area-constrained plots in Great Smoky Mountains National Park. Estimates of salamander abundance varied among years, but annual changes in abundance did not vary uniformly among species. Except for one species, abundance estimates were not correlated with site covariates (elevation/soil and water pH, conductivity, air and water temperature). The uncertainty in the estimates was so large as to make correlations ineffectual in predicting which covariates might influence abundance. Detection probabilities also varied among species and sometimes among years for the six species examined. We found such a high degree of variation in our counts and in estimates of detection among species, sites, and years as to cast doubt upon the appropriateness of using count data to monitor population trends using a small number of area-constrained survey plots. Still, the model provided reasonable estimates of abundance that could make it useful in estimating population size from count surveys.
The effect of coupling hydrologic and hydrodynamic models on probable maximum flood estimation
NASA Astrophysics Data System (ADS)
Felder, Guido; Zischg, Andreas; Weingartner, Rolf
2017-07-01
Deterministic rainfall-runoff modelling usually assumes stationary hydrological system, as model parameters are calibrated with and therefore dependant on observed data. However, runoff processes are probably not stationary in the case of a probable maximum flood (PMF) where discharge greatly exceeds observed flood peaks. Developing hydrodynamic models and using them to build coupled hydrologic-hydrodynamic models can potentially improve the plausibility of PMF estimations. This study aims to assess the potential benefits and constraints of coupled modelling compared to standard deterministic hydrologic modelling when it comes to PMF estimation. The two modelling approaches are applied using a set of 100 spatio-temporal probable maximum precipitation (PMP) distribution scenarios. The resulting hydrographs, the resulting peak discharges as well as the reliability and the plausibility of the estimates are evaluated. The discussion of the results shows that coupling hydrologic and hydrodynamic models substantially improves the physical plausibility of PMF modelling, although both modelling approaches lead to PMF estimations for the catchment outlet that fall within a similar range. Using a coupled model is particularly suggested in cases where considerable flood-prone areas are situated within a catchment.
Estimation of submarine mass failure probability from a sequence of deposits with age dates
Geist, Eric L.; Chaytor, Jason D.; Parsons, Thomas E.; ten Brink, Uri S.
2013-01-01
The empirical probability of submarine mass failure is quantified from a sequence of dated mass-transport deposits. Several different techniques are described to estimate the parameters for a suite of candidate probability models. The techniques, previously developed for analyzing paleoseismic data, include maximum likelihood and Type II (Bayesian) maximum likelihood methods derived from renewal process theory and Monte Carlo methods. The estimated mean return time from these methods, unlike estimates from a simple arithmetic mean of the center age dates and standard likelihood methods, includes the effects of age-dating uncertainty and of open time intervals before the first and after the last event. The likelihood techniques are evaluated using Akaike’s Information Criterion (AIC) and Akaike’s Bayesian Information Criterion (ABIC) to select the optimal model. The techniques are applied to mass transport deposits recorded in two Integrated Ocean Drilling Program (IODP) drill sites located in the Ursa Basin, northern Gulf of Mexico. Dates of the deposits were constrained by regional bio- and magnetostratigraphy from a previous study. Results of the analysis indicate that submarine mass failures in this location occur primarily according to a Poisson process in which failures are independent and return times follow an exponential distribution. However, some of the model results suggest that submarine mass failures may occur quasiperiodically at one of the sites (U1324). The suite of techniques described in this study provides quantitative probability estimates of submarine mass failure occurrence, for any number of deposits and age uncertainty distributions.
Recent developments on the probable maximum precipitation (PMP) estimation in China
NASA Astrophysics Data System (ADS)
Zhan, Daojiang; Zhou, Jinshang
1984-02-01
This paper deals with regional and seasonal characteristics of rain storms in China which are introducing the most intensive rainfall occurrences. The paper further summarizes the techniques and practices involved for estimating the probable maximum precipitation (PMP). In consequence of inadequate streamflow data and abundance of heavy storms in China, it would be very difficult and dubious to extrapolate a frequency curve to the long return periods required for a spillway of a major structure. Apart from this, there are often dense populated areas downstream from reservoirs. Thus, in the design criterion of earth dams and/or rockfill dams (embankment) for reservoirs of major significance and also for important small dams, whose failure could result in fatalities as well as catastrophic damages, the probable maximum precipitation and probable maximum flood should be used. Thus, generalized charts of 24-hr. point-PMP have been developed.
Probability based remaining capacity estimation using data-driven and neural network model
NASA Astrophysics Data System (ADS)
Wang, Yujie; Yang, Duo; Zhang, Xu; Chen, Zonghai
2016-05-01
Since large numbers of lithium-ion batteries are composed in pack and the batteries are complex electrochemical devices, their monitoring and safety concerns are key issues for the applications of battery technology. An accurate estimation of battery remaining capacity is crucial for optimization of the vehicle control, preventing battery from over-charging and over-discharging and ensuring the safety during its service life. The remaining capacity estimation of a battery includes the estimation of state-of-charge (SOC) and state-of-energy (SOE). In this work, a probability based adaptive estimator is presented to obtain accurate and reliable estimation results for both SOC and SOE. For the SOC estimation, an n ordered RC equivalent circuit model is employed by combining an electrochemical model to obtain more accurate voltage prediction results. For the SOE estimation, a sliding window neural network model is proposed to investigate the relationship between the terminal voltage and the model inputs. To verify the accuracy and robustness of the proposed model and estimation algorithm, experiments under different dynamic operation current profiles are performed on the commercial 1665130-type lithium-ion batteries. The results illustrate that accurate and robust estimation can be obtained by the proposed method.
Tvedt, Christine; Sjetne, Ingeborg Strømseng; Helgeland, Jon; Bukholm, Geir
2014-01-01
Background There is a growing body of evidence for associations between the work environment and patient outcomes. A good work environment may maximise healthcare workers’ efforts to avoid failures and to facilitate quality care that is focused on patient safety. Several studies use nurse-reported quality measures, but it is uncertain whether these outcomes are correlated with clinical outcomes. The aim of this study was to determine the correlations between hospital-aggregated, nurse-assessed quality and safety, and estimated probabilities for 30-day survival in and out of hospital. Methods In a multicentre study involving almost all Norwegian hospitals with more than 85 beds (sample size=30, information about nurses’ perceptions of organisational characteristics were collected. Subscales from this survey were used to describe properties of the organisations: quality system, patient safety management, nurse–physician relationship, staffing adequacy, quality of nursing and patient safety. The average scores for these organisational characteristics were aggregated to hospital level, and merged with estimated probabilities for 30-day survival in and out of hospital (survival probabilities) from a national database. In this observational, ecological study, the relationships between the organisational characteristics (independent variables) and clinical outcomes (survival probabilities) were examined. Results Survival probabilities were correlated with nurse-assessed quality of nursing. Furthermore, the subjective perception of staffing adequacy was correlated with overall survival. Conclusions This study showed that perceived staffing adequacy and nurses’ assessments of quality of nursing were correlated with survival probabilities. It is suggested that the way nurses characterise the microsystems they belong to, also reflects the general performance of hospitals. PMID:24728887
Estimating migratory connectivity of birds when re-encounter probabilities are heterogeneous.
Cohen, Emily B; Hostetler, Jeffrey A; Royle, J Andrew; Marra, Peter P
2014-05-01
Understanding the biology and conducting effective conservation of migratory species requires an understanding of migratory connectivity - the geographic linkages of populations between stages of the annual cycle. Unfortunately, for most species, we are lacking such information. The North American Bird Banding Laboratory (BBL) houses an extensive database of marking, recaptures and recoveries, and such data could provide migratory connectivity information for many species. To date, however, few species have been analyzed for migratory connectivity largely because heterogeneous re-encounter probabilities make interpretation problematic. We accounted for regional variation in re-encounter probabilities by borrowing information across species and by using effort covariates on recapture and recovery probabilities in a multistate capture-recapture and recovery model. The effort covariates were derived from recaptures and recoveries of species within the same regions. We estimated the migratory connectivity for three tern species breeding in North America and over-wintering in the tropics, common (Sterna hirundo), roseate (Sterna dougallii), and Caspian terns (Hydroprogne caspia). For western breeding terns, model-derived estimates of migratory connectivity differed considerably from those derived directly from the proportions of re-encounters. Conversely, for eastern breeding terns, estimates were merely refined by the inclusion of re-encounter probabilities. In general, eastern breeding terns were strongly connected to eastern South America, and western breeding terns were strongly linked to the more western parts of the nonbreeding range under both models. Through simulation, we found this approach is likely useful for many species in the BBL database, although precision improved with higher re-encounter probabilities and stronger migratory connectivity. We describe an approach to deal with the inherent biases in BBL banding and re-encounter data to demonstrate that
Estimating migratory connectivity of birds when re-encounter probabilities are heterogeneous
Cohen, Emily B; Hostetler, Jeffrey A; Royle, J Andrew; Marra, Peter P
2014-01-01
Understanding the biology and conducting effective conservation of migratory species requires an understanding of migratory connectivity – the geographic linkages of populations between stages of the annual cycle. Unfortunately, for most species, we are lacking such information. The North American Bird Banding Laboratory (BBL) houses an extensive database of marking, recaptures and recoveries, and such data could provide migratory connectivity information for many species. To date, however, few species have been analyzed for migratory connectivity largely because heterogeneous re-encounter probabilities make interpretation problematic. We accounted for regional variation in re-encounter probabilities by borrowing information across species and by using effort covariates on recapture and recovery probabilities in a multistate capture–recapture and recovery model. The effort covariates were derived from recaptures and recoveries of species within the same regions. We estimated the migratory connectivity for three tern species breeding in North America and over-wintering in the tropics, common (Sterna hirundo), roseate (Sterna dougallii), and Caspian terns (Hydroprogne caspia). For western breeding terns, model-derived estimates of migratory connectivity differed considerably from those derived directly from the proportions of re-encounters. Conversely, for eastern breeding terns, estimates were merely refined by the inclusion of re-encounter probabilities. In general, eastern breeding terns were strongly connected to eastern South America, and western breeding terns were strongly linked to the more western parts of the nonbreeding range under both models. Through simulation, we found this approach is likely useful for many species in the BBL database, although precision improved with higher re-encounter probabilities and stronger migratory connectivity. We describe an approach to deal with the inherent biases in BBL banding and re-encounter data to demonstrate
Estimating migratory connectivity of birds when re-encounter probabilities are heterogeneous
Cohen, Emily B.; Hostelter, Jeffrey A.; Royle, J. Andrew; Marra, Peter P.
2014-01-01
Understanding the biology and conducting effective conservation of migratory species requires an understanding of migratory connectivity – the geographic linkages of populations between stages of the annual cycle. Unfortunately, for most species, we are lacking such information. The North American Bird Banding Laboratory (BBL) houses an extensive database of marking, recaptures and recoveries, and such data could provide migratory connectivity information for many species. To date, however, few species have been analyzed for migratory connectivity largely because heterogeneous re-encounter probabilities make interpretation problematic. We accounted for regional variation in re-encounter probabilities by borrowing information across species and by using effort covariates on recapture and recovery probabilities in a multistate capture–recapture and recovery model. The effort covariates were derived from recaptures and recoveries of species within the same regions. We estimated the migratory connectivity for three tern species breeding in North America and over-wintering in the tropics, common (Sterna hirundo), roseate (Sterna dougallii), and Caspian terns (Hydroprogne caspia). For western breeding terns, model-derived estimates of migratory connectivity differed considerably from those derived directly from the proportions of re-encounters. Conversely, for eastern breeding terns, estimates were merely refined by the inclusion of re-encounter probabilities. In general, eastern breeding terns were strongly connected to eastern South America, and western breeding terns were strongly linked to the more western parts of the nonbreeding range under both models. Through simulation, we found this approach is likely useful for many species in the BBL database, although precision improved with higher re-encounter probabilities and stronger migratory connectivity. We describe an approach to deal with the inherent biases in BBL banding and re-encounter data to demonstrate
Relating space radiation environments to risk estimates
Curtis, S.B.
1991-10-01
This lecture will provide a bridge from the physical energy or LET spectra as might be calculated in an organ to the risk of carcinogenesis, a particular concern for extended missions to the moon or beyond to Mars. Topics covered will include (1) LET spectra expected from galactic cosmic rays, (2) probabilities that individual cell nuclei in the body will be hit by heavy galactic cosmic ray particles, (3) the conventional methods of calculating risks from a mixed environment of high and low LET radiation, (4) an alternate method which provides certain advantages using fluence-related risk coefficients (risk cross sections), and (5) directions for future research and development of these ideas.
Relating space radiation environments to risk estimates
Curtis, S.B.
1991-10-01
This lecture will provide a bridge from the physical energy or LET spectra as might be calculated in an organ to the risk of carcinogenesis, a particular concern for extended missions to the moon or beyond to Mars. Topics covered will include (1) LET spectra expected from galactic cosmic rays, (2) probabilities that individual cell nuclei in the body will be hit by heavy galactic cosmic ray particles, (3) the conventional methods of calculating risks from a mixed environment of high and low LET radiation, (4) an alternate method which provides certain advantages using fluence-related risk coefficients (risk cross sections), and (5) directions for future research and development of these ideas.
NASA Astrophysics Data System (ADS)
Boslough, M.
2011-12-01
Climate-related uncertainty is traditionally presented as an error bar, but it is becoming increasingly common to express it in terms of a probability density function (PDF). PDFs are a necessary component of probabilistic risk assessments, for which simple "best estimate" values are insufficient. Many groups have generated PDFs for climate sensitivity using a variety of methods. These PDFs are broadly consistent, but vary significantly in their details. One axiom of the verification and validation community is, "codes don't make predictions, people make predictions." This is a statement of the fact that subject domain experts generate results using assumptions within a range of epistemic uncertainty and interpret them according to their expert opinion. Different experts with different methods will arrive at different PDFs. For effective decision support, a single consensus PDF would be useful. We suggest that market methods can be used to aggregate an ensemble of opinions into a single distribution that expresses the consensus. Prediction markets have been shown to be highly successful at forecasting the outcome of events ranging from elections to box office returns. In prediction markets, traders can take a position on whether some future event will or will not occur. These positions are expressed as contracts that are traded in a double-action market that aggregates price, which can be interpreted as a consensus probability that the event will take place. Since climate sensitivity cannot directly be measured, it cannot be predicted. However, the changes in global mean surface temperature are a direct consequence of climate sensitivity, changes in forcing, and internal variability. Viable prediction markets require an undisputed event outcome on a specific date. Climate-related markets exist on Intrade.com, an online trading exchange. One such contract is titled "Global Temperature Anomaly for Dec 2011 to be greater than 0.65 Degrees C." Settlement is based
Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.
Chiba, Tomoaki; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru
2017-01-01
In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.
Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis
Chiba, Tomoaki; Akaho, Shotaro; Murata, Noboru
2017-01-01
In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group’s sales beat GM’s sales, which is a reasonable scenario. PMID:28076383
How should detection probability be incorporated into estimates of relative abundance?
MacKenzie, D.I.; Kendall, W.L.
2002-01-01
Determination of the relative abundance of two populations, separated by time or space, is of interest in many ecological situations. We focus on two estimators of relative abundance, which assume that the probability that an individual is detected at least once in the survey is either equal or unequal for the two populations. We present three methods for incorporating the collected information into our inference. The first method, proposed previously, is a traditional hypothesis test for evidence that detection probabilities are unequal. However, we feel that, a priori, it is more likely that detection probabilities are actually different; hence, the burden of proof should be shifted, requiring evidence that detection probabilities are practically equivalent. The second method we present, equivalence testing, is one approach to doing so. Third, we suggest that model averaging could be used by combining the two estimators according to derived model weights. These differing approaches are applied to a mark-recapture experiment on Nuttail's cottontail rabbit (Sylvilagus nuttallii) conducted in central Oregon during 1974 and 1975, which has been previously analyzed by other authors.
NASA Technical Reports Server (NTRS)
Courey, Karim; Wright, Clara; Asfour, Shihab; Bayliss, Jon; Ludwig, Larry
2008-01-01
Existing risk simulations make the assumption that when a free tin whisker has bridged two adjacent exposed electrical conductors, the result is an electrical short circuit. This conservative assumption is made because shorting is a random event that has a currently unknown probability associated with it. Due to contact resistance, electrical shorts may not occur at lower voltage levels. In this experiment, we study the effect of varying voltage on the breakdown of the contact resistance which leads to a short circuit. From this data, we can estimate the probability of an electrical short, as a function of voltage, given that a free tin whisker has bridged two adjacent exposed electrical conductors. In addition, three tin whiskers grown from the same Space Shuttle Orbiter card guide used in the aforementioned experiment were cross sectioned and studied using a focused ion beam (FIB).
Security Events and Vulnerability Data for Cybersecurity Risk Estimation.
Allodi, Luca; Massacci, Fabio
2017-08-01
Current industry standards for estimating cybersecurity risk are based on qualitative risk matrices as opposed to quantitative risk estimates. In contrast, risk assessment in most other industry sectors aims at deriving quantitative risk estimations (e.g., Basel II in Finance). This article presents a model and methodology to leverage on the large amount of data available from the IT infrastructure of an organization's security operation center to quantitatively estimate the probability of attack. Our methodology specifically addresses untargeted attacks delivered by automatic tools that make up the vast majority of attacks in the wild against users and organizations. We consider two-stage attacks whereby the attacker first breaches an Internet-facing system, and then escalates the attack to internal systems by exploiting local vulnerabilities in the target. Our methodology factors in the power of the attacker as the number of "weaponized" vulnerabilities he/she can exploit, and can be adjusted to match the risk appetite of the organization. We illustrate our methodology by using data from a large financial institution, and discuss the significant mismatch between traditional qualitative risk assessments and our quantitative approach. © 2017 Society for Risk Analysis.
1980-03-01
H. Phelps, Stanley M. Halpin, Edgar M. Johnson, and Franklin L. Moses HUMAN FACTORS TECHNICAL AREA U. S. Army Research Institute for the Behavioral...Army Technical Director Commander NOTICES DISTRIBUTION: Primatry distribution of this rewot ha been mode by ARI. PIS.. addrescorrespondence 0O~rniflll...explored by relating the psychological research on the use of subjective probability estimates with the need of Army intelli- gence analysts to
a Parametric Study of Eddy Current Response for Probability of Detection Estimation
NASA Astrophysics Data System (ADS)
Hoppe, W. C.
2010-02-01
In the study reported here, historical Probability of Detection (POD) data for eddy current inspections were analyzed using an extension of the "a-hat versus a" model in order to better account for known crack variables and thereby better separate system and crack factors influencing the POD parameters. Intriguing insights have been gained in the process suggesting a simple model for POD estimation. The parametric model will be presented including results of the study and suggestions for further research.
PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling.
Park, Danny S; Baran, Yael; Hormozdiari, Farhad; Eng, Celeste; Torgerson, Dara G; Burchard, Esteban G; Zaitlen, Noah
2015-01-01
Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments.
A robust design mark-resight abundance estimator allowing heterogeneity in resighting probabilities
McClintock, B.T.; White, Gary C.; Burnham, K.P.
2006-01-01
This article introduces the beta-binomial estimator (BBE), a closed-population abundance mark-resight model combining the favorable qualities of maximum likelihood theory and the allowance of individual heterogeneity in sighting probability (p). The model may be parameterized for a robust sampling design consisting of multiple primary sampling occasions where closure need not be met between primary occasions. We applied the model to brown bear data from three study areas in Alaska and compared its performance to the joint hypergeometric estimator (JHE) and Bowden's estimator (BOWE). BBE estimates suggest heterogeneity levels were non-negligible and discourage the use of JHE for these data. Compared to JHE and BOWE, confidence intervals were considerably shorter for the AICc model-averaged BBE. To evaluate the properties of BBE relative to JHE and BOWE when sample sizes are small, simulations were performed with data from three primary occasions generated under both individual heterogeneity and temporal variation in p. All models remained consistent regardless of levels of variation in p. In terms of precision, the AICc model-averaged BBE showed advantages over JHE and BOWE when heterogeneity was present and mean sighting probabilities were similar between primary occasions. Based on the conditions examined, BBE is a reliable alternative to JHE or BOWE and provides a framework for further advances in mark-resight abundance estimation. ?? 2006 American Statistical Association and the International Biometric Society.
Maximum a posteriori probability estimation for localizing damage using ultrasonic guided waves
NASA Astrophysics Data System (ADS)
Flynn, Eric B.; Todd, Michael D.; Wilcox, Paul D.; Drinkwater, Bruce W.; Croxford, Anthony J.
2011-04-01
Presented is an approach to damage localization for guided wave structural health monitoring (GWSHM) in plate-like structures. In this mode of SHM, transducers excite and sense guided waves in order to detect and characterize the presence of damage. The premise of the presented localization approach is simple: use as the estimated damage location the point on the structure with the maximum a posteriori probability (MAP) of being the location of damage (i.e., the most probable location given a set of sensor measurements). This is accomplished by constructing a minimally-informed statistical model of the GWSHM process. Parameters of the model which are unknown, such as scattered wave amplitude, are assigned non-informative Bayesian prior distributions and averaged out of the a posteriori probability calculation. Using an ensemble of measurements from an instrumented plate with stiffening stringers, the performance of the MAP estimate is compared to that of what were found to be the two most effective previously reported algorithms. The MAP estimate proved superior in nearly all test cases and was particularly effective in localizing damage using very sparse arrays of as few as three transducers.
Using optimal transport theory to estimate transition probabilities in metapopulation dynamics
Nichols, Jonathan M.; Spendelow, Jeffrey A.; Nichols, James
2017-01-01
This work considers the estimation of transition probabilities associated with populations moving among multiple spatial locations based on numbers of individuals at each location at two points in time. The problem is generally underdetermined as there exists an extremely large number of ways in which individuals can move from one set of locations to another. A unique solution therefore requires a constraint. The theory of optimal transport provides such a constraint in the form of a cost function, to be minimized in expectation over the space of possible transition matrices. We demonstrate the optimal transport approach on marked bird data and compare to the probabilities obtained via maximum likelihood estimation based on marked individuals. It is shown that by choosing the squared Euclidean distance as the cost, the estimated transition probabilities compare favorably to those obtained via maximum likelihood with marked individuals. Other implications of this cost are discussed, including the ability to accurately interpolate the population's spatial distribution at unobserved points in time and the more general relationship between the cost and minimum transport energy.
NASA Technical Reports Server (NTRS)
Courey, Karim; Wright, Clara; Asfour, Shihab; Onar, Arzu; Bayliss, Jon; Ludwig, Larry
2009-01-01
In this experiment, an empirical model to quantify the probability of occurrence of an electrical short circuit from tin whiskers as a function of voltage was developed. This empirical model can be used to improve existing risk simulation models. FIB and TEM images of a tin whisker confirm the rare polycrystalline structure on one of the three whiskers studied. FIB cross-section of the card guides verified that the tin finish was bright tin.
ERIC Educational Resources Information Center
Harris, Adam J. L.; Corner, Adam
2011-01-01
Verbal probability expressions are frequently used to communicate risk and uncertainty. The Intergovernmental Panel on Climate Change (IPCC), for example, uses them to convey risks associated with climate change. Given the potential for human action to mitigate future environmental risks, it is important to understand how people respond to these…
ERIC Educational Resources Information Center
Harris, Adam J. L.; Corner, Adam
2011-01-01
Verbal probability expressions are frequently used to communicate risk and uncertainty. The Intergovernmental Panel on Climate Change (IPCC), for example, uses them to convey risks associated with climate change. Given the potential for human action to mitigate future environmental risks, it is important to understand how people respond to these…
On estimating probability of presence from use-availability or presence-background data.
Phillips, Steven J; Elith, Jane
2013-06-01
A fundamental ecological modeling task is to estimate the probability that a species is present in (or uses) a site, conditional on environmental variables. For many species, available data consist of "presence" data (locations where the species [or evidence of it] has been observed), together with "background" data, a random sample of available environmental conditions. Recently published papers disagree on whether probability of presence is identifiable from such presence-background data alone. This paper aims to resolve the disagreement, demonstrating that additional information is required. We defined seven simulated species representing various simple shapes of response to environmental variables (constant, linear, convex, unimodal, S-shaped) and ran five logistic model-fitting methods using 1000 presence samples and 10 000 background samples; the simulations were repeated 100 times. The experiment revealed a stark contrast between two groups of methods: those based on a strong assumption that species' true probability of presence exactly matches a given parametric form had highly variable predictions and much larger RMS error than methods that take population prevalence (the fraction of sites in which the species is present) as an additional parameter. For six species, the former group grossly under- or overestimated probability of presence. The cause was not model structure or choice of link function, because all methods were logistic with linear and, where necessary, quadratic terms. Rather, the experiment demonstrates that an estimate of prevalence is not just helpful, but is necessary (except in special cases) for identifying probability of presence. We therefore advise against use of methods that rely on the strong assumption, due to Lele and Keim (recently advocated by Royle et al.) and Lancaster and Imbens. The methods are fragile, and their strong assumption is unlikely to be true in practice. We emphasize, however, that we are not arguing against
Kendall, W.L.; Nichols, J.D.
2002-01-01
Temporary emigration was identified some time ago as causing potential problems in capture-recapture studies, and in the last five years approaches have been developed for dealing with special cases of this general problem. Temporary emigration can be viewed more generally as involving transitions to and from an unobservable state, and frequently the state itself is one of biological interest (e.g., 'nonbreeder'). Development of models that permit estimation of relevant parameters in the presence of an unobservable state requires either extra information (e.g., as supplied by Pollock's robust design) or the following classes of model constraints: reducing the order of Markovian transition probabilities, imposing a degree of determinism on transition probabilities, removing state specificity of survival probabilities, and imposing temporal constancy of parameters. The objective of the work described in this paper is to investigate estimability of model parameters under a variety of models that include an unobservable state. Beginning with a very general model and no extra information, we used numerical methods to systematically investigate the use of ancillary information and constraints to yield models that are useful for estimation. The result is a catalog of models for which estimation is possible. An example analysis of sea turtle capture-recapture data under two different models showed similar point estimates but increased precision for the model that incorporated ancillary data (the robust design) when compared to the model with deterministic transitions only. This comparison and the results of our numerical investigation of model structures lead to design suggestions for capture-recapture studies in the presence of an unobservable state.
Shen, Pao-sheng
2012-01-01
The cumulative incidence function provides intuitive summary information about competing risks data. Via a mixture decomposition of this function, Chang and Wang (Statist. Sinca 19:391-408, 2009) study how covariates affect the cumulative incidence probability of a particular failure type at a chosen time point. Without specifying the corresponding failure time distribution, they proposed two estimators and derived their large sample properties. The first estimator utilized the technique of weighting to adjust for the censoring bias, and can be considered as an extension of Fine's method (J R Stat Soc Ser B 61: 817-830, 1999). The second used imputation and extends the idea of Wang (J R Stat Soc Ser B 65: 921-935, 2003) from a nonparametric setting to the current regression framework. In this article, when covariates take only discrete values, we extend both approaches of Chang and Wang (Statist Sinca 19:391-408, 2009) by allowing left truncation. Large sample properties of the proposed estimators are derived, and their finite sample performance is investigated through a simulation study. We also apply our methods to heart transplant survival data.
NASA Astrophysics Data System (ADS)
Barengoltz, Jack
2016-07-01
Monte Carlo (MC) is a common method to estimate probability, effectively by a simulation. For planetary protection, it may be used to estimate the probability of impact P{}_{I} by a launch vehicle (upper stage) of a protected planet. The object of the analysis is to provide a value for P{}_{I} with a given level of confidence (LOC) that the true value does not exceed the maximum allowed value of P{}_{I}. In order to determine the number of MC histories required, one must also guess the maximum number of hits that will occur in the analysis. This extra parameter is needed because a LOC is desired. If more hits occur, the MC analysis would indicate that the true value may exceed the specification value with a higher probability than the LOC. (In the worst case, even the mean value of the estimated P{}_{I} might exceed the specification value.) After the analysis is conducted, the actual number of hits is, of course, the mean. The number of hits arises from a small probability per history and a large number of histories; these are the classic requirements for a Poisson distribution. For a known Poisson distribution (the mean is the only parameter), the probability for some interval in the number of hits is calculable. Before the analysis, this is not possible. Fortunately, there are methods that can bound the unknown mean for a Poisson distribution. F. Garwoodfootnote{ F. Garwood (1936), ``Fiduciary limits for the Poisson distribution.'' Biometrika 28, 437-442.} published an appropriate method that uses the Chi-squared function, actually its inversefootnote{ The integral chi-squared function would yield probability α as a function of the mean µ and an actual value n.} (despite the notation used): This formula for the upper and lower limits of the mean μ with the two-tailed probability 1-α depends on the LOC α and an estimated value of the number of "successes" n. In a MC analysis for planetary protection, only the upper limit is of interest, i.e., the single
Estimating twin concordance for bivariate competing risks twin data.
Scheike, Thomas H; Holst, Klaus K; Hjelmborg, Jacob B
2014-03-30
For twin time-to-event data, we consider different concordance probabilities, such as the casewise concordance that are routinely computed as a measure of the lifetime dependence/correlation for specific diseases. The concordance probability here is the probability that both twins have experienced the event of interest. Under the assumption that both twins are censored at the same time, we show how to estimate this probability in the presence of right censoring, and as a consequence, we can then estimate the casewise twin concordance. In addition, we can model the magnitude of within pair dependence over time, and covariates may be further influential on the marginal risk and dependence structure. We establish the estimators large sample properties and suggest various tests, for example, for inferring familial influence. The method is demonstrated and motivated by specific twin data on cancer events with the competing risk death. We thus aim to quantify the degree of dependence through the casewise concordance function and show a significant genetic component.
NASA Technical Reports Server (NTRS)
Courey, Karim J.; Asfour, Shihab S.; Onar, Arzu; Bayliss, Jon A.; Ludwig, Larry L.; Wright, Maria C.
2009-01-01
To comply with lead-free legislation, many manufacturers have converted from tin-lead to pure tin finishes of electronic components. However, pure tin finishes have a greater propensity to grow tin whiskers than tin-lead finishes. Since tin whiskers present an electrical short circuit hazard in electronic components, simulations have been developed to quantify the risk of said short circuits occurring. Existing risk simulations make the assumption that when a free tin whisker has bridged two adjacent exposed electrical conductors, the result is an electrical short circuit. This conservative assumption is made because shorting is a random event that had an unknown probability associated with it. Note however that due to contact resistance electrical shorts may not occur at lower voltage levels. In our first article we developed an empirical probability model for tin whisker shorting. In this paper, we develop a more comprehensive empirical model using a refined experiment with a larger sample size, in which we studied the effect of varying voltage on the breakdown of the contact resistance which leads to a short circuit. From the resulting data we estimated the probability distribution of an electrical short, as a function of voltage. In addition, the unexpected polycrystalline structure seen in the focused ion beam (FIB) cross section in the first experiment was confirmed in this experiment using transmission electron microscopy (TEM). The FIB was also used to cross section two card guides to facilitate the measurement of the grain size of each card guide's tin plating to determine its finish.
A short note on measuring subjective life expectancy: survival probabilities versus point estimates.
Rappange, David R; van Exel, Job; Brouwer, Werner B F
2017-01-01
Understanding subjective longevity expectations is important, but measurement is not straightforward. Two common elicitation formats are the direct measurement of a subjective point estimate of life expectancy and the assessment of survival probabilities to a range of target ages. This study presents one of the few direct comparisons of these two methods. Results from a representative sample of the Dutch population indicate that respondents on average gave higher estimates of longevity using survival probabilities (83.6 years) compared to point estimates (80.2 years). Individual differences between elicitation methods were smaller for younger respondents and for respondents with a higher socioeconomic status. The correlation between the subjective longevity estimations was moderate, but their associations with respondents' characteristics were similar. Our results are in line with existing literature and suggest that findings from both elicitation methods may not be directly comparable, especially in certain subgroups of the population. Implications of inconsistent and focal point answers, rounding and anchoring require further attention. More research on the measurement of subjective expectations is required.
Greenberg, Joshua; Price, Bertram; Ware, Adam
2010-04-01
Microbial source tracking (MST) is a procedure used to determine the relative contributions of humans and animals to fecal microbial contamination of surface waters in a given watershed. Studies of MST methodology have focused on optimizing sampling, laboratory, and statistical analysis methods in order to improve the reliability of determining which sources contributed most to surface water fecal contaminant. The usual approach for estimating a source distribution of microbial contamination is to classify water sample microbial isolates into discrete source categories and calculate the proportion of these isolates in each source category. The set of proportions is an estimate of the contaminant source distribution. In this paper we propose and compare an alternative method for estimating a source distribution-averaging posterior probabilities of source identity across isolates. We conducted a Monte Carlo simulation covering a wide variety of watershed scenarios to compare the two methods. The results show that averaging source posterior probabilities across isolates leads to more accurate source distribution estimates than proportions that follow classification.
Estimates of EPSP amplitude based on changes in motoneuron discharge rate and probability.
Powers, Randall K; Türker, K S
2010-10-01
When motor units are discharging tonically, transient excitatory synaptic inputs produce an increase in the probability of spike occurrence and also increase the instantaneous discharge rate. Several researchers have proposed that these induced changes in discharge rate and probability can be used to estimate the amplitude of the underlying excitatory post-synaptic potential (EPSP). We tested two different methods of estimating EPSP amplitude by comparing the amplitude of simulated EPSPs with their effects on the discharge of rat hypoglossal motoneurons recorded in an in vitro brainstem slice preparation. The first estimation method (simplified-trajectory method) is based on the assumptions that the membrane potential trajectory between spikes can be approximated by a 10 mV post-spike hyperpolarization followed by a linear rise to the next spike and that EPSPs sum linearly with this trajectory. We hypothesized that this estimation method would not be accurate due to interspike variations in membrane conductance and firing threshold that are not included in the model and that an alternative method based on estimating the effective distance to threshold would provide more accurate estimates of EPSP amplitude. This second method (distance-to-threshold method) uses interspike interval statistics to estimate the effective distance to threshold throughout the interspike interval and incorporates this distance-to-threshold trajectory into a threshold-crossing model. We found that the first method systematically overestimated the amplitude of small (<5 mV) EPSPs and underestimated the amplitude of large (>5 mV EPSPs). For large EPSPs, the degree of underestimation increased with increasing background discharge rate. Estimates based on the second method were more accurate for small EPSPs than those based on the first model, but estimation errors were still large for large EPSPs. These errors were likely due to two factors: (1) the distance to threshold can only be
Estimation of probable maximum precipitation for catchments in eastern India by a generalized method
NASA Astrophysics Data System (ADS)
Rakhecha, P. R.; Mandal, B. N.; Kulkarni, A. K.; Deshpande, N. R.
1995-03-01
A generalized method to estimate the probable maximum precipitation (PMP) has been developed for catchments in eastern India (80° E, 18° N) by pooling together all the major rainstorms that have occurred in this area. The areal raindepths of these storms are normalized for factors such as storm dew point temperature, distance of the storm from the coast, topographic effects and any intervening mountain barriers between the storm area and the moisture source. The normalized values are then applied, with appropriate adjustment factors in estimating PMP raindepths, to the Subarnarekha river catchment (upto the Chandil dam site) with an area of 5663 km2. The PMP rainfall for 1, 2 and 3 days were found to be roughly 53 cm, 78 cm and 98 cm, respectively. It is expected that the application of the generalized method proposed here will give more reliable estimates of PMP for different duration rainfall events.
Saarela, Olli; Liu, Zhihui Amy
2016-10-15
Marginal structural Cox models are used for quantifying marginal treatment effects on outcome event hazard function. Such models are estimated using inverse probability of treatment and censoring (IPTC) weighting, which properly accounts for the impact of time-dependent confounders, avoiding conditioning on factors on the causal pathway. To estimate the IPTC weights, the treatment assignment mechanism is conventionally modeled in discrete time. While this is natural in situations where treatment information is recorded at scheduled follow-up visits, in other contexts, the events specifying the treatment history can be modeled in continuous time using the tools of event history analysis. This is particularly the case for treatment procedures, such as surgeries. In this paper, we propose a novel approach for flexible parametric estimation of continuous-time IPTC weights and illustrate it in assessing the relationship between metastasectomy and mortality in metastatic renal cell carcinoma patients. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
McGinn, Thomas; Jervis, Ramiro; Wisnivesky, Juan; Keitz, Sheri
2008-01-01
Background Clinical prediction rules (CPR) are tools that clinicians can use to predict the most likely diagnosis, prognosis, or response to treatment in a patient based on individual characteristics. CPRs attempt to standardize, simplify, and increase the accuracy of clinicians’ diagnostic and prognostic assessments. The teaching tips series is designed to give teachers advice and materials they can use to attain specific educational objectives. Educational Objectives In this article, we present 3 teaching tips aimed at helping clinical learners use clinical prediction rules and to more accurately assess pretest probability in every day practice. The first tip is designed to demonstrate variability in physician estimation of pretest probability. The second tip demonstrates how the estimate of pretest probability influences the interpretation of diagnostic tests and patient management. The third tip exposes learners to various examples and different types of Clinical Prediction Rules (CPR) and how to apply them in practice. Pilot Testing We field tested all 3 tips with 16 learners, a mix of interns and senior residents. Teacher preparatory time was approximately 2 hours. The field test utilized a board and a data projector; 3 handouts were prepared. The tips were felt to be clear and the educational objectives reached. Potential teaching pitfalls were identified. Conclusion Teaching with these tips will help physicians appreciate the importance of applying evidence to their every day decisions. In 2 or 3 short teaching sessions, clinicians can also become familiar with the use of CPRs in applying evidence consistently in everyday practice. PMID:18491194
Probability problems in seismic risk analysis and load combinations for nuclear power plants
George, L.L.
1983-01-01
This workshop describes some probability problems in power plant reliability and maintenance analysis. The problems are seismic risk analysis, loss of load probability, load combinations, and load sharing. The seismic risk problem is to compute power plant reliability given an earthquake and the resulting risk. Component survival occurs if its peak random response to the earthquake does not exceed its strength. Power plant survival is a complicated Boolean function of component failures and survivals. The responses and strengths of components are dependent random processes, and the peak responses are maxima of random processes. The resulting risk is the expected cost of power plant failure.
Lühr, Armin; Löck, Steffen; Jakobi, Annika; Stützer, Kristin; Bandurska-Luque, Anna; Vogelius, Ivan Richter; Enghardt, Wolfgang; Baumann, Michael; Krause, Mechthild
2017-07-01
Objectives of this work are (1) to derive a general clinically relevant approach to model tumor control probability (TCP) for spatially variable risk of failure and (2) to demonstrate its applicability by estimating TCP for patients planned for photon and proton irradiation. The approach divides the target volume into sub-volumes according to retrospectively observed spatial failure patterns. The product of all sub-volume TCPi values reproduces the observed TCP for the total tumor. The derived formalism provides for each target sub-volume i the tumor control dose (D50,i) and slope (γ50,i) parameters at 50% TCPi. For a simultaneous integrated boost (SIB) prescription for 45 advanced head and neck cancer patients, TCP values for photon and proton irradiation were calculated and compared. The target volume was divided into gross tumor volume (GTV), surrounding clinical target volume (CTV), and elective CTV (CTVE). The risk of a local failure in each of these sub-volumes was taken from the literature. Convenient expressions for D50,i and γ50,i were provided for the Poisson and the logistic model. Comparable TCP estimates were obtained for photon and proton plans of the 45 patients using the sub-volume model, despite notably higher dose levels (on average +4.9%) in the low-risk CTVE for photon irradiation. In contrast, assuming a homogeneous dose response in the entire target volume resulted in TCP estimates contradicting clinical experience (the highest failure rate in the low-risk CTVE) and differing substantially between photon and proton irradiation. The presented method is of practical value for three reasons: It (a) is based on empirical clinical outcome data; (b) can be applied to non-uniform dose prescriptions as well as different tumor entities and dose-response models; and (c) is provided in a convenient compact form. The approach may be utilized to target spatial patterns of local failures observed in patient cohorts by prescribing different doses to
A generalised technique for the estimation of probable maximum precipitation in India
NASA Astrophysics Data System (ADS)
Rakhecha, P. R.; Kennedy, M. R.
1985-06-01
In this paper a version of a generalised method of estimating probable maximum precipitation (PMP) is applied to the catchments of four large dams in India. The value of a secure dam is high both in terms of human life and in economic terms. Reliable estimates of PMP are required in estimating the design flood for spillways of large earth and rockfill dams. Estimates of PMP obtained using the traditional method of moisture maximisation and storm transposition can be unreliable as highly efficient rain storms may not be represented in the rainfall records of an area. Generalised methods of (calculating) PMP are used to obtain reliable estimates of PMP and also to give estimates which are consistent over a region. This is done by pooling together all the rainfall data from a very large area. The rainfall depths are normalised for such factors as storm dew-point temperature, distance of the storm from the coast, topographic effects and any intervening mountain barriers between the rainfall area and the moisture source. These normalised values can then be applied to any individual catchment, with the appropriate adjustment factors.
NASA Technical Reports Server (NTRS)
Edmonds, L. D.
2016-01-01
Because advancing technology has been producing smaller structures in electronic circuits, the floating gates in modern flash memories are becoming susceptible to prompt charge loss from ionizing radiation environments found in space. A method for estimating the risk of a charge-loss event is given.
NASA Technical Reports Server (NTRS)
Edmonds, L. D.
2016-01-01
Since advancing technology has been producing smaller structures in electronic circuits, the floating gates in modern flash memories are becoming susceptible to prompt charge loss from ionizing radiation environments found in space. A method for estimating the risk of a charge-loss event is given.
NASA Technical Reports Server (NTRS)
Edmonds, L. D.
2016-01-01
Because advancing technology has been producing smaller structures in electronic circuits, the floating gates in modern flash memories are becoming susceptible to prompt charge loss from ionizing radiation environments found in space. A method for estimating the risk of a charge-loss event is given.
Hanayama, Nobutane; Sibuya, Masaaki
2016-08-01
In modern biology, theories of aging fall mainly into two groups: damage theories and programed theories. If programed theories are true, the probability that human beings live beyond a specific age will be zero. In contrast, if damage theories are true, such an age does not exist, and a longevity record will be eventually destroyed. In this article, for examining real state, a special type of binomial model based on the generalized Pareto distribution has been applied to data of Japanese centenarians. From the results, it is concluded that the upper limit of lifetime probability distribution in the Japanese population has been estimated 123 years. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Geissler, P.H.; Moyer, L.M.
1983-01-01
Four sampling and estimation methods for estimating the number of red-cockaded woodpecker colonies on National Forests in the Southeast were compared, using samples chosen from simulated populations based on the observed sample. The methods included (1) simple random sampling without replacement using a mean per sampling unit estimator, (2) simple random sampling without replacement with a ratio per pine area estimator, (3) probability proportional to 'size' sampling with replacement, and (4) probability proportional to 'size' without replacement using Murthy's estimator. The survey sample of 274 National Forest compartments (1000 acres each) constituted a superpopulation from which simulated stratum populations were selected with probability inversely proportional to the original probability of selection. Compartments were originally sampled with probabilities proportional to the probabilities that the compartments contained woodpeckers ('size'). These probabilities were estimated with a discriminant analysis based on tree species and tree age. The ratio estimator would have been the best estimator for this survey based on the mean square error. However, if more accurate predictions of woodpecker presence had been available, Murthy's estimator would have been the best. A subroutine to calculate Murthy's estimates is included; it is computationally feasible to analyze up to 10 samples per stratum.
A predictive model to estimate the pretest probability of metastasis in patients with osteosarcoma
Wang, Sisheng; Zheng, Shaoluan; Hu, Kongzu; Sun, Heyan; Zhang, Jinling; Rong, Genxiang; Gao, Jie; Ding, Nan; Gui, Binjie
2017-01-01
Abstract Osteosarcomas (OSs) represent a huge challenge to improve the overall survival, especially in metastatic patients. Increasing evidence indicates that both tumor-associated elements but also on host-associated elements are under a remarkable effect on the prognosis of cancer patients, especially systemic inflammatory response. By analyzing a series prognosis of factors, including age, gender, primary tumor size, tumor location, tumor grade, and histological classification, monocyte ratio, and NLR ratio, a clinical predictive model was established by using stepwise logistic regression involved circulating leukocyte to compute the estimated probabilities of metastases for OS patients. The clinical predictive model was described by the following equations: probability of developing metastases = ex/(1 + ex), x = −2.150 + (1.680 × monocyte ratio) + (1.533 × NLR ratio), where is the base of the natural logarithm, the assignment to each of the 2 variables is 1 if the ratio >1 (otherwise 0). The calculated AUC of the receiver-operating characteristic curve as 0.793 revealed well accuracy of this model (95% CI, 0.740–0.845). The predicted probabilities that we generated with the cross-validation procedure had a similar AUC (0.743; 95% CI, 0.684–0.803). The present model could be used to improve the outcomes of the metastases by developing a predictive model considering circulating leukocyte influence to estimate the pretest probability of developing metastases in patients with OS. PMID:28099353
Estimating survival and breeding probability for pond-breeding amphibians: a modified robust design
Bailey, L.L.; Kendall, W.L.; Church, D.R.; Wilbur, H.M.
2004-01-01
Many studies of pond-breeding amphibians involve sampling individuals during migration to and from breeding habitats. Interpreting population processes and dynamics from these studies is difficult because (1) only a proportion of the population is observable each season, while an unknown proportion remains unobservable (e.g., non-breeding adults) and (2) not all observable animals are captured. Imperfect capture probability can be easily accommodated in capture?recapture models, but temporary transitions between observable and unobservable states, often referred to as temporary emigration, is known to cause problems in both open- and closed-population models. We develop a multistate mark?recapture (MSMR) model, using an open-robust design that permits one entry and one exit from the study area per season. Our method extends previous temporary emigration models (MSMR with an unobservable state) in two ways. First, we relax the assumption of demographic closure (no mortality) between consecutive (secondary) samples, allowing estimation of within-pond survival. Also, we add the flexibility to express survival probability of unobservable individuals (e.g., ?non-breeders?) as a function of the survival probability of observable animals while in the same, terrestrial habitat. This allows for potentially different annual survival probabilities for observable and unobservable animals. We apply our model to a relictual population of eastern tiger salamanders (Ambystoma tigrinum tigrinum). Despite small sample sizes, demographic parameters were estimated with reasonable precision. We tested several a priori biological hypotheses and found evidence for seasonal differences in pond survival. Our methods could be applied to a variety of pond-breeding species and other taxa where individuals are captured entering or exiting a common area (e.g., spawning or roosting area, hibernacula).
A predictive model to estimate the pretest probability of metastasis in patients with osteosarcoma.
Wang, Sisheng; Zheng, Shaoluan; Hu, Kongzu; Sun, Heyan; Zhang, Jinling; Rong, Genxiang; Gao, Jie; Ding, Nan; Gui, Binjie
2017-01-01
Osteosarcomas (OSs) represent a huge challenge to improve the overall survival, especially in metastatic patients. Increasing evidence indicates that both tumor-associated elements but also on host-associated elements are under a remarkable effect on the prognosis of cancer patients, especially systemic inflammatory response. By analyzing a series prognosis of factors, including age, gender, primary tumor size, tumor location, tumor grade, and histological classification, monocyte ratio, and NLR ratio, a clinical predictive model was established by using stepwise logistic regression involved circulating leukocyte to compute the estimated probabilities of metastases for OS patients. The clinical predictive model was described by the following equations: probability of developing metastases = ex/(1 + ex), x = -2.150 + (1.680 × monocyte ratio) + (1.533 × NLR ratio), where is the base of the natural logarithm, the assignment to each of the 2 variables is 1 if the ratio >1 (otherwise 0). The calculated AUC of the receiver-operating characteristic curve as 0.793 revealed well accuracy of this model (95% CI, 0.740-0.845). The predicted probabilities that we generated with the cross-validation procedure had a similar AUC (0.743; 95% CI, 0.684-0.803). The present model could be used to improve the outcomes of the metastases by developing a predictive model considering circulating leukocyte influence to estimate the pretest probability of developing metastases in patients with OS.
Estimating superpopulation size and annual probability of breeding for pond-breeding salamanders
Kinkead, K.E.; Otis, D.L.
2007-01-01
It has long been accepted that amphibians can skip breeding in any given year, and environmental conditions act as a cue for breeding. In this paper, we quantify temporary emigration or nonbreeding probability for mole and spotted salamanders (Ambystoma talpoideum and A. maculatum). We estimated that 70% of mole salamanders may skip breeding during an average rainfall year and 90% may skip during a drought year. Spotted salamanders may be more likely to breed, with only 17% avoiding the breeding pond during an average rainfall year. We illustrate how superpopulations can be estimated using temporary emigration probability estimates. The superpopulation is the total number of salamanders associated with a given breeding pond. Although most salamanders stay within a certain distance of a breeding pond for the majority of their life spans, it is difficult to determine true overall population sizes for a given site if animals are only captured during a brief time frame each year with some animals unavailable for capture at any time during a given year. ?? 2007 by The Herpetologists' League, Inc.
Modeling and estimation of stage-specific daily survival probabilities of nests
Stanley, T.R.
2000-01-01
In studies of avian nesting success, it is often of interest to estimate stage-specific daily survival probabilities of nests. When data can be partitioned by nesting stage (e.g., incubation stage, nestling stage), piecewise application of the Mayfield method or Johnsona??s method is appropriate. However, when the data contain nests where the transition from one stage to the next occurred during the interval between visits, piecewise approaches are inappropriate. In this paper, I present a model that allows joint estimation of stage-specific daily survival probabilities even when the time of transition between stages is unknown. The model allows interval lengths between visits to nests to vary, and the exact time of failure of nests does not need to be known. The performance of the model at various sample sizes and interval lengths between visits was investigated using Monte Carlo simulations, and it was found that the model performed quite well: bias was small and confidence-interval coverage was at the nominal 95% rate. A SAS program for obtaining maximum likelihood estimates of parameters, and their standard errors, is provided in the Appendix.
Lamb, Jennifer Y.; Waddle, J. Hardin; Qualls, Carl P.
2017-01-01
Large gaps exist in our knowledge of the ecology of stream-breeding plethodontid salamanders in the Gulf Coastal Plain. Data describing where these salamanders are likely to occur along environmental gradients, as well as their likelihood of detection, are important for the prevention and management of amphibian declines. We used presence/absence data from leaf litter bag surveys and a hierarchical Bayesian multispecies single-season occupancy model to estimate the occurrence of five species of plethodontids across reaches in headwater streams in the Gulf Coastal Plain. Average detection probabilities were high (range = 0.432–0.942) and unaffected by sampling covariates specific to the use of litter bags (i.e., bag submergence, sampling season, in-stream cover). Estimates of occurrence probabilities differed substantially between species (range = 0.092–0.703) and were influenced by the size of the upstream drainage area and by the maximum proportion of the reach that dried. The effects of these two factors were not equivalent across species. Our results demonstrate that hierarchical multispecies models successfully estimate occurrence parameters for both rare and common stream-breeding plethodontids. The resulting models clarify how species are distributed within stream networks, and they provide baseline values that will be useful in evaluating the conservation statuses of plethodontid species within lotic systems in the Gulf Coastal Plain.
Submarine tower escape decompression sickness risk estimation.
Loveman, G A M; Seddon, E M; Thacker, J C; Stansfield, M R; Jurd, K M
2014-01-01
Actions to enhance survival in a distressed submarine (DISSUB) scenario may be guided in part by knowledge of the likely risk of decompression sickness (DCS) should the crew attempt tower escape. A mathematical model for DCS risk estimation has been calibrated against DCS outcome data from 3,738 exposures of either men or goats to raised pressure. Body mass was used to scale DCS risk. The calibration data included more than 1,000 actual or simulated submarine escape exposures and no exposures with substantial staged decompression. Cases of pulmonary barotrauma were removed from the calibration data. The calibrated model was used to estimate the likelihood of DCS occurrence following submarine escape from the United Kingdom Royal Navy tower escape system. Where internal DISSUB pressure remains at - 0.1 MPa, escape from DISSUB depths < 200 meters is estimated to have DCS risk < 6%. Saturation at raised DISSUB pressure markedly increases risk, with > 60% DCS risk predicted for a 200-meter escape from saturation at 0.21 MPa. Using the calibrated model to predict DCS for direct ascent from saturation gives similar risk estimates to other published models.
ERIC Educational Resources Information Center
HUDMAN, JOHN T.; ZABROWSKI, EDWARD K.
EQUATIONS FOR SYSTEM INTAKE, DROPOUT, AND RETENTION RATE CALCULATIONS ARE DERIVED FOR ELEMENTARY SCHOOLS, SECONDARY SCHOOLS, AND COLLEGES. THE PROCEDURES DESCRIBED WERE FOLLOWED IN DEVELOPING ESTIMATES OF SELECTED ELEMENTS OF THE TRANSITION PROBABILITY MATRICES USED IN DYNAMOD II. THE PROBABILITY MATRIX CELLS ESTIMATED BY THE PROCEDURES DESCRIBED…
Olson, Scott A.; Brouillette, Michael C.
2006-01-01
A logistic regression equation was developed for estimating the probability of a stream flowing intermittently at unregulated, rural stream sites in Vermont. These determinations can be used for a wide variety of regulatory and planning efforts at the Federal, State, regional, county and town levels, including such applications as assessing fish and wildlife habitats, wetlands classifications, recreational opportunities, water-supply potential, waste-assimilation capacities, and sediment transport. The equation will be used to create a derived product for the Vermont Hydrography Dataset having the streamflow characteristic of 'intermittent' or 'perennial.' The Vermont Hydrography Dataset is Vermont's implementation of the National Hydrography Dataset and was created at a scale of 1:5,000 based on statewide digital orthophotos. The equation was developed by relating field-verified perennial or intermittent status of a stream site during normal summer low-streamflow conditions in the summer of 2005 to selected basin characteristics of naturally flowing streams in Vermont. The database used to develop the equation included 682 stream sites with drainage areas ranging from 0.05 to 5.0 square miles. When the 682 sites were observed, 126 were intermittent (had no flow at the time of the observation) and 556 were perennial (had flowing water at the time of the observation). The results of the logistic regression analysis indicate that the probability of a stream having intermittent flow in Vermont is a function of drainage area, elevation of the site, the ratio of basin relief to basin perimeter, and the areal percentage of well- and moderately well-drained soils in the basin. Using a probability cutpoint (a lower probability indicates the site has perennial flow and a higher probability indicates the site has intermittent flow) of 0.5, the logistic regression equation correctly predicted the perennial or intermittent status of 116 test sites 85 percent of the time.
Impact of microbial count distributions on human health risk estimates.
Duarte, A S R; Nauta, M J
2015-02-16
Quantitative microbiological risk assessment (QMRA) is influenced by the choice of the probability distribution used to describe pathogen concentrations, as this may eventually have a large effect on the distribution of doses at exposure. When fitting a probability distribution to microbial enumeration data, several factors may have an impact on the accuracy of that fit. Analysis of the best statistical fits of different distributions alone does not provide a clear indication of the impact in terms of risk estimates. Thus, in this study we focus on the impact of fitting microbial distributions on risk estimates, at two different concentration scenarios and at a range of prevalence levels. By using five different parametric distributions, we investigate whether different characteristics of a good fit are crucial for an accurate risk estimate. Among the factors studied are the importance of accounting for the Poisson randomness in counts, the difference between treating "true" zeroes as such or as censored below a limit of quantification (LOQ) and the importance of making the correct assumption about the underlying distribution of concentrations. By running a simulation experiment with zero-inflated Poisson-lognormal distributed data and an existing QMRA model from retail to consumer level, it was possible to assess the difference between expected risk and the risk estimated with using a lognormal, a zero-inflated lognormal, a Poisson-gamma, a zero-inflated Poisson-gamma and a zero-inflated Poisson-lognormal distribution. We show that the impact of the choice of different probability distributions to describe concentrations at retail on risk estimates is dependent both on concentration and prevalence levels. We also show that the use of an LOQ should be done consciously, especially when zero-inflation is not used. In general, zero-inflation does not necessarily improve the absolute risk estimation, but performance of zero-inflated distributions in QMRA tends to be
Coding of reward probability and risk by single neurons in animals.
Burke, Christopher J; Tobler, Philippe N
2011-01-01
Probability and risk are important factors for value-based decision making and optimal foraging. In order to survive in an unpredictable world, organisms must be able to assess the probability and risk attached to future events and use this information to generate adaptive behavior. Recent studies in non-human primates and rats have shown that both probability and risk are processed in a distributed fashion throughout the brain at the level of single neurons. Reward probability has mainly been shown to be coded by phasic increases and decreases in firing rates in neurons in the basal ganglia, midbrain, parietal, and frontal cortex. Reward variance is represented in orbitofrontal and posterior cingulate cortex and through a sustained response of dopaminergic midbrain neurons.
Risks of probable SUDEP among people with convulsive epilepsy in rural West China.
Zhang, Wen-Wu; Si, Yang; Chen, Tao; Chen, Deng; Liu, Ling; Deng, Ying; He, Jun; Li, You; Zhou, Dong
2016-07-01
This study aimed to examine the risk factors of probable sudden unexpected death in epilepsy (SUDEP) among patients with convulsive epilepsy in rural communities. A total of 35 cases with identified probable SUDEP were recruited in the study and compared with three survival controls that were sex and age matched from the same cohort for each case. Three healthy controls per case were chosen as a control group. Risk factors were analyzed using the logistic regression model. The odds ratio (OR) was calculated to determine the risk or protective effect. The following three factors significantly increased the risk of probable SUDEP: early-onset age of seizures (≤10 years vs. >10 years) with an OR of 6.8 (95% CI: 1.5-32.6), high seizure frequency at baseline (>10 years vs. ≤10 years) before regular phenobarbital treatment with an OR of 5.9 (95% CI: 2.2-16.6), and experiencing one or more seizures (vs. seizure-free) in the month prior to probable SUDEP with an OR of 9.5 (95% CI: 3.0-30.1). Lack of seizure freedom before and during regular antiepileptic drug treatment increase the risk of probable SUDEP. Special attention should be given to patients with early convulsive epilepsy-onset, and the proper control of convulsive seizures is critical for the prevention of probable SUDEP. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Lukeš, Tomáš; Křížek, Pavel; Švindrych, Zdeněk; Benda, Jakub; Ovesný, Martin; Fliegel, Karel; Klíma, Miloš; Hagen, Guy M
2014-12-01
We introduce and demonstrate a new high performance image reconstruction method for super-resolution structured illumination microscopy based on maximum a posteriori probability estimation (MAP-SIM). Imaging performance is demonstrated on a variety of fluorescent samples of different thickness, labeling density and noise levels. The method provides good suppression of out of focus light, improves spatial resolution, and allows reconstruction of both 2D and 3D images of cells even in the case of weak signals. The method can be used to process both optical sectioning and super-resolution structured illumination microscopy data to create high quality super-resolution images.
Local neighborhood transition probability estimation and its use in contextual classification
NASA Technical Reports Server (NTRS)
Chittineni, C. B.
1979-01-01
The problem of incorporating spatial or contextual information into classifications is considered. A simple model that describes the spatial dependencies between the neighboring pixels with a single parameter, Theta, is presented. Expressions are derived for updating the posteriori probabilities of the states of nature of the pattern under consideration using information from the neighboring patterns, both for spatially uniform context and for Markov dependencies in terms of Theta. Techniques for obtaining the optimal value of the parameter Theta as a maximum likelihood estimate from the local neighborhood of the pattern under consideration are developed.
Estimating the probability of allelic drop-out of STR alleles in forensic genetics.
Tvedebrink, Torben; Eriksen, Poul Svante; Mogensen, Helle Smidt; Morling, Niels
2009-09-01
In crime cases with available DNA evidence, the amount of DNA is often sparse due to the setting of the crime. In such cases, allelic drop-out of one or more true alleles in STR typing is possible. We present a statistical model for estimating the per locus and overall probability of allelic drop-out using the results of all STR loci in the case sample as reference. The methodology of logistic regression is appropriate for this analysis, and we demonstrate how to incorporate this in a forensic genetic framework.
Population-based absolute risk estimation with survey data
Kovalchik, Stephanie A.; Pfeiffer, Ruth M.
2013-01-01
Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level. PMID:23686614
Probable Maximum Precipitation Estimation Using the Revised Km-Value Method in Hong Kong
NASA Astrophysics Data System (ADS)
Lan, Ping; Lin, Bingzhang; Zhang, Yehui; Chen, Hong
2017-04-01
A brief overview of statistical method to estimate the Probable Maximum Precipitation (PMP) is presented. This study addresses some issues associated with Hershfield's Km-value method to estimate PMP in China, which can be solved by the revised Hershfield's Km-value method. This new derivation makes it clear that the frequency factor Km is depended on only two variables, the standardized variable, ϕm, the maximum deviation from the mean, scaled by its standard deviation, and the sample size, n. It is found that there is a consistent relationship between Km and ϕm. Therefore, Km can be used to make a preliminary estimate of PMP under some conditions when sufficient rainfall data are available. The advantages and disadvantages of this revised Km-value method are also discussed here with a case study for the estimation of 24-h PMP in Hong Kong. The 24-h PMP estimate in Hong Kong based on the local rainfall data is approximately to be 1753mm.
Southern California regional earthquake probability estimated from continuous GPS geodetic data
NASA Astrophysics Data System (ADS)
Anderson, G.
2002-12-01
Current seismic hazard estimates are primarily based on seismic and geologic data, but geodetic measurements from large, dense arrays such as the Southern California Integrated GPS Network (SCIGN) can also be used to estimate earthquake probabilities and seismic hazard. Geodetically-derived earthquake probability estimates are particularly important in regions with poorly-constrained fault slip rates. In addition, they are useful because such estimates come with well-determined error bounds. Long-term planning is underway to incorporate geodetic data in the next generation of United States national seismic hazard maps, and techniques for doing so need further development. I present a new method for estimating the expected rates of earthquakes using strain rates derived from geodetic station velocities. I compute the strain rates using a new technique devised by Y. Hsu and M. Simons [Y. Hsu and M. Simons, pers. comm.], which computes the horizontal strain rate tensor ( {˙ {ɛ}}) at each node of a pre-defined regular grid, using all geodetic velocities in the data set weighted by distance and estimated uncertainty. In addition, they use a novel weighting to handle the effects of station distribution: they divide the region covered by the geodetic network into Voronoi cells using the station locations and weight each station's contribution to {˙ {ɛ}} by the area of the Voronoi cell centered at that station. I convert {˙ {ɛ}} into the equivalent seismic moment rate density (˙ {M}) using the method of \\textit{Savage and Simpson} [1997] and maximum seismogenic depths estimated from regional seismicity; ˙ {M} gives the expected rate of seismic moment release in a region, based on the geodetic strain rates. Assuming the seismicity in the given region follows a Gutenberg-Richter relationship, I convert ˙ {M} to an expected rate of earthquakes of a given magnitude. I will present results of a study applying this method to data from the SCIGN array to estimate
Estimated probability of arsenic in groundwater from bedrock aquifers in New Hampshire, 2011
Ayotte, Joseph D.; Cahillane, Matthew; Hayes, Laura; Robinson, Keith W.
2012-01-01
The statewide maps generated by the probability models are not designed to predict arsenic concentration in any single well, but they are expected to provide useful information in areas of the State that currently contain little to no data on arsenic concentration. They also may aid in resource decision making, in determining potential risk for private wells, and in ecological-level analysis of disease outcomes. The approach for modeling arsenic in groundwater could also be applied to other environmental contaminants that have potential implications for human health, such as uranium, radon, fluoride, manganese, volatile organic compounds, nitrate, and bacteria.
Estimated Probability of a Cervical Spine Injury During an ISS Mission
NASA Technical Reports Server (NTRS)
Brooker, John E.; Weaver, Aaron S.; Myers, Jerry G.
2013-01-01
Introduction: The Integrated Medical Model (IMM) utilizes historical data, cohort data, and external simulations as input factors to provide estimates of crew health, resource utilization and mission outcomes. The Cervical Spine Injury Module (CSIM) is an external simulation designed to provide the IMM with parameter estimates for 1) a probability distribution function (PDF) of the incidence rate, 2) the mean incidence rate, and 3) the standard deviation associated with the mean resulting from injury/trauma of the neck. Methods: An injury mechanism based on an idealized low-velocity blunt impact to the superior posterior thorax of an ISS crewmember was used as the simulated mission environment. As a result of this impact, the cervical spine is inertially loaded from the mass of the head producing an extension-flexion motion deforming the soft tissues of the neck. A multibody biomechanical model was developed to estimate the kinematic and dynamic response of the head-neck system from a prescribed acceleration profile. Logistic regression was performed on a dataset containing AIS1 soft tissue neck injuries from rear-end automobile collisions with published Neck Injury Criterion values producing an injury transfer function (ITF). An injury event scenario (IES) was constructed such that crew 1 is moving through a primary or standard translation path transferring large volume equipment impacting stationary crew 2. The incidence rate for this IES was estimated from in-flight data and used to calculate the probability of occurrence. The uncertainty in the model input factors were estimated from representative datasets and expressed in terms of probability distributions. A Monte Carlo Method utilizing simple random sampling was employed to propagate both aleatory and epistemic uncertain factors. Scatterplots and partial correlation coefficients (PCC) were generated to determine input factor sensitivity. CSIM was developed in the SimMechanics/Simulink environment with a
NASA Astrophysics Data System (ADS)
Ascasibar, Yago
2010-08-01
The Field Estimator for Arbitrary Spaces (FiEstAS) computes the continuous probability density field underlying a given discrete data sample in multiple, non-commensurate dimensions. The algorithm works by constructing a metric-independent tessellation of the data space based on a recursive binary splitting. Individual, data-driven bandwidths are assigned to each point, scaled so that a constant “mass”M is enclosed. Kernel density estimation may then be performed for different kernel shapes, and a combination of balloon and sample point estimators is proposed as a compromise between resolution and variance. A bias correction is evaluated for the particular (yet common) case where the density is computed exactly at the locations of the data points rather than at an uncorrelated set of locations. By default, the algorithm combines a top-hat kernel with M=2.0 with the balloon estimator and applies the corresponding bias correction. These settings are shown to yield reasonable results for a simple test case, a two-dimensional ring, that illustrates the performance for oblique distributions, as well as for a six-dimensional Hernquist sphere, a fairly realistic model of the dynamical structure of stellar bulges in galaxies and dark matter haloes in cosmological N-body simulations. Results for different parameter settings are discussed in order to provide a guideline to select an optimal configuration in other cases. Source code is available upon request.
Fisicaro, E; Braibanti, A; Sambasiva Rao, R; Compari, C; Ghiozzi, A; Nageswara Rao, G
1998-04-01
An algorithm is proposed for the estimation of binding parameters for the interaction of biologically important macromolecules with smaller ones from electrometric titration data. The mathematical model is based on the representation of equilibria in terms of probability concepts of statistical molecular thermodynamics. The refinement of equilibrium concentrations of the components and estimation of binding parameters (log site constant and cooperativity factor) is performed using singular value decomposition, a chemometric technique which overcomes the general obstacles due to near singularity. The present software is validated with a number of biochemical systems of varying number of sites and cooperativity factors. The effect of random errors of realistic magnitude in experimental data is studied using the simulated primary data for some typical systems. The safe area within which approximate binding parameters ensure convergence has been reported for the non-self starting optimization algorithms.
Sacks, H.K.; Novak, T.
2008-03-15
During the past decade, several methane/air explosions in abandoned or sealed areas of underground coal mines have been attributed to lightning. Previously published work by the authors showed, through computer simulations, that currents from lightning could propagate down steel-cased boreholes and ignite explosive methane/air mixtures. The presented work expands on the model and describes a methodology based on IEEE Standard 1410-2004 to estimate the probability of an ignition. The methodology provides a means to better estimate the likelihood that an ignition could occur underground and, more importantly, allows the calculation of what-if scenarios to investigate the effectiveness of engineering controls to reduce the hazard. The computer software used for calculating fields and potentials is also verified by comparing computed results with an independently developed theoretical model of electromagnetic field propagation through a conductive medium.
Crowe, D.E.; Longshore, K.M.
2010-01-01
We estimated relative abundance and density of Western Burrowing Owls (Athene cunicularia hypugaea) at two sites in the Mojave Desert (200304). We made modifications to previously established Burrowing Owl survey techniques for use in desert shrublands and evaluated several factors that might influence the detection of owls. We tested the effectiveness of the call-broadcast technique for surveying this species, the efficiency of this technique at early and late breeding stages, and the effectiveness of various numbers of vocalization intervals during broadcasting sessions. Only 1 (3) of 31 initial (new) owl responses was detected during passive-listening sessions. We found that surveying early in the nesting season was more likely to produce new owl detections compared to surveying later in the nesting season. New owls detected during each of the three vocalization intervals (each consisting of 30 sec of vocalizations followed by 30 sec of silence) of our broadcasting session were similar (37, 40, and 23; n 30). We used a combination of detection trials (sighting probability) and double-observer method to estimate the components of detection probability, i.e., availability and perception. Availability for all sites and years, as determined by detection trials, ranged from 46.158.2. Relative abundance, measured as frequency of occurrence and defined as the proportion of surveys with at least one owl, ranged from 19.232.0 for both sites and years. Density at our eastern Mojave Desert site was estimated at 0.09 ?? 0.01 (SE) owl territories/km2 and 0.16 ?? 0.02 (SE) owl territories/km2 during 2003 and 2004, respectively. In our southern Mojave Desert site, density estimates were 0.09 ?? 0.02 (SE) owl territories/km2 and 0.08 ?? 0.02 (SE) owl territories/km 2 during 2004 and 2005, respectively. ?? 2010 The Raptor Research Foundation, Inc.
Estimation of (n,f) Cross-Sections by Measuring Reaction Probability Ratios
Plettner, C; Ai, H; Beausang, C W; Bernstein, L A; Ahle, L; Amro, H; Babilon, M; Burke, J T; Caggiano, J A; Casten, R F; Church, J A; Cooper, J R; Crider, B; Gurdal, G; Heinz, A; McCutchan, E A; Moody, K; Punyon, J A; Qian, J; Ressler, J J; Schiller, A; Williams, E; Younes, W
2005-04-21
Neutron-induced reaction cross-sections on unstable nuclei are inherently difficult to measure due to target activity and the low intensity of neutron beams. In an alternative approach, named the 'surrogate' technique, one measures the decay probability of the same compound nucleus produced using a stable beam on a stable target to estimate the neutron-induced reaction cross-section. As an extension of the surrogate method, in this paper they introduce a new technique of measuring the fission probabilities of two different compound nuclei as a ratio, which has the advantage of removing most of the systematic uncertainties. This method was benchmarked in this report by measuring the probability of deuteron-induced fission events in coincidence with protons, and forming the ratio P({sup 236}U(d,pf))/P({sup 238}U(d,pf)), which serves as a surrogate for the known cross-section ratio of {sup 236}U(n,f)/{sup 238}U(n,f). IN addition, the P({sup 238}U(d,d{prime}f))/P({sup 236}U(d,d{prime}f)) ratio as a surrogate for the {sup 237}U(n,f)/{sup 235}U(n,f) cross-section ratio was measured for the first time in an unprecedented range of excitation energies.
Development of a statistical tool for the estimation of riverbank erosion probability
NASA Astrophysics Data System (ADS)
Varouchakis, Emmanouil
2016-04-01
Riverbank erosion affects river morphology and local habitat, and results in riparian land loss, property and infrastructure damage, and ultimately flood defence weakening. An important issue concerning riverbank erosion is the identification of the vulnerable areas in order to predict river changes and assist stream management/restoration. An approach to predict areas vulnerable to erosion is to quantify the erosion probability by identifying the underlying relations between riverbank erosion and geomorphological or hydrological variables that prevent or stimulate erosion. In the present work, a innovative statistical methodology is proposed to predict the probability of presence or absence of erosion in a river section. A physically based model determines the locations vulnerable to erosion by quantifying the potential eroded area. The derived results are used to determine validation locations for the evaluation of the statistical tool performance. The statistical tool is based on a series of independent local variables and employs the Logistic Regression methodology. It is developed in two forms, Logistic Regression and Locally Weighted Logistic Regression, which both deliver useful and accurate results. The second form though, provides the most accurate results as it validates the presence or absence of erosion at all validation locations. The proposed tool is easy to use, accurate and can be applied to any region and river. Varouchakis, E. A., Giannakis, G. V., Lilli, M. A., Ioannidou, E., Nikolaidis, N. P., and Karatzas, G. P.: Development of a statistical tool for the estimation of riverbank erosion probability, SOIL (EGU), in print, 2016.
Xue, Xiaofang; Wu, Songfeng; Wang, Zhongsheng; Zhu, Yunping; He, Fuchu
2006-12-01
The calculation of protein probabilities is one of the most intractable problems in large-scale proteomic research. Current available estimating methods, for example, ProteinProphet, PROT_PROBE, Poisson model and two-peptide hits, employ different models trying to resolve this problem. Until now, no efficient method is used for comparative evaluation of the above methods in large-scale datasets. In order to evaluate these various methods, we developed a semi-random sampling model to simulate large-scale proteomic data. In this model, the identified peptides were sampled from the designed proteins and their cross-correlation scores were simulated according to the results from reverse database searching. The simulated result of 18 control proteins was consistent with the experimental one, demonstrating the efficiency of our model. According to the simulated results of human liver sample, ProteinProphet returned slightly higher probabilities and lower specificity than real cases. PROT_PROBE was a more efficient method with higher specificity. Predicted results from a Poisson model roughly coincide with real datasets, and the method of two-peptide hits seems solid but imprecise. However, the probabilities of identified proteins are strongly correlated with several experimental factors including spectra number, database size and protein abundance distribution.
Toward 3D-guided prostate biopsy target optimization: an estimation of tumor sampling probabilities
NASA Astrophysics Data System (ADS)
Martin, Peter R.; Cool, Derek W.; Romagnoli, Cesare; Fenster, Aaron; Ward, Aaron D.
2014-03-01
Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy aims to reduce the ~23% false negative rate of clinical 2D TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsy still yields false negatives. Therefore, we propose optimization of biopsy targeting to meet the clinician's desired tumor sampling probability, optimizing needle targets within each tumor and accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. We obtained multiparametric MRI and 3D TRUS images from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D surfaces that were registered to 3D TRUS. We estimated the probability, P, of obtaining a tumor sample with a single biopsy. Given an RMS needle delivery error of 3.5 mm for a contemporary fusion biopsy system, P >= 95% for 21 out of 81 tumors when the point of optimal sampling probability was targeted. Therefore, more than one biopsy core must be taken from 74% of the tumors to achieve P >= 95% for a biopsy system with an error of 3.5 mm. Our experiments indicated that the effect of error along the needle axis on the percentage of core involvement (and thus the measured tumor burden) was mitigated by the 18 mm core length.
Lahodny, G E; Gautam, R; Ivanek, R
2015-01-01
Indirect transmission through the environment, pathogen shedding by infectious hosts, replication of free-living pathogens within the environment, and environmental decontamination are suspected to play important roles in the spread and control of environmentally transmitted infectious diseases. To account for these factors, the classic Susceptible-Infectious-Recovered-Susceptible epidemic model is modified to include a compartment representing the amount of free-living pathogen within the environment. The model accounts for host demography, direct and indirect transmission, replication of free-living pathogens in the environment, and removal of free-living pathogens by natural death or environmental decontamination. Based on the assumptions of the deterministic model, a continuous-time Markov chain model is developed. An estimate for the probability of disease extinction or a major outbreak is obtained by approximating the Markov chain with a multitype branching process. Numerical simulations illustrate important differences between the deterministic and stochastic counterparts, relevant for outbreak prevention, that depend on indirect transmission, pathogen shedding by infectious hosts, replication of free-living pathogens, and environmental decontamination. The probability of a major outbreak is computed for salmonellosis in a herd of dairy cattle as well as cholera in a human population. An explicit expression for the probability of disease extinction or a major outbreak in terms of the model parameters is obtained for systems with no direct transmission or replication of free-living pathogens.
[Estimation of absolute risk for fracture].
Fujiwara, Saeko
2009-03-01
Osteoporosis treatment aims to prevent fractures and maintain the QOL of the elderly. However, persons at high risk of future fracture cannot be effectively identified on the basis of bone density (BMD) alone, although BMD is used as an diagnostic criterion. Therefore, the WHO recommended that absolute risk for fracture (10-year probability of fracture) for each individual be evaluated and used as an index for intervention threshold. The 10-year probability of fracture is calculated based on age, sex, BMD at the femoral neck (body mass index if BMD is not available), history of previous fractures, parental hip fracture history, smoking, steroid use, rheumatoid arthritis, secondary osteoporosis and alcohol consumption. The WHO has just announced the development of a calculation tool (FRAX: WHO Fracture Risk Assessment Tool) in February this year. Fractures could be prevented more effectively if, based on each country's medical circumstances, an absolute risk value for fracture to determine when to start medical treatment is established and persons at high risk of fracture are identified and treated accordingly.
NASA Astrophysics Data System (ADS)
Morrow, P.; McCloskey, J.; Steacy, S.
2001-12-01
It is now widely accepted that the goal of deterministic earthquake prediction is unattainable in the short term and may even be forbidden by nonlinearity in the generating dynamics. This nonlinearity does not, however, preclude the estimation of earthquake probability and, in particular, how this probability might change in space and time; earthquake hazard estimation might be possible in the absence of earthquake prediction. Recently, there has been a major development in the understanding of stress triggering of earthquakes which allows accurate calculation of the spatial variation of aftershock probability following any large earthquake. Over the past few years this Coulomb stress technique (CST) has been the subject of intensive study in the geophysics literature and has been extremely successful in explaining the spatial distribution of aftershocks following several major earthquakes. The power of current micro-computers, the great number of local, telemetered seismic networks, the rapid acquisition of data from satellites coupled with the speed of modern telecommunications and data transfer all mean that it may be possible that these new techniques could be applied in a forward sense. In other words, it is theoretically possible today to make predictions of the likely spatial distribution of aftershocks in near-real-time following a large earthquake. Approximate versions of such predictions could be available within, say, 0.1 days after the mainshock and might be continually refined and updated over the next 100 days. The European Commission has recently provided funding for a project to assess the extent to which it is currently possible to move CST predictions into a practically useful time frame so that low-confidence estimates of aftershock probability might be made within a few hours of an event and improved in near-real-time, as data of better quality become available over the following days to tens of days. Specifically, the project aims to assess the
Fast method for the estimation of impact probability of near-Earth objects
NASA Astrophysics Data System (ADS)
Vavilov, D.; Medvedev, Y.
2014-07-01
We propose a method to estimate the probability of collision of a celestial body with the Earth (or another major planet) at a given time moment t. Let there be a set of observations of a small body. At initial time moment T_0, a nominal orbit is defined by the least squares method. In our method, a unique coordinate system is used. It is supposed that errors of observations are related to errors of coordinates and velocities linearly and the distribution law of observation errors is normal. The unique frame is defined as follows. First of all, we fix an osculating ellipse of the body's orbit at the time moment t. The mean anomaly M in this osculating ellipse is a coordinate of the introduced system. The spatial coordinate ξ is perpendicular to the plane which contains the fixed ellipse. η is a spatial coordinate, too, and our axes satisfy the right-hand rule. The origin of ξ and η corresponds to the given M point on the ellipse. The components of the velocity are the corresponding derivatives of dotξ, dotη, dot{M}. To calculate the probability of collision, we numerically integrate equations of an asteroid's motion taking into account perturbations and calculate a normal matrix N. The probability is determinated as follows: P = {|detN|^{ {1}/{2} }}/{ (2π)^3 } int_Ω e^{ - {1}/{2} x^T N x } dx where x denotes a six-dimensional vector of coordinates and velocities, Ω is the region which is occupied by the Earth, and the superscript T denotes the matrix transpose operation. To take into account a gravitational attraction of the Earth, the radius of the Earth is increased by √{1 + {v_s^2}/{v_{rel}^2} } times, where v_s is the escape velocity and v_{rel} is the small body's velocity relative to the Earth. The 6-dimensional integral is analytically integrated over the velocity components on (-∞,+∞). After that we have the 3×3 matrix N_1. That 6-dimensional integral becomes a 3-dimensional integral. To calculate it quickly we do the following. We introduce
Rajwade, Ajit; Banerjee, Arunava; Rangarajan, Anand
2009-03-01
We present a new, geometric approach for determining the probability density of the intensity values in an image. We drop the notion of an image as a set of discrete pixels, and assume a piecewise-continuous representation. The probability density can then be regarded as being proportional to the area between two nearby isocontours of the image surface. Our paper extends this idea to joint densities of image pairs. We demonstrate the application of our method to affine registration between two or more images using information theoretic measures such as mutual information. We show cases where our method outperforms existing methods such as simple histograms, histograms with partial volume interpolation, Parzen windows, etc. under fine intensity quantization for affine image registration under significant image noise. Furthermore, we demonstrate results on simultaneous registration of multiple images, as well as for pairs of volume datasets, and show some theoretical properties of our density estimator. Our approach requires the selection of only an image interpolant. The method neither requires any kind of kernel functions (as in Parzen windows) which are unrelated to the structure of the image in itself, nor does it rely on any form of sampling for density estimation.
Kruppa, Jochen; Liu, Yufeng; Diener, Hans-Christian; Holste, Theresa; Weimar, Christian; König, Inke R; Ziegler, Andreas
2014-07-01
Machine learning methods are applied to three different large datasets, all dealing with probability estimation problems for dichotomous or multicategory data. Specifically, we investigate k-nearest neighbors, bagged nearest neighbors, random forests for probability estimation trees, and support vector machines with the kernels of Bessel, linear, Laplacian, and radial basis type. Comparisons are made with logistic regression. The dataset from the German Stroke Study Collaboration with dichotomous and three-category outcome variables allows, in particular, for temporal and external validation. The other two datasets are freely available from the UCI learning repository and provide dichotomous outcome variables. One of them, the Cleveland Clinic Foundation Heart Disease dataset, uses data from one clinic for training and from three clinics for external validation, while the other, the thyroid disease dataset, allows for temporal validation by separating data into training and test data by date of recruitment into study. For dichotomous outcome variables, we use receiver operating characteristics, areas under the curve values with bootstrapped 95% confidence intervals, and Hosmer-Lemeshow-type figures as comparison criteria. For dichotomous and multicategory outcomes, we calculated bootstrap Brier scores with 95% confidence intervals and also compared them through bootstrapping. In a supplement, we provide R code for performing the analyses and for random forest analyses in Random Jungle, version 2.1.0. The learning machines show promising performance over all constructed models. They are simple to apply and serve as an alternative approach to logistic or multinomial logistic regression analysis.
Rajwade, Ajit; Banerjee, Arunava; Rangarajan, Anand
2010-01-01
We present a new geometric approach for determining the probability density of the intensity values in an image. We drop the notion of an image as a set of discrete pixels and assume a piecewise-continuous representation. The probability density can then be regarded as being proportional to the area between two nearby isocontours of the image surface. Our paper extends this idea to joint densities of image pairs. We demonstrate the application of our method to affine registration between two or more images using information-theoretic measures such as mutual information. We show cases where our method outperforms existing methods such as simple histograms, histograms with partial volume interpolation, Parzen windows, etc., under fine intensity quantization for affine image registration under significant image noise. Furthermore, we demonstrate results on simultaneous registration of multiple images, as well as for pairs of volume data sets, and show some theoretical properties of our density estimator. Our approach requires the selection of only an image interpolant. The method neither requires any kind of kernel functions (as in Parzen windows), which are unrelated to the structure of the image in itself, nor does it rely on any form of sampling for density estimation. PMID:19147876
NASA Astrophysics Data System (ADS)
Ho, Chih-Hsiang; Smith, Eugene I.; Feuerbach, Daniel L.; Naumann, Terry R.
1991-12-01
Investigations are currently underway to evaluate the impact of potentially adverse conditions (e.g. volcanism, faulting, seismicity) on the waste-isolation capability of the proposed nuclear waste repository at Yucca Mountain, Nevada, USA. This paper is the first in a series that will examine the probability of disruption of the Yucca Mountain site by volcanic eruption. In it, we discuss three estimating techniques for determining the recurrence rate of volcanic eruption (λ), an important parameter in the Poisson probability model. The first method is based on the number of events occurring over a certain observation period, the second is based on repose times, and the final is based on magma volume. All three require knowledge of the total number of eruptions in the Yucca Mountain area during the observation period ( E). Following this discussion we then propose an estimate of E which takes into account the possibility of polygenetic and polycyclic volcanism at all the volcanic centers near the Yucca Mountain site.
[Estimation of risk areas for hepatitis A].
Braga, Ricardo Cerqueira Campos; Valencia, Luís Iván Ortiz; Medronho, Roberto de Andrade; Escosteguy, Claudia Caminha
2008-08-01
This study estimated hepatitis A risk areas in a region of Duque de Caxias, Rio de Janeiro State, Brazil. A cross-sectional study consisting of a hepatitis A serological survey and a household survey were conducted in 19 census tracts. Of these, 11 tracts were selected and 1,298 children from one to ten years of age were included in the study. Geostatistical techniques allowed modeling the spatial continuity of hepatitis A, non-use of filtered drinking water, time since installation of running water, and number of water taps per household and their spatial estimation through ordinary and indicator kriging. Adjusted models for the outcome and socioeconomic variables were isotropic; risk maps were constructed; cross-validation of the four models was satisfactory. Spatial estimation using the kriging method detected areas with increased risk of hepatitis A, independently of the urban administrative area in which the census tracts were located.
Estimating the probability of radiographic osteoarthritis in the older patient with knee pain.
Peat, George; Thomas, Elaine; Duncan, Rachel; Wood, Laurence; Wilkie, Ross; Hill, Jonathan; Hay, Elaine M; Croft, Peter
2007-06-15
To determine whether clinical information can practically rule in or rule out the presence of radiographic osteoarthritis in older adults with knee pain. We conducted a cross-sectional diagnostic study involving 695 adults ages >/=50 years reporting knee pain within the last year identified by postal survey and attending a research clinic. Potential indicators of radiographic osteoarthritis were gathered by self-complete questionnaires, clinical interview, and physical examination. Participants underwent plain radiography (posteroanterior, skyline, and lateral views). Radiographic osteoarthritis was defined as the presence of definite osteophytes in at least 1 joint compartment of the index knee. Independent predictors of radiographic osteoarthritis were age, sex, body mass index, absence of whole leg pain, traumatic onset, difficulty descending stairs, palpable effusion, fixed-flexion deformity, restricted-flexion range of motion, and crepitus. Using this model, 245 participants had a predicted probability >/=80% (practical rule in), of whom 231 (94%) actually had radiographic osteoarthritis (specificity 93%). Twenty-one participants had a predicted probability <20% (practical rule out), of whom only 2 (10%) had radiographic osteoarthritis (sensitivity 99.6%). The predicted probability of radiographic osteoarthritis for the remaining 429 participants fell into an intermediate category (20-79%). Simple clinical information can be used to estimate the probability of radiographic osteoarthritis in individual patients. However, for the majority of community-dwelling older adults with knee pain this method enables the presence of radiographic osteoarthritis to be neither confidently ruled in nor ruled out. Prospective validation and updating of these findings in an independent sample is required.
Estimating Probabilities of Peptide Database Identifications to LC-FTICR-MS Observations
Anderson, Kevin K.; Monroe, Matthew E.; Daly, Don S.
2006-02-24
One of the grand challenges in the post-genomic era is proteomics, the characterization of the proteins expressed in a cell under specific conditions. A promising technology for high-throughput proteomics is mass spectrometry, specifically liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS). The accuracy and certainty of the determinations of peptide identities and abundances provided by LC-FTICR-MS are an important and necessary component of systems biology research. Methods: After a tryptically digested protein mixture is analyzed by LC-FTICR-MS, the observed masses and normalized elution times of the detected features are statistically matched to the theoretical masses and elution times of known peptides listed in a large database. The probability of matching is estimated for each peptide in the reference database using statistical classification methods assuming bivariate Gaussian probability distributions on the uncertainties in the masses and the normalized elution times. A database of 69,220 features from 32 LC-FTICR-MS analyses of a tryptically digested bovine serum albumin (BSA) sample was matched to a database populated with 97% false positive peptides. The percentage of high confidence identifications was found to be consistent with other database search procedures. BSA database peptides were identified with high confidence on average in 14.1 of the 32 analyses. False positives were identified on average in just 2.7 analyses. Using a priori probabilities that contrast peptides from expected and unexpected proteins was shown to perform better in identifying target peptides than using equally likely a priori probabilities. This is because a large percentage of the target peptides were similar to unexpected peptides which were included to be false positives. The use of triplicate analyses with a ''2 out of 3'' reporting rule was shown to have excellent rejection of false positives.
Decisions under risk in Parkinson's disease: preserved evaluation of probability and magnitude.
Sharp, Madeleine E; Viswanathan, Jayalakshmi; McKeown, Martin J; Appel-Cresswell, Silke; Stoessl, A Jon; Barton, Jason J S
2013-11-01
Unmedicated Parkinson's disease patients tend to be risk-averse while dopaminergic treatment causes a tendency to take risks. While dopamine agonists may result in clinically apparent impulse control disorders, treatment with levodopa also causes shift in behaviour associated with an enhanced response to rewards. Two important determinants in decision-making are how subjects perceive the magnitude and probability of outcomes. Our objective was to determine if patients with Parkinson's disease on or off levodopa showed differences in their perception of value when making decisions under risk. The Vancouver Gambling task presents subjects with a choice between one prospect with larger outcome and a second with higher probability. Eighteen age-matched controls and eighteen patients with Parkinson's disease before and after levodopa were tested. In the Gain Phase subjects chose between one prospect with higher probability and another with larger reward to maximize their gains. In the Loss Phase, subjects played to minimize their losses. Patients with Parkinson's disease, on or off levodopa, were similar to controls when evaluating gains. However, in the Loss Phase before levodopa, they were more likely to avoid the prospect with lower probability but larger loss, as indicated by the steeper slope of their group psychometric function (t(24) = 2.21, p = 0.04). Modelling with prospect theory suggested that this was attributable to a 28% overestimation of the magnitude of loss, rather than an altered perception of its probability. While pre-medicated patients with Parkinson's disease show risk-aversion for large losses, patients on levodopa have normal perception of magnitude and probability for both loss and gain. The finding of accurate and normally biased decisions under risk in medicated patients with PD is important because it indicates that, if there is indeed anomalous risk-seeking behaviour in such a cohort, it may derive from abnormalities in components of
Tillery, Anne C.; Matherne, Anne Marie; Verdin, Kristine L.
2012-01-01
In May and June 2012, the Whitewater-Baldy Fire burned approximately 1,200 square kilometers (300,000 acres) of the Gila National Forest, in southwestern New Mexico. The burned landscape is now at risk of damage from postwildfire erosion, such as that caused by debris flows and flash floods. This report presents a preliminary hazard assessment of the debris-flow potential from 128 basins burned by the Whitewater-Baldy Fire. A pair of empirical hazard-assessment models developed by using data from recently burned basins throughout the intermountain Western United States was used to estimate the probability of debris-flow occurrence and volume of debris flows along the burned area drainage network and for selected drainage basins within the burned area. The models incorporate measures of areal burned extent and severity, topography, soils, and storm rainfall intensity to estimate the probability and volume of debris flows following the fire. In response to the 2-year-recurrence, 30-minute-duration rainfall, modeling indicated that four basins have high probabilities of debris-flow occurrence (greater than or equal to 80 percent). For the 10-year-recurrence, 30-minute-duration rainfall, an additional 14 basins are included, and for the 25-year-recurrence, 30-minute-duration rainfall, an additional eight basins, 20 percent of the total, have high probabilities of debris-flow occurrence. In addition, probability analysis along the stream segments can identify specific reaches of greatest concern for debris flows within a basin. Basins with a high probability of debris-flow occurrence were concentrated in the west and central parts of the burned area, including tributaries to Whitewater Creek, Mineral Creek, and Willow Creek. Estimated debris-flow volumes ranged from about 3,000-4,000 cubic meters (m3) to greater than 500,000 m3 for all design storms modeled. Drainage basins with estimated volumes greater than 500,000 m3 included tributaries to Whitewater Creek, Willow
Fast and accurate probability density estimation in large high dimensional astronomical datasets
NASA Astrophysics Data System (ADS)
Gupta, Pramod; Connolly, Andrew J.; Gardner, Jeffrey P.
2015-01-01
Astronomical surveys will generate measurements of hundreds of attributes (e.g. color, size, shape) on hundreds of millions of sources. Analyzing these large, high dimensional data sets will require efficient algorithms for data analysis. An example of this is probability density estimation that is at the heart of many classification problems such as the separation of stars and quasars based on their colors. Popular density estimation techniques use binning or kernel density estimation. Kernel density estimation has a small memory footprint but often requires large computational resources. Binning has small computational requirements but usually binning is implemented with multi-dimensional arrays which leads to memory requirements which scale exponentially with the number of dimensions. Hence both techniques do not scale well to large data sets in high dimensions. We present an alternative approach of binning implemented with hash tables (BASH tables). This approach uses the sparseness of data in the high dimensional space to ensure that the memory requirements are small. However hashing requires some extra computation so a priori it is not clear if the reduction in memory requirements will lead to increased computational requirements. Through an implementation of BASH tables in C++ we show that the additional computational requirements of hashing are negligible. Hence this approach has small memory and computational requirements. We apply our density estimation technique to photometric selection of quasars using non-parametric Bayesian classification and show that the accuracy of the classification is same as the accuracy of earlier approaches. Since the BASH table approach is one to three orders of magnitude faster than the earlier approaches it may be useful in various other applications of density estimation in astrostatistics.
Estimating the probability of arsenic occurrence in domestic wells in the United States
NASA Astrophysics Data System (ADS)
Ayotte, J.; Medalie, L.; Qi, S.; Backer, L. F.; Nolan, B. T.
2016-12-01
Approximately 43 million people (about 14 percent of the U.S. population) rely on privately owned domestic wells as their source of drinking water. Unlike public water systems, which are regulated by the Safe Drinking Water Act, there is no comprehensive national program to ensure that the water from domestic wells is routinely tested and that is it safe to drink. A study published in 2009 from the National Water-Quality Assessment Program of the U.S. Geological Survey assessed water-quality conditions from 2,100 domestic wells within 48 states and reported that more than one in five (23 percent) of the sampled wells contained one or more contaminants at a concentration greater than a human-health benchmark. In addition, there are many activities such as resource extraction, climate change-induced drought, and changes in land use patterns that could potentially affect the quality of the ground water source for domestic wells. The Health Studies Branch (HSB) of the National Center for Environmental Health, Centers for Disease Control and Prevention, created a Clean Water for Health Program to help address domestic well concerns. The goals of this program are to identify emerging public health issues associated with using domestic wells for drinking water and develop plans to address these issues. As part of this effort, HSB in cooperation with the U.S. Geological Survey has created probability models to estimate the probability of arsenic occurring at various concentrations in domestic wells in the U.S. We will present preliminary results of the project, including estimates of the population supplied by domestic wells that is likely to have arsenic greater than 10 micrograms per liter. Nationwide, we estimate this to be just over 2 million people. Logistic regression model results showing probabilities of arsenic greater than the Maximum Contaminant Level for public supply wells of 10 micrograms per liter in domestic wells in the U.S., based on data for arsenic
NASA Astrophysics Data System (ADS)
Eleftheriadou, Anastasia K.; Baltzopoulou, Aikaterini D.; Karabinis, Athanasios I.
2016-06-01
The current seismic risk assessment is based on two discrete approaches, actual and probable, validating afterwards the produced results. In the first part of this research, the seismic risk is evaluated from the available data regarding the mean statistical repair/strengthening or replacement cost for the total number of damaged structures (180,427 buildings) after the 7/9/1999 Parnitha (Athens) earthquake. The actual evaluated seismic risk is afterwards compared to the estimated probable structural losses, which is presented in the second part of the paper, based on a damage scenario in the referring earthquake. The applied damage scenario is based on recently developed damage probability matrices (DPMs) from Athens (Greece) damage database. The seismic risk estimation refers to 750,085 buildings situated in the extended urban region of Athens. The building exposure is categorized in five typical structural types and represents 18.80 % of the entire building stock in Greece. The last information is provided by the National Statistics Service of Greece (NSSG) according to the 2000-2001 census. The seismic input is characterized by the ratio, a g/ a o, where a g is the regional peak ground acceleration (PGA) which is evaluated from the earlier estimated research macroseismic intensities, and a o is the PGA according to the hazard map of the 2003 Greek Seismic Code. Finally, the collected investigated financial data derived from different National Services responsible for the post-earthquake crisis management concerning the repair/strengthening or replacement costs or other categories of costs for the rehabilitation of earthquake victims (construction and function of settlements for earthquake homeless, rent supports, demolitions, shorings) are used to determine the final total seismic risk factor.
Fatality risk estimation for Replacement Tritium Facility
Kim, K.S.
1994-09-01
Prompt and latent cancer fatality risks are estimated in this report for the operation of the Replacement Tritium Facility (RTF) at the Savannah River Site (SRS). The purpose of this report is to demonstrate that: (1) the calculated fatality risk for the RTF operation is well within the quantitative Nuclear Safety Goals established by the Department of Energy (DOE) and (2) a simple point estimate method can produce results comparable to those of more detailed calculation utilizing computer codes and protocols developed for Probabilistic Risk Assessment (PRA) of nuclear power reactors. Point estimates of prompt and cancer fatality risks are performed using a simple mathematical formalism derived from the complementary cumulative distribution function (CCDF) of consequences. The protocol of establishing CCDF is based on a successive summation of event frequencies. The consequences (i.e., calculated individual radiation doses at site boundary) and associated event frequencies are available from the RTF Final Safety Analysis Report (FSAR). The results indicate that calculated prompt fatality and cancer fatality risks due to RTF operation are 0 and 1.5 {times} 10{sup {minus}9}/yr, respectively, well below the DOE`s Safety Goals of 5 {times} 10{sup {minus}7}/yr (prompt fatality) and 2 {times} 10{sup {minus}6}/yr (cancer fatality). The agreement between the point estimate method and the PRA method is very good considering the differences in assumptions between the two methods (i.e., additional earthquake-induced scenarios).
NASA Astrophysics Data System (ADS)
Mazzoleni, Maurizio; Brandimarte, Luigia; Barontini, Stefano; Ranzi, Roberto
2014-05-01
Over the centuries many societies have preferred to settle down nearby floodplains area and take advantage of the favorable environmental conditions. Due to changing hydro-meteorological conditions, over time, levee systems along rivers have been raised to protect urbanized area and reduce the impact of floods. As expressed by the so called "levee paradox", many societies might to tend to trust these levee protection systems due to an induced sense of safety and, as a consequence, invest even more in urban developing in levee protected flood prone areas. As a result, considering also the increasing number of population around the world, people living in floodplains is growing. However, human settlements in floodplains are not totally safe and have been continuously endangered by the risk of flooding. In fact, failures of levee system in case of flood event have also produced the most devastating disasters of the last two centuries due to the exposure of the developed floodprone areas to risk. In those cases, property damage is certain, but loss of life can vary dramatically with the extent of the inundation area, the size of the population at risk, and the amount of warning time available. The aim of this study is to propose an innovative methodology to estimate the reliability of a general river levee system in case of piping, considering different sources of uncertainty, and analyze the influence of different discretization of the river reach in sub-reaches in the evaluation of the probability of failure. The reliability analysis, expressed in terms of fragility curve, was performed evaluating the probability of failure, conditioned by a given hydraulic load in case of a certain levee failure mechanism, using a Monte Carlo and First Order Reliability Method. Knowing the information about fragility curve for each discrete levee reach, different fragility indexes were introduced. Using the previous information was then possible to classify the river into sub
Accretion of Fine Particles: Sticking Probability Estimated by Optical Sizing of Fractal Aggregates
NASA Astrophysics Data System (ADS)
Sugiura, N.; Higuchi, Y.
1993-07-01
Sticking probability of fine particles is an important parameter that determines (1) the settling of fine particles to the equatorial plane of the solar nebula and hence the formation of planetesimals, and (2) the thermal structure of the nebula, which is dependent on the particle size through opacity. It is generally agreed that the sticking probability is 1 for submicrometer particles, but at sizes larger than 1 micrometer, there exist almost no data on the sticking probability. A recent study [1] showed that aggregates (with radius from 0.2 to 2 mm) did not stick when collided at a speed of 0.15 to 4 m/s. Therefore, somewhere between 1 micrometer and 200 micrometers, sticking probabilities of fine particles change from nearly 1 to nearly 0. We have been studying [2,3] sticking probabilities of dust aggregates in this size range using an optical sizing method. The optical sizing method has been well established for spherical particles. This method utilizes the fact that the smaller the size, the larger the angle of the scattered light. For spheres with various sizes, the size distribution is determined by solving Y(i) = M(i,j)X(j), where Y(i) is the scattered light intensity at angle i, X(j) is the number density of spheres with size j, and M(i,j) is the scattering matrix, which is determined by Mie theory. Dust aggregates, which we expect to be present in the early solar nebula, are not solid spheres, but probably have a porous fractal structure. For such aggregates the scattering matrix M(i,j) must be determined by taking account of all the interaction among constituent particles (discrete dipole approximation). Such calculation is possible only for very small aggregates, and for larger aggregates we estimate the scattering matrix by extrapolation, assuming that the fractal nature of the aggregates allows such extrapolation. In the experiments using magnesium oxide fine particles floating in a chamber at ambient pressure, the size distribution (determined by
NASA Astrophysics Data System (ADS)
Cavuoti, S.; Amaro, V.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.
2017-02-01
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z probability density function (PDF), due to the fact that the analytical relation mapping the photometric parameters on to the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine-learning model chosen to predict photo-z. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDFs obtained by the LE PHARE spectral energy distribution template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.
Estimating the distribution of probable age-at-death from dental remains of immature human fossils.
Shackelford, Laura L; Stinespring Harris, Ashley E; Konigsberg, Lyle W
2012-02-01
In two historic longitudinal growth studies, Moorrees et al. (Am J Phys Anthropol 21 (1963) 99-108; J Dent Res 42 (1963) 1490-1502) presented the "mean attainment age" for stages of tooth development for 10 permanent tooth types and three deciduous tooth types. These findings were presented graphically to assess the rate of tooth formation in living children and to age immature skeletal remains. Despite being widely cited, these graphical data are difficult to implement because there are no accompanying numerical values for the parameters underlying the growth data. This analysis generates numerical parameters from the data reported by Moorrees et al. by digitizing 358 points from these tooth formation graphs using DataThief III, version 1.5. Following the original methods, the digitized points for each age transition were conception-corrected and converted to the logarithmic scale to determine a median attainment age for each dental formation stage. These values are subsequently used to estimate age-at-death distributions for immature individuals using a single tooth or multiple teeth, including estimates for 41 immature early modern humans and 25 immature Neandertals. Within-tooth variance is calculated for each age estimate based on a single tooth, and a between-tooth component of variance is calculated for age estimates based on two or more teeth to account for the increase in precision that comes from using additional teeth. Finally, we calculate the relative probability of observing a particular dental formation sequence given known-age reference information and demonstrate its value in estimating age for immature fossil specimens. Copyright © 2011 Wiley Periodicals, Inc.
Spatial ascariasis risk estimation using socioeconomic variables.
Valencia, Luis Iván Ortiz; Fortes, Bruno de Paula Menezes Drumond; Medronho, Roberto de Andrade
2005-12-01
Frequently, disease incidence is mapped as area data, for example, census tracts, districts or states. Spatial disease incidence can be highly heterogeneous inside these areas. Ascariasis is a highly prevalent disease, which is associated with poor sanitation and hygiene. Geostatistics was applied to model spatial distribution of Ascariasis risk and socioeconomic risk events in a poor community in Rio de Janeiro, Brazil. Data were gathered from a coproparasitologic and a domiciliary survey in 1550 children aged 1-9. Ascariasis risk and socioeconomic risk events were spatially estimated using Indicator Kriging. Cokriging models with a Linear Model of Coregionalization incorporating one socioeconomic variable were implemented. If a housewife attended school for less than four years, the non-use of a home water filter, a household density greater than one, and a household income lower than one Brazilian minimum wage increased the risk of Ascariasis. Cokriging improved spatial estimation of Ascariasis risk areas when compared to Indicator Kriging and detected more Ascariasis very-high risk areas than the GIS Overlay method.
Methods for estimating dispersal probabilities and related parameters using marked animals
Bennetts, R.E.; Nichols, J.D.; Pradel, R.; Lebreton, J.D.; Kitchens, W.M.; Clobert, Jean; Danchin, Etienne; Dhondt, Andre A.; Nichols, James D.
2001-01-01
Deriving valid inferences about the causes and consequences of dispersal from empirical studies depends largely on our ability reliably to estimate parameters associated with dispersal. Here, we present a review of the methods available for estimating dispersal and related parameters using marked individuals. We emphasize methods that place dispersal in a probabilistic framework. In this context, we define a dispersal event as a movement of a specified distance or from one predefined patch to another, the magnitude of the distance or the definition of a `patch? depending on the ecological or evolutionary question(s) being addressed. We have organized the chapter based on four general classes of data for animals that are captured, marked, and released alive: (1) recovery data, in which animals are recovered dead at a subsequent time, (2) recapture/resighting data, in which animals are either recaptured or resighted alive on subsequent sampling occasions, (3) known-status data, in which marked animals are reobserved alive or dead at specified times with probability 1.0, and (4) combined data, in which data are of more than one type (e.g., live recapture and ring recovery). For each data type, we discuss the data required, the estimation techniques, and the types of questions that might be addressed from studies conducted at single and multiple sites.
Emg Amplitude Estimators Based on Probability Distribution for Muscle-Computer Interface
NASA Astrophysics Data System (ADS)
Phinyomark, Angkoon; Quaine, Franck; Laurillau, Yann; Thongpanja, Sirinee; Limsakul, Chusak; Phukpattaranont, Pornchai
To develop an advanced muscle-computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.
SAR amplitude probability density function estimation based on a generalized Gaussian model.
Moser, Gabriele; Zerubia, Josiane; Serpico, Sebastiano B
2006-06-01
In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed "method-of-log-cumulants" (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena.
ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning
NASA Astrophysics Data System (ADS)
Sadeh, I.; Abdalla, F. B.; Lahav, O.
2016-10-01
We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister & Lahav, which now includes generation of full probability distribution functions (PDFs). ANNz2 utilizes multiple machine learning methods, such as artificial neural networks and boosted decision/regression trees. The objective of the algorithm is to optimize the performance of the photo-z estimation, to properly derive the associated uncertainties, and to produce both single-value solutions and PDFs. In addition, estimators are made available, which mitigate possible problems of non-representative or incomplete spectroscopic training samples. ANNz2 has already been used as part of the first weak lensing analysis of the Dark Energy Survey, and is included in the experiment's first public data release. Here we illustrate the functionality of the code using data from the tenth data release of the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey. The code is available for download at http://github.com/IftachSadeh/ANNZ.
Lin, Feng; Chen, Xinguang
2009-01-01
In order to find better strategies for tobacco control, it is often critical to know the transitional probabilities among various stages of tobacco use. Traditionally, such probabilities are estimated by analyzing data from longitudinal surveys that are often time-consuming and expensive to conduct. Since cross-sectional surveys are much easier to conduct, it will be much more practical and useful to estimate transitional probabilities from cross-sectional survey data if possible. However, no previous research has attempted to do this. In this paper, we propose a method to estimate transitional probabilities from cross-sectional survey data. The method is novel and is based on a discrete event system framework. In particular, we introduce state probabilities and transitional probabilities to conventional discrete event system models. We derive various equations that can be used to estimate the transitional probabilities. We test the method using cross-sectional data of the National Survey on Drug Use and Health. The estimated transitional probabilities can be used in predicting the future smoking behavior for decision-making, planning and evaluation of various tobacco control programs. The method also allows a sensitivity analysis that can be used to find the most effective way of tobacco control. Since there are much more cross-sectional survey data in existence than longitudinal ones, the impact of this new method is expected to be significant. PMID:20161437
Risk estimation in partial duration series
NASA Astrophysics Data System (ADS)
Rasmussen, Peter Funder; Rosbjerg, Dan
1989-11-01
The estimation of design floods is, in practice, often based on small samples of data, which may cause a severe uncertainty. For a particular version of the partial duration series (exponentially distributed exceedances and Poissonian occurrence times) the distribution of the T-year design estimate xˆT is derived along with the distribution of RT; defined as the true risk of exceeding xˆT within a given disposal period. For a fixed flood level the distributions of the return period estimator Tˆ and the estimator of the risk in lifetime Rˆ are also presented. Analytical closed-form expressions for mean value and standard deviation are derived for these variables, except for Tˆ, which does not possess moments. The concept of "expected risk" is introduced, and an analytical expression describing this property is derived. A risk-based design technique, which is essentially different from the traditional procedure, is presented, and its applicability is verified using Monte Carlo simulation.
Estimating the probability for a protein to have a new fold: A statistical computational model
Portugaly, Elon; Linial, Michal
2000-01-01
Structural genomics aims to solve a large number of protein structures that represent the protein space. Currently an exhaustive solution for all structures seems prohibitively expensive, so the challenge is to define a relatively small set of proteins with new, currently unknown folds. This paper presents a method that assigns each protein with a probability of having an unsolved fold. The method makes extensive use of protomap, a sequence-based classification, and scop, a structure-based classification. According to protomap, the protein space encodes the relationship among proteins as a graph whose vertices correspond to 13,354 clusters of proteins. A representative fold for a cluster with at least one solved protein is determined after superposition of all scop (release 1.37) folds onto protomap clusters. Distances within the protomap graph are computed from each representative fold to the neighboring folds. The distribution of these distances is used to create a statistical model for distances among those folds that are already known and those that have yet to be discovered. The distribution of distances for solved/unsolved proteins is significantly different. This difference makes it possible to use Bayes' rule to derive a statistical estimate that any protein has a yet undetermined fold. Proteins that score the highest probability to represent a new fold constitute the target list for structural determination. Our predicted probabilities for unsolved proteins correlate very well with the proportion of new folds among recently solved structures (new scop 1.39 records) that are disjoint from our original training set. PMID:10792051
Ventricular fibrillation risk estimation for conducted electrical weapons: critical convolutions.
Kroll, Mark W; Lakkireddy, Dhanunjaya; Rahko, Peter S; Panescu, Dorin
2011-01-01
The TASER® Conducted Electrical Weapon (CEW) is used by law enforcement agencies about 900 times per day worldwide and has been shown to reduce suspect and officer injuries by about 65%. However, since a CEW delivers rapid electrical pulses through injected probes, the risk of inducing ventricular fibrillation (VF) has been considered. Animal studies have shown that the tip of the probe must come within a few millimeters of the surface of the heart for the CEW to induce VF in a typical animal application. Early calculations of the CEW VF risk in humans used sophisticated 3-D chest models to determine the size of the probe landing areas that had cardiac tissue within a given distance of the inner surface of the ribs. This produced a distribution of area (cm(2)) vs. mm of depth. Echocardiography was then used to determine the shortest distance from the skin surface to the cardiac surface. This produced a population distribution of skin-to-heart (STH) distances. These 2 distributions were then convolved to arrive at a probability of inducing VF for a typical human CEW application. With 900, 000 probe-mode field uses to date, epidemiological results have shown that these initial VF risk estimates were significant overestimates. We present model refinements that take into account the gender and body-mass-index (BMI) of the target demographics and produce VF risk estimates concordant with the epidemiological results. The risk of VF is estimated at 0.4 per million uses with males.
Adams, Vanessa M.; Pressey, Robert L.; Stoeckl, Natalie
2014-01-01
The need to integrate social and economic factors into conservation planning has become a focus of academic discussions and has important practical implications for the implementation of conservation areas, both private and public. We conducted a survey in the Daly Catchment, Northern Territory, to inform the design and implementation of a stewardship payment program. We used a choice model to estimate the likely level of participation in two legal arrangements - conservation covenants and management agreements - based on payment level and proportion of properties required to be managed. We then spatially predicted landholders’ probability of participating at the resolution of individual properties and incorporated these predictions into conservation planning software to examine the potential for the stewardship program to meet conservation objectives. We found that the properties that were least costly, per unit area, to manage were also the least likely to participate. This highlights a tension between planning for a cost-effective program and planning for a program that targets properties with the highest probability of participation. PMID:24892520
Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation
Jordan, P.D.; Oldenburg, C.M.; Nicot, J.-P.
2011-05-15
Emission of carbon dioxide from fossil-fueled power generation stations contributes to global climate change. Storage of this carbon dioxide within the pores of geologic strata (geologic carbon storage) is one approach to mitigating the climate change that would otherwise occur. The large storage volume needed for this mitigation requires injection into brine-filled pore space in reservoir strata overlain by cap rocks. One of the main concerns of storage in such rocks is leakage via faults. In the early stages of site selection, site-specific fault coverages are often not available. This necessitates a method for using available fault data to develop an estimate of the likelihood of injected carbon dioxide encountering and migrating up a fault, primarily due to buoyancy. Fault population statistics provide one of the main inputs to calculate the encounter probability. Previous fault population statistics work is shown to be applicable to areal fault density statistics. This result is applied to a case study in the southern portion of the San Joaquin Basin with the result that the probability of a carbon dioxide plume from a previously planned injection had a 3% chance of encountering a fully seal offsetting fault.
Smith, L.L.; Barichivich, W.J.; Staiger, J.S.; Smith, Kimberly G.; Dodd, C.K.
2006-01-01
We conducted an amphibian inventory at Okefenokee National Wildlife Refuge from August 2000 to June 2002 as part of the U.S. Department of the Interior's national Amphibian Research and Monitoring Initiative. Nineteen species of amphibians (15 anurans and 4 caudates) were documented within the Refuge, including one protected species, the Gopher Frog Rana capito. We also collected 1 y of monitoring data for amphibian populations and incorporated the results into the inventory. Detection probabilities and site occupancy estimates for four species, the Pinewoods Treefrog (Hyla femoralis), Pig Frog (Rana grylio), Southern Leopard Frog (R. sphenocephala) and Carpenter Frog (R. virgatipes) are presented here. Detection probabilities observed in this study indicate that spring and summer surveys offer the best opportunity to detect these species in the Refuge. Results of the inventory suggest that substantial changes may have occurred in the amphibian fauna within and adjacent to the swamp. However, monitoring the amphibian community of Okefenokee Swamp will prove difficult because of the logistical challenges associated with a rigorous statistical assessment of status and trends.
Estimation of the failure probability during EGS stimulation based on borehole data
NASA Astrophysics Data System (ADS)
Meller, C.; Kohl, Th.; Gaucher, E.
2012-04-01
In recent times the search for alternative sources of energy has been fostered by the scarcity of fossil fuels. With its ability to permanently provide electricity or heat with little emission of CO2, geothermal energy will have an important share in the energy mix of the future. Within Europe, scientists identified many locations with conditions suitable for Enhanced Geothermal System (EGS) projects. In order to provide sufficiently high reservoir permeability, EGS require borehole stimulations prior to installation of power plants (Gérard et al, 2006). Induced seismicity during water injection into reservoirs EGS systems is a factor that currently cannot be predicted nor controlled. Often, people living near EGS projects are frightened by smaller earthquakes occurring during stimulation or injection. As this fear can lead to widespread disapproval of geothermal power plants, it is appreciable to find a way to estimate the probability of fractures to shear when injecting water with a distinct pressure into a geothermal reservoir. This provides knowledge, which enables to predict the mechanical behavior of a reservoir in response to a change in pore pressure conditions. In the present study an approach for estimation of the shearing probability based on statistical analyses of fracture distribution, orientation and clusters, together with their geological properties is proposed. Based on geophysical logs of five wells in Soultz-sous-Forêts, France, and with the help of statistical tools, the Mohr criterion, geological and mineralogical properties of the host rock and the fracture fillings, correlations between the wells are analyzed. This is achieved with the self-written MATLAB-code Fracdens, which enables us to statistically analyze the log files in different ways. With the application of a pore pressure change, the evolution of the critical pressure on the fractures can be determined. A special focus is on the clay fillings of the fractures and how they reduce
Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics.
Holland, Dominic; Wang, Yunpeng; Thompson, Wesley K; Schork, Andrew; Chen, Chi-Hua; Lo, Min-Tzu; Witoelar, Aree; Werge, Thomas; O'Donovan, Michael; Andreassen, Ole A; Dale, Anders M
2016-01-01
Genome-wide Association Studies (GWAS) result in millions of summary statistics ("z-scores") for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N = 82,315) and putamen volume (N = 12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes
Estimated Probability of Traumatic Abdominal Injury During an International Space Station Mission
NASA Technical Reports Server (NTRS)
Lewandowski, Beth E.; Brooker, John E.; Weavr, Aaron S.; Myers, Jerry G., Jr.; McRae, Michael P.
2013-01-01
The Integrated Medical Model (IMM) is a decision support tool that is useful to spaceflight mission planners and medical system designers when assessing risks and optimizing medical systems. The IMM project maintains a database of medical conditions that could occur during a spaceflight. The IMM project is in the process of assigning an incidence rate, the associated functional impairment, and a best and a worst case end state for each condition. The purpose of this work was to develop the IMM Abdominal Injury Module (AIM). The AIM calculates an incidence rate of traumatic abdominal injury per person-year of spaceflight on the International Space Station (ISS). The AIM was built so that the probability of traumatic abdominal injury during one year on ISS could be predicted. This result will be incorporated into the IMM Abdominal Injury Clinical Finding Form and used within the parent IMM model.
Estimates of health risk from exposure to radioactive pollutants
Sullivan, R.E.; Nelson, N.S.; Ellett, W.H.; Dunning, D.E. Jr.; Leggett, R.W.; Yalcintas, M.G.; Eckerman, K.F.
1981-11-01
A dosimetric and health effects analysis has been performed for the Office of Radiation Programs of the Environmental Protection Agency (EPA) to assess potential hazards from radioactive pollutants. Contemporary dosimetric methods were used to obtain estimates of dose rates to reference organs from internal exposures due to either inhalation of contaminated air or ingestion of contaminated food, or from external exposures due to either immersion in contaminated air or proximity to contaminated ground surfaces. These dose rates were then used to estimate the number of premature cancer deaths arising from such exposures and the corresponding number of years of life lost in a cohort of 100,000 persons, all simultaneously liveborn and all going through life with the same risks of dying from competing causes. The risk of dying from a competing cause for a given year was taken to be the probability of dying from all causes as given in a recent actuarial life table for the total US population.
Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo
2017-02-01
Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.
NASA Astrophysics Data System (ADS)
Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo
2017-02-01
Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.
NASA Astrophysics Data System (ADS)
Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo
2016-07-01
Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.
NASA Astrophysics Data System (ADS)
Castellarin, A.; Guse, B.; Pugliese, A.
2013-12-01
Envelope curves of flood flows are classical hydrological tools that graphically summarize the current bound on our experience of extreme floods in a region. Probabilistic Regional Envelope Curves (PRECs) have been recently introduced in the literature, as well as an empirical estimator of the return period, RP, associated with the curves. PRECs can be used to estimate the RP-year flood (design-flood) for any basin in a given region as a function of the catchment area alone. We present a collection of R-functions that can be used for (1) constructing the empirical envelope curve of flood flows for a given hydrological region and (2) estimating the curve's RP on the basis of a mathematical representation of the cross-correlation structure of observed flood sequences. The R-functions, which we tested on synthetic regional datasets of annual sequences characterized by different degrees of cross-correlation generated through Monte Carlo resampling, provide the user with straightforward means for predicting the exceedance probability, 1/RP, associated with a regional envelope curve, and therefore the RP-year flood in any ungauged basin in the study region for large and very large RP values (e.g. hundreds of years). Furthermore, the R-tools can be easily coupled with other regional flood frequency analysis procedures to effectively improve the accuracy of flood quantile estimates at high RP values, or extended to rainfall extremes for predicting extreme point-rainfall depths associated with a given duration and recurrence interval in any ungauged site within a region.
NASA Astrophysics Data System (ADS)
Haigh, Ivan D.; Wijeratne, E. M. S.; MacPherson, Leigh R.; Pattiaratchi, Charitha B.; Mason, Matthew S.; Crompton, Ryan P.; George, Steve
2014-01-01
The occurrence of extreme water levels along low-lying, highly populated and/or developed coastlines can lead to considerable loss of life and billions of dollars of damage to coastal infrastructure. Therefore it is vitally important that the exceedance probabilities of extreme water levels are accurately evaluated to inform risk-based flood management, engineering and future land-use planning. This ensures the risk of catastrophic structural failures due to under-design or expensive wastes due to over-design are minimised. This paper estimates for the first time present day extreme water level exceedence probabilities around the whole coastline of Australia. A high-resolution depth averaged hydrodynamic model has been configured for the Australian continental shelf region and has been forced with tidal levels from a global tidal model and meteorological fields from a global reanalysis to generate a 61-year hindcast of water levels. Output from this model has been successfully validated against measurements from 30 tide gauge sites. At each numeric coastal grid point, extreme value distributions have been fitted to the derived time series of annual maxima and the several largest water levels each year to estimate exceedence probabilities. This provides a reliable estimate of water level probabilities around southern Australia; a region mainly impacted by extra-tropical cyclones. However, as the meteorological forcing used only weakly includes the effects of tropical cyclones, extreme water level probabilities are underestimated around the western, northern and north-eastern Australian coastline. In a companion paper we build on the work presented here and more accurately include tropical cyclone-induced surges in the estimation of extreme water level. The multi-decadal hindcast generated here has been used primarily to estimate extreme water level exceedance probabilities but could be used more widely in the future for a variety of other research and practical
Bratt, Ola; Drevin, Linda; Akre, Olof; Garmo, Hans; Stattin, Pär
2016-10-01
Familial prostate cancer risk estimates are inflated by clinically insignificant low-risk cancer, diagnosed after prostate-specific antigen testing. We provide age-specific probabilities of non-low- and high-risk prostate cancer. Fifty-one thousand, eight hundred ninety-seven brothers of 32 807 men with prostate cancer were identified in Prostate Cancer data Base Sweden (PCBaSe). Nelson-Aalen estimates with 95% confidence intervals (CIs) were calculated for cumulative, family history-stratified probabilities of any, non-low- (any of Gleason score ≥ 7, prostate-specific antigen [PSA] ≥ 10 ng/mL, T3-4, N1, and/or M1) and high-risk prostate cancer (Gleason score ≥ 8 and/or T3-4 and/or PSA ≥ 20 ng/mL and/or N1 and/or M1). The population probability of any prostate cancer was 4.8% (95% CI = 4.8% to 4.9%) at age 65 years and 12.9% (95% CI = 12.8% to 12.9%) at age 75 years, of non-low-risk prostate cancer 2.8% (95% CI = 2.7% to 2.8%) at age 65 years and 8.9% (95% CI = 8.8% to 8.9%) at age 75 years, and of high-risk prostate cancer 1.4% (95% CI = 1.3% to 1.4%) at age 65 years and 5.2% (95% CI = 5.1% to 5.2%) at age 75 years. For men with one affected brother, probabilities of any prostate cancer were 14.9% (95% CI = 14.1% to 15.8%) at age 65 years and 30.3% (95% CI = 29.3% to 31.3%) at age 75 years, of non-low-risk prostate cancer 7.3% (95% CI = 6.7% to 7.9%) at age 65 years and 18.8% (95% CI = 17.9% to 19.6%) at age 75 years, and of high-risk prostate cancer 3.0% (95% CI = 2.6% to 3.4%) at age 65 years and 8.9% (95% CI = 8.2% to 9.5%) at age 75 years. Probabilities were higher for men with a stronger family history. For example, men with two affected brothers had a 13.6% (95% CI = 9.9% to 17.6 %) probability of high-risk cancer at age 75 years. The age-specific probabilities of non-low- and high-risk cancer presented here are more informative than relative risks of any prostate cancer and more suitable to use
Quantitative cancer risk estimation for formaldehyde
Starr, T.B. )
1990-03-01
Of primary concern are irreversible effects, such as cancer induction, that formaldehyde exposure could have on human health. Dose-response data from human exposure situations would provide the most solid foundation for risk assessment, avoiding problematic extrapolations from the health effects seen in nonhuman species. However, epidemiologic studies of human formaldehyde exposure have provided little definitive information regarding dose-response. Reliance must consequently be placed on laboratory animal evidence. An impressive array of data points to significantly nonlinear relationships between rodent tumor incidence and administered dose, and between target tissue dose and administered dose (the latter for both rodents and Rhesus monkeys) following exposure to formaldehyde by inhalation. Disproportionately less formaldehyde binds covalently to the DNA of nasal respiratory epithelium at low than at high airborne concentrations. Use of this internal measure of delivered dose in analyses of rodent bioassay nasal tumor response yields multistage model estimates of low-dose risk, both point and upper bound, that are lower than equivalent estimates based upon airborne formaldehyde concentration. In addition, risk estimates obtained for Rhesus monkeys appear at least 10-fold lower than corresponding estimates for identically exposed Fischer-344 rats. 70 references.
Elliott, John G.; Flynn, Jennifer L.; Bossong, Clifford R.; Char, Stephen J.
2011-01-01
The subwatersheds with the greatest potential postwildfire and postprecipitation hazards are those with both high probabilities of debris-flow occurrence and large estimated volumes of debris-flow material. The high probabilities of postwildfire debris flows, the associated large estimated debris-flow volumes, and the densely populated areas along the creeks and near the outlets of the primary watersheds indicate that Indiana, Pennsylvania, and Spruce Creeks are associated with a relatively high combined debris-flow hazard.
Nonlinear Memory and Risk Estimation in Financial Records
NASA Astrophysics Data System (ADS)
Bunde, Armin; Bogachev, Mikhail I.
It is well known that financial data sets are multifractal and governed by nonlinear correlations. Here we are interested in the daily returns of a financial asset and in the way the occurrence of large gains or losses is triggered by the nonlinear memory. To this end, we study the statistics of the return intervals between gains (or losses) above a certain threshold Q. In the case of i.i.d. random numbers the probability density function (pdf) of the return intervals decays exponentially and the return intervals are uncorrelated. Here we show that the nonlinear correlations lead to a power law decay of the pdf and linear long-term correlations between the return intervals that are described by a power-law decay of the corresponding autocorrelation function. From the pdf of the return intervals one obtains the risk function W Q (t; Δt), which is the probability that within the next Δt units of time at least one event above Q occurs, if the last event occurred t time units ago. We propose an analytical estimate of W Q and show explicitly that the proposed method is superior to the conventional precursory pattern recognition technique widely used in signal analysis, which requires considerable fine-tuning and is difficult to implement. We also show that the estimation of the Value at Risk, which is a standard tool in finances, can be improved considerably compared with previous estimates.
Nielson, Ryan M.; Gray, Brian R.; McDonald, Lyman L.; Heglund, Patricia J.
2011-01-01
Estimation of site occupancy rates when detection probabilities are <1 is well established in wildlife science. Data from multiple visits to a sample of sites are used to estimate detection probabilities and the proportion of sites occupied by focal species. In this article we describe how site occupancy methods can be applied to estimate occupancy rates of plants and other sessile organisms. We illustrate this approach and the pitfalls of ignoring incomplete detection using spatial data for 2 aquatic vascular plants collected under the Upper Mississippi River's Long Term Resource Monitoring Program (LTRMP). Site occupancy models considered include: a naïve model that ignores incomplete detection, a simple site occupancy model assuming a constant occupancy rate and a constant probability of detection across sites, several models that allow site occupancy rates and probabilities of detection to vary with habitat characteristics, and mixture models that allow for unexplained variation in detection probabilities. We used information theoretic methods to rank competing models and bootstrapping to evaluate the goodness-of-fit of the final models. Results of our analysis confirm that ignoring incomplete detection can result in biased estimates of occupancy rates. Estimates of site occupancy rates for 2 aquatic plant species were 19–36% higher compared to naive estimates that ignored probabilities of detection <1. Simulations indicate that final models have little bias when 50 or more sites are sampled, and little gains in precision could be expected for sample sizes >300. We recommend applying site occupancy methods for monitoring presence of aquatic species.
NASA Astrophysics Data System (ADS)
Abadie, Luis Maria; Galarraga, Ibon; Sainz de Murieta, Elisa
2017-01-01
A quantification of present and future mean annual losses due to extreme coastal events can be crucial for adequate decision making on adaptation to climate change in coastal areas around the globe. However, this approach is limited when uncertainty needs to be accounted for. In this paper, we assess coastal flood risk from sea-level rise and extreme events in 120 major cities around the world using an alternative stochastic approach that accounts for uncertainty. Probability distributions of future relative (local) sea-level rise have been used for each city, under three IPPC emission scenarios, RCP 2.6, 4.5 and 8.5. The approach allows a continuous stochastic function to be built to assess yearly evolution of damages from 2030 to 2100. Additionally, we present two risk measures that put low-probability, high-damage events in the spotlight: the Value at Risk (VaR) and the Expected Shortfall (ES), which enable the damages to be estimated when a certain risk level is exceeded. This level of acceptable risk can be defined involving different stakeholders to guide progressive adaptation strategies. The method presented here is new in the field of economics of adaptation and offers a much broader picture of the challenges related to dealing with climate impacts. Furthermore, it can be applied to assess not only adaptation needs but also to put adaptation into a timeframe in each city.
Risk Estimation Methodology for Launch Accidents.
Clayton, Daniel James; Lipinski, Ronald J.; Bechtel, Ryan D.
2014-02-01
As compact and light weight power sources with reliable, long lives, Radioisotope Power Systems (RPSs) have made space missions to explore the solar system possible. Due to the hazardous material that can be released during a launch accident, the potential health risk of an accident must be quantified, so that appropriate launch approval decisions can be made. One part of the risk estimation involves modeling the response of the RPS to potential accident environments. Due to the complexity of modeling the full RPS response deterministically on dynamic variables, the evaluation is performed in a stochastic manner with a Monte Carlo simulation. The potential consequences can be determined by modeling the transport of the hazardous material in the environment and in human biological pathways. The consequence analysis results are summed and weighted by appropriate likelihood values to give a collection of probabilistic results for the estimation of the potential health risk. This information is used to guide RPS designs, spacecraft designs, mission architecture, or launch procedures to potentially reduce the risk, as well as to inform decision makers of the potential health risks resulting from the use of RPSs for space missions.
Estimated Autism Risk and Older Reproductive Age
King, Marissa D.; Fountain, Christine; Dakhlallah, Diana
2009-01-01
Objectives. We sought to estimate the risk for autism associated with maternal and paternal age across successive birth cohorts. Methods. We linked birth records and autism diagnostic records from the California Department of Developmental Services for children born in California between 1992 and 2000 to calculate the risk associated with maternal and paternal age for each birth cohort as well as for the pooled data. Results. The categorical risks associated with maternal age over 40 years ranged from a high of 1.84 (95% confidence interval [CI] = 1.37, 2.47) to a low of 1.27 (95% CI = 0.95, 1.69). The risk associated with paternal age ranged from 1.29 (95% CI = 1.03, 1.6) to 1.71 (95% CI = 1.41, 2.08). Conclusions. Pooling data across multiple birth cohorts inflates the risk associated with paternal age. Analyses that do not suffer from problems produced by pooling across birth cohorts demonstrated that advanced maternal age, rather than paternal age, may pose greater risk. Future research examining parental age as a risk factor must be careful to avoid the paradoxes that can arise from pooling data, particularly during periods of social demographic change. PMID:19608957
NASA Astrophysics Data System (ADS)
Eilers, Anna-Christina; Hennawi, Joseph F.; Lee, Khee-Gan
2017-08-01
We present a new Bayesian algorithm making use of Markov Chain Monte Carlo sampling that allows us to simultaneously estimate the unknown continuum level of each quasar in an ensemble of high-resolution spectra, as well as their common probability distribution function (PDF) for the transmitted Lyα forest flux. This fully automated PDF regulated continuum fitting method models the unknown quasar continuum with a linear principal component analysis (PCA) basis, with the PCA coefficients treated as nuisance parameters. The method allows one to estimate parameters governing the thermal state of the intergalactic medium (IGM), such as the slope of the temperature-density relation γ -1, while marginalizing out continuum uncertainties in a fully Bayesian way. Using realistic mock quasar spectra created from a simplified semi-numerical model of the IGM, we show that this method recovers the underlying quasar continua to a precision of ≃ 7 % and ≃ 10 % at z = 3 and z = 5, respectively. Given the number of principal component spectra, this is comparable to the underlying accuracy of the PCA model itself. Most importantly, we show that we can achieve a nearly unbiased estimate of the slope γ -1 of the IGM temperature-density relation with a precision of +/- 8.6 % at z = 3 and +/- 6.1 % at z = 5, for an ensemble of ten mock high-resolution quasar spectra. Applying this method to real quasar spectra and comparing to a more realistic IGM model from hydrodynamical simulations would enable precise measurements of the thermal and cosmological parameters governing the IGM, albeit with somewhat larger uncertainties, given the increased flexibility of the model.
NASA Technical Reports Server (NTRS)
Huddleston, Lisa L.; Roeder, William P.; Merceret, Francis J.
2011-01-01
A new technique has been developed to estimate the probability that a nearby cloud to ground lightning stroke was within a specified radius of any point of interest. This process uses the bivariate Gaussian distribution of probability density provided by the current lightning location error ellipse for the most likely location of a lightning stroke and integrates it to determine the probability that the stroke is inside any specified radius of any location, even if that location is not centered on or even with the location error ellipse. This technique is adapted from a method of calculating the probability of debris collision with spacecraft. Such a technique is important in spaceport processing activities because it allows engineers to quantify the risk of induced current damage to critical electronics due to nearby lightning strokes. This technique was tested extensively and is now in use by space launch organizations at Kennedy Space Center and Cape Canaveral Air Force Station. Future applications could include forensic meteorology.
NASA Technical Reports Server (NTRS)
Huddleston, Lisa L.; Roeder, William P.; Merceret, Francis J.
2010-01-01
A new technique has been developed to estimate the probability that a nearby cloud-to-ground lightning stroke was within a specified radius of any point of interest. This process uses the bivariate Gaussian distribution of probability density provided by the current lightning location error ellipse for the most likely location of a lightning stroke and integrates it to determine the probability that the stroke is inside any specified radius of any location, even if that location is not centered on or even within the location error ellipse. This technique is adapted from a method of calculating the probability of debris collision with spacecraft. Such a technique is important in spaceport processing activities because it allows engineers to quantify the risk of induced current damage to critical electronics due to nearby lightning strokes. This technique was tested extensively and is now in use by space launch organizations at Kennedy Space Center and Cape Canaveral Air Force station.
Estimating Non-stationary Flood Risk in a Changing Climate
NASA Astrophysics Data System (ADS)
Yu, X.; Cohn, T. A.; Stedinger, J. R.
2015-12-01
Flood risk is usually described by a probability distribution for annual maximum streamflow which is assumed not to change with time. Federal, state and local governments in the United States are demanding guidance on flood frequency estimates that account for climate change. If a trend exists in peak flow series, ignoring it could result in large quantile estimator bias, while trying to estimate a trend will increase the flood quantile estimator's variance. Thus the issue is, what bias-variance tradeoff should we accept? This paper discusses approaches to flood frequency analysis (FFA) when flood series have trends. GCMs describe how annual runoff might vary over sub-continental scales, but this information is nearly useless for FFA in small watersheds. A LP3 Monte Carlo analysis and a re-sampling study of 100-year flood estimation (25- and 50-year projections) compares the performance of five methods: FFA as prescribed in national guidelines (Bulletin 17B), assumes the flood series is stationary and follows a log-Pearson type III (LP3) distribution; Fitting a LP3 distribution with time-varying parameters that include future trends in mean and perhaps variance, where slopes are assumed known; Fitting a LP3 distribution with time-varying parameters that capture future trends in mean and perhaps variance, where slopes are estimated from annual peak flow series; Employing only the most recent 30 years of flood records to fit a LP3 distribution; Applying a safety factor to the 100-year flood estimator (e.g. 25% increase). The 100-year flood estimator of method 2 has the smallest log-space mean squared error, though it is unlikely that the true trend would be known. Method 3 is only recommended over method 1 for large trends (≥ 0.5% per year). The 100-year flood estimators of method 1, 4, and 5 often have poor accuracy. Clearly, flood risk assessment will be a challenge in an uncertain world.
How Many Significant Figures are Useful for Public Risk Estimates?
NASA Astrophysics Data System (ADS)
Wilde, Paul D.; Duffy, Jim
2013-09-01
This paper considers the level of uncertainty in the calculation of public risks from launch or reentry and provides guidance on the number of significant digits that can be used with confidence when reporting the analysis results to decision-makers. The focus of this paper is the uncertainty in collective risk calculations that are used for launches of new and mature ELVs. This paper examines the computational models that are used to estimate total collective risk to the public for a launch, including the model input data and the model results, and characterizes the uncertainties due to both bias and variability. There have been two recent efforts to assess the uncertainty in state-of-the-art risk analysis models used in the US and their input data. One assessment focused on launch area risk from an Atlas V at Vandenberg Air Force Base (VAFB) and the other focused on downrange risk to Eurasia from a Falcon 9 launched from Cape Canaveral Air Force Station (CCAFS). The results of these studies quantified the uncertainties related to both the probability and the consequence of the launch debris hazards. This paper summarizes the results of both of these relatively comprehensive launch risk uncertainty analyses, which addressed both aleatory and epistemic uncertainties. The epistemic uncertainties of most concern were associated with probability of failure and the debris list. Other major sources of uncertainty evaluated were: the casualty area for people in shelters that are impacted by debris, impact distribution size, yield from exploding propellant and propellant tanks, probability of injury from a blast wave for people in shelters or outside, and population density. This paper also summarizes a relatively comprehensive over-flight risk uncertainty analysis performed by the FAA for the second stage of flight for a Falcon 9 from CCAFS. This paper is applicable to baseline collective risk analyses, such as those used to make a commercial license determination, and
Bent, Gardner C.; Archfield, Stacey A.
2002-01-01
A logistic regression equation was developed for estimating the probability of a stream flowing perennially at a specific site in Massachusetts. The equation provides city and town conservation commissions and the Massachusetts Department of Environmental Protection with an additional method for assessing whether streams are perennial or intermittent at a specific site in Massachusetts. This information is needed to assist these environmental agencies, who administer the Commonwealth of Massachusetts Rivers Protection Act of 1996, which establishes a 200-foot-wide protected riverfront area extending along the length of each side of the stream from the mean annual high-water line along each side of perennial streams, with exceptions in some urban areas. The equation was developed by relating the verified perennial or intermittent status of a stream site to selected basin characteristics of naturally flowing streams (no regulation by dams, surface-water withdrawals, ground-water withdrawals, diversion, waste-water discharge, and so forth) in Massachusetts. Stream sites used in the analysis were identified as perennial or intermittent on the basis of review of measured streamflow at sites throughout Massachusetts and on visual observation at sites in the South Coastal Basin, southeastern Massachusetts. Measured or observed zero flow(s) during months of extended drought as defined by the 310 Code of Massachusetts Regulations (CMR) 10.58(2)(a) were not considered when designating the perennial or intermittent status of a stream site. The database used to develop the equation included a total of 305 stream sites (84 intermittent- and 89 perennial-stream sites in the State, and 50 intermittent- and 82 perennial-stream sites in the South Coastal Basin). Stream sites included in the database had drainage areas that ranged from 0.14 to 8.94 square miles in the State and from 0.02 to 7.00 square miles in the South Coastal Basin.Results of the logistic regression analysis
A methodology for estimating risks associated with landslides of contaminated soil into rivers.
Göransson, Gunnel; Norrman, Jenny; Larson, Magnus; Alén, Claes; Rosén, Lars
2014-02-15
Urban areas adjacent to surface water are exposed to soil movements such as erosion and slope failures (landslides). A landslide is a potential mechanism for mobilisation and spreading of pollutants. This mechanism is in general not included in environmental risk assessments for contaminated sites, and the consequences associated with contamination in the soil are typically not considered in landslide risk assessments. This study suggests a methodology to estimate the environmental risks associated with landslides in contaminated sites adjacent to rivers. The methodology is probabilistic and allows for datasets with large uncertainties and the use of expert judgements, providing quantitative estimates of probabilities for defined failures. The approach is illustrated by a case study along the river Göta Älv, Sweden, where failures are defined and probabilities for those failures are estimated. Failures are defined from a pollution perspective and in terms of exceeding environmental quality standards (EQSs) and acceptable contaminant loads. Models are then suggested to estimate probabilities of these failures. A landslide analysis is carried out to assess landslide probabilities based on data from a recent landslide risk classification study along the river Göta Älv. The suggested methodology is meant to be a supplement to either landslide risk assessment (LRA) or environmental risk assessment (ERA), providing quantitative estimates of the risks associated with landslide in contaminated sites. The proposed methodology can also act as a basis for communication and discussion, thereby contributing to intersectoral management solutions. From the case study it was found that the defined failures are governed primarily by the probability of a landslide occurring. The overall probabilities for failure are low; however, if a landslide occurs the probabilities of exceeding EQS are high and the probability of having at least a 10% increase in the contamination load
A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.
Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen
2014-01-01
Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.
Arterberry, Martha E; Bornstein, Marc H; Haynes, O Maurice
2011-04-01
Two analytical procedures for identifying young children as categorizers, the Monte Carlo Simulation and the Probability Estimate Model, were compared. Using a sequential touching method, children aged 12, 18, 24, and 30 months were given seven object sets representing different levels of categorical classification. From their touching performance, the probability that children were categorizing was then determined independently using Monte Carlo Simulation and the Probability Estimate Model. The two analytical procedures resulted in different percentages of children being classified as categorizers. Results using the Monte Carlo Simulation were more consistent with group-level analyses than results using the Probability Estimate Model. These findings recommend using the Monte Carlo Simulation for determining individual categorizer classification. Copyright © 2011 Elsevier Inc. All rights reserved.
Hart, Stephen D; Cooke, David J
2013-01-01
We investigated the precision of individual risk estimates made using actuarial risk assessment instruments (ARAIs) by discussing some major conceptual issues and then illustrating them by analyzing new data. We used a standard multivariate statistical procedure, logistic regression, to create a new ARAI based on data from a follow-up study of 90 adult male sex offenders. We indexed predictive precision at the group level using confidence intervals for group mean probability estimates, and at the individual level using prediction intervals for individual probability estimates. Consistent with past research, ARAI scores were moderately and significantly predictive of failure in the aggregate, but group probability estimates had substantial margins of error and individual probability estimates had very large margins of error. We conclude that, without major advances in our understanding of the causes of violence, ARAIs cannot be used to estimate the specific probability or absolute likelihood of future violence with any reasonable degree of precision or certainty. The implications for conducting violence risk assessments in forensic mental health are discussed.
Change of flood risk under climate change based on Discharge Probability Index in Japan
NASA Astrophysics Data System (ADS)
Nitta, T.; Yoshimura, K.; Kanae, S.; Oki, T.
2010-12-01
Water-related disasters under the climate change have recently gained considerable interest, and there have been many studies referring to flood risk at the global scale (e.g. Milly et al., 2002; Hirabayashi et al., 2008). In order to build adaptive capacity, however, regional impact evaluation is needed. We thus focus on the flood risk over Japan in the present study. The output from the Regional Climate Model 20 (RCM20), which was developed by the Meteorological Research Institute, was used. The data was first compared with observed data based on Automated Meteorological Data Acquisition System and ground weather observations, and the model biases were corrected using the ratio and difference of the 20-year mean values. The bias-corrected RCM20 atmospheric data were then forced to run a land surface model and a river routing model (Yoshimura et al., 2007; Ngo-Duc, T. et al. 2007) to simulate river discharge during 1981-2000, 2031-2050, and 2081-2100. Simulated river discharge was converted to Discharge Probability Index (DPI), which was proposed by Yoshimura et al based on a statistical approach. The bias and uncertainty of the models are already taken into account in the concept of DPI, so that DPI serves as a good indicator of flood risk. We estimated the statistical parameters for DPI using the river discharge for 1981-2000 with an assumption that the parameters stay the same in the different climate periods. We then evaluated the occurrence of flood events corresponding to DPI categories in each 20 years and averaged them in 9 regions. The results indicate that low DPI flood events (return period of 2 years) will become more frequent in 2031-2050 and high DPI flood events (return period of 200 years) will become more frequent in 2081-2100 compared with the period of 1981-2000, though average precipitation will become larger during 2031-2050 than during 2081-2100 in most regions. It reflects the increased extreme precipitation during 2081-2100.
NASA Astrophysics Data System (ADS)
Samejima, Masaki; Negoro, Keisuke; Mitsukuni, Koshichiro; Akiyoshi, Masanori
We propose a finding method of business risk factors on qualitative and quantitative hybrid simulation in time series. Effect ratios of qualitative arcs in the hybrid simulation vary output values of the simulation, so we define effect ratios causing risk as business risk factors. Finding business risk factors in entire ranges of effect ratios is time-consuming. It is considered that probability distributions of effect ratios in present time step and ones in previous time step are similar, the probability distributions in present time step can be estimated. Our method finds business risk factors in only estimated ranges effectively. Experimental results show that a precision rate and a recall rate are 86%, and search time is decreased 20% at least.
Signor, R S; Ashbolt, N J
2009-12-01
Some national drinking water guidelines provide guidance on how to define 'safe' drinking water. Regarding microbial water quality, a common position is that the chance of an individual becoming infected by some reference waterborne pathogen (e.g. Cryptsporidium) present in the drinking water should < 10(-4) in any year. However the instantaneous levels of risk to a water consumer vary over the course of a year, and waterborne disease outbreaks have been associated with shorter-duration periods of heightened risk. Performing probabilistic microbial risk assessments is becoming commonplace to capture the impacts of temporal variability on overall infection risk levels. A case is presented here for adoption of a shorter-duration reference period (i.e. daily) infection probability target over which to assess, report and benchmark such risks. A daily infection probability benchmark may provide added incentive and guidance for exercising control over short-term adverse risk fluctuation events and their causes. Management planning could involve outlining measures so that the daily target is met under a variety of pre-identified event scenarios. Other benefits of a daily target could include providing a platform for managers to design and assess management initiatives, as well as simplifying the technical components of the risk assessment process.
Over, Thomas; Saito, Riki J.; Veilleux, Andrea; Sharpe, Jennifer B.; Soong, David T.; Ishii, Audrey
2016-06-28
This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions.The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, generalized skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter.This report also provides the following: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at
Neural response to reward anticipation under risk is nonlinear in probabilities.
Hsu, Ming; Krajbich, Ian; Zhao, Chen; Camerer, Colin F
2009-02-18
A widely observed phenomenon in decision making under risk is the apparent overweighting of unlikely events and the underweighting of nearly certain events. This violates standard assumptions in expected utility theory, which requires that expected utility be linear (objective) in probabilities. Models such as prospect theory have relaxed this assumption and introduced the notion of a "probability weighting function," which captures the key properties found in experimental data. This study reports functional magnetic resonance imaging (fMRI) data that neural response to expected reward is nonlinear in probabilities. Specifically, we found that activity in the striatum during valuation of monetary gambles are nonlinear in probabilities in the pattern predicted by prospect theory, suggesting that probability distortion is reflected at the level of the reward encoding process. The degree of nonlinearity reflected in individual subjects' decisions is also correlated with striatal activity across subjects. Our results shed light on the neural mechanisms of reward processing, and have implications for future neuroscientific studies of decision making involving extreme tails of the distribution, where probability weighting provides an explanation for commonly observed behavioral anomalies.
Jönsson, Siv; Karlsson, Mats O
2005-05-01
Methods for optimizing dosing strategies for individualization with a limited number of discrete doses, in terms of maximizing the expected utility of treatment or responder probability, are presented. The optimality criteria require models for both beneficial and adverse effects that are part of the utility definition and published population models describing those effects for oxybutynin (urge urinary incontinence episodes per week and severity of dry mouth, respectively) were used for illustration. Dosing strategies with two dosing categories were defined in terms of sizes of the daily doses (low and high dose) and the proportion of patients that can be expected to be preferentially treated at the low dose level. Utility and responder definitions were varied to investigate the influence on the resulting dosing strategy. By minimizing a risk function, describing the seriousness of deviations from the predefined target, optimal dosing strategies were estimated using mixture models in NONMEM. The estimated dose ranges for oxybutynin were similar to those recommended. The optimal individualization conditions were dependent on the definitions of responder and utility. The predicted gain of individualization given utility and responder definitions used was greater, when a responder criteria was maximized compared with maximizing utility.
Haber, M.; An, Q.; Foppa, I. M.; Shay, D. K.; Ferdinands, J. M.; Orenstein, W. A.
2014-01-01
Summary As influenza vaccination is now widely recommended, randomized clinical trials are no longer ethical in many populations. Therefore, observational studies on patients seeking medical care for acute respiratory illnesses (ARI) are a popular option for estimating influenza vaccine effectiveness (VE). We developed a probability model for evaluating and comparing bias and precision of estimates of VE against symptomatic influenza from two commonly-used case-control study designs: the test-negative design and the traditional case-control design. We show that when vaccination does not affect the probability of developing non-influenza ARI then VE estimates from test-negative design studies are unbiased even if vaccinees and non-vaccinees have different probabilities of seeking medical care against ARI, as long as the ratio of these probabilities is the same for illnesses resulting from influenza and non-influenza infections. Our numerical results suggest that in general, estimates from the test-negative design have smaller bias compared to estimates from the traditional case-control design as long as the probability of non-influenza ARI is similar among vaccinated and unvaccinated individuals. We did not find consistent differences between the standard errors of the estimates from the two study designs. PMID:25147970
Ghosh, Karthik; Crawford, Brianna J; Pruthi, Sandhya; Williams, Constance I; Neal, Lonzetta; Sandhu, Nicole P; Johnson, Ruth E; Wahner-Roedler, Dietlind; Britain, Marcia K; Cha, Stephen S; Ghosh, Amit K
2008-10-20
Breast cancer risk education enables women make informed decisions regarding their options for screening and risk reduction. We aimed to determine whether patient education regarding breast cancer risk using a bar graph, with or without a frequency format diagram, improved the accuracy of risk perception. We conducted a prospective, randomized trial among women at increased risk for breast cancer. The main outcome measurement was patients' estimation of their breast cancer risk before and after education with a bar graph (BG group) or bar graph plus a frequency format diagram (BG+FF group), which was assessed by previsit and postvisit questionnaires. Of 150 women in the study, 74 were assigned to the BG group and 76 to the BG+FF group. Overall, 72% of women overestimated their risk of breast cancer. The improvement in accuracy of risk perception from the previsit to the postvisit questionnaire (BG group, 19% to 61%; BG+FF group, 13% to 67%) was not significantly different between the 2 groups (P = .10). Among women who inaccurately perceived very high risk (> or = 50% risk), inaccurate risk perception decreased significantly in the BG+FF group (22% to 3%) compared with the BG group (28% to 19%) (P = .004). Breast cancer risk communication using a bar graph plus a frequency format diagram can improve the short-term accuracy of risk perception among women perceiving inaccurately high risk.
PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT.
Muschelli, John; Sweeney, Elizabeth M; Ullman, Natalie L; Vespa, Paul; Hanley, Daniel F; Crainiceanu, Ciprian M
2017-01-01
Intracerebral hemorrhage (ICH), where a blood vessel ruptures into areas of the brain, accounts for approximately 10-15% of all strokes. X-ray computed tomography (CT) scanning is largely used to assess the location and volume of these hemorrhages. Manual segmentation of the CT scan using planimetry by an expert reader is the gold standard for volume estimation, but is time-consuming and has within- and across-reader variability. We propose a fully automated segmentation approach using a random forest algorithm with features extracted from X-ray computed tomography (CT) scans. The Minimally Invasive Surgery plus rt-PA in ICH Evacuation (MISTIE) trial was a multi-site Phase II clinical trial that tested the safety of hemorrhage removal using recombinant-tissue plasminogen activator (rt-PA). For this analysis, we use 112 baseline CT scans from patients enrolled in the MISTE trial, one CT scan per patient. ICH was manually segmented on these CT scans by expert readers. We derived a set of imaging predictors from each scan. Using 10 randomly-selected scans, we used a first-pass voxel selection procedure based on quantiles of a set of predictors and then built 4 models estimating the voxel-level probability of ICH. The models used were: 1) logistic regression, 2) logistic regression with a penalty on the model parameters using LASSO, 3) a generalized additive model (GAM) and 4) a random forest classifier. The remaining 102 scans were used for model validation.For each validation scan, the model predicted the probability of ICH at each voxel. These voxel-level probabilities were then thresholded to produce binary segmentations of the hemorrhage. These masks were compared to the manual segmentations using the Dice Similarity Index (DSI) and the correlation of hemorrhage volume of between the two segmentations. We tested equality of median DSI using the Kruskal-Wallis test across the 4 models. We tested equality of the median DSI from sets of 2 models using a Wilcoxon
Estimation of the Probable Maximum Flood for a Small Lowland River in Poland
NASA Astrophysics Data System (ADS)
Banasik, K.; Hejduk, L.
2009-04-01
The planning, designe and use of hydrotechnical structures often requires the assesment of maximu flood potentials. The most common term applied to this upper limit of flooding is the probable maximum flood (PMF). The PMP/UH (probable maximum precipitation/unit hydrograph) method has been used in the study to predict PMF from a small agricultural lowland river basin of Zagozdzonka (left tributary of Vistula river) in Poland. The river basin, located about 100 km south of Warsaw, with an area - upstream the gauge of Plachty - of 82 km2, has been investigated by Department of Water Engineering and Environmenal Restoration of Warsaw University of Life Sciences - SGGW since 1962. Over 40-year flow record was used in previous investigation for predicting T-year flood discharge (Banasik et al., 2003). The objective here was to estimate the PMF using the PMP/UH method and to compare the results with the 100-year flood. A new relation of depth-duration curve of PMP for the local climatic condition has been developed based on Polish maximum observed rainfall data (Ozga-Zielinska & Ozga-Zielinski, 2003). Exponential formula, with the value of exponent of 0.47, i.e. close to the exponent in formula for world PMP and also in the formula of PMP for Great Britain (Wilson, 1993), gives the rainfall depth about 40% lower than the Wilson's one. The effective rainfall (runoff volume) has been estimated from the PMP of various duration using the CN-method (USDA-SCS, 1986). The CN value as well as parameters of the IUH model (Nash, 1957) have been established from the 27 rainfall-runoff events, recorded in the river basin in the period 1980-2004. Varibility of the parameter values with the size of the events will be discussed in the paper. The results of the analyse have shown that the peak discharge of the PMF is 4.5 times larger then 100-year flood, and volume ratio of the respective direct hydrographs caused by rainfall events of critical duration is 4.0. References 1.Banasik K
Estimation of the Probable Maximum Flood for a Small Lowland River in Poland
NASA Astrophysics Data System (ADS)
Banasik, K.; Hejduk, L.
2009-04-01
The planning, designe and use of hydrotechnical structures often requires the assesment of maximu flood potentials. The most common term applied to this upper limit of flooding is the probable maximum flood (PMF). The PMP/UH (probable maximum precipitation/unit hydrograph) method has been used in the study to predict PMF from a small agricultural lowland river basin of Zagozdzonka (left tributary of Vistula river) in Poland. The river basin, located about 100 km south of Warsaw, with an area - upstream the gauge of Plachty - of 82 km2, has been investigated by Department of Water Engineering and Environmenal Restoration of Warsaw University of Life Sciences - SGGW since 1962. Over 40-year flow record was used in previous investigation for predicting T-year flood discharge (Banasik et al., 2003). The objective here was to estimate the PMF using the PMP/UH method and to compare the results with the 100-year flood. A new relation of depth-duration curve of PMP for the local climatic condition has been developed based on Polish maximum observed rainfall data (Ozga-Zielinska & Ozga-Zielinski, 2003). Exponential formula, with the value of exponent of 0.47, i.e. close to the exponent in formula for world PMP and also in the formula of PMP for Great Britain (Wilson, 1993), gives the rainfall depth about 40% lower than the Wilson's one. The effective rainfall (runoff volume) has been estimated from the PMP of various duration using the CN-method (USDA-SCS, 1986). The CN value as well as parameters of the IUH model (Nash, 1957) have been established from the 27 rainfall-runoff events, recorded in the river basin in the period 1980-2004. Varibility of the parameter values with the size of the events will be discussed in the paper. The results of the analyse have shown that the peak discharge of the PMF is 4.5 times larger then 100-year flood, and volume ratio of the respective direct hydrographs caused by rainfall events of critical duration is 4.0. References 1.Banasik K
NASA Astrophysics Data System (ADS)
Lussana, C.
2013-04-01
The presented work focuses on the investigation of gridded daily minimum (TN) and maximum (TX) temperature probability density functions (PDFs) with the intent of both characterising a region and detecting extreme values. The empirical PDFs estimation procedure has been realised using the most recent years of gridded temperature analysis fields available at ARPA Lombardia, in Northern Italy. The spatial interpolation is based on an implementation of Optimal Interpolation using observations from a dense surface network of automated weather stations. An effort has been made to identify both the time period and the spatial areas with a stable data density otherwise the elaboration could be influenced by the unsettled station distribution. The PDF used in this study is based on the Gaussian distribution, nevertheless it is designed to have an asymmetrical (skewed) shape in order to enable distinction between warming and cooling events. Once properly defined the occurrence of extreme events, it is possible to straightforwardly deliver to the users the information on a local-scale in a concise way, such as: TX extremely cold/hot or TN extremely cold/hot.
Moment-Based Probability Modeling and Extreme Response Estimation, The FITS Routine Version 1.2
MANUEL,LANCE; KASHEF,TINA; WINTERSTEIN,STEVEN R.
1999-11-01
This report documents the use of the FITS routine, which provides automated fits of various analytical, commonly used probability models from input data. It is intended to complement the previously distributed FITTING routine documented in RMS Report 14 (Winterstein et al., 1994), which implements relatively complex four-moment distribution models whose parameters are fit with numerical optimization routines. Although these four-moment fits can be quite useful and faithful to the observed data, their complexity can make them difficult to automate within standard fitting algorithms. In contrast, FITS provides more robust (lower moment) fits of simpler, more conventional distribution forms. For each database of interest, the routine estimates the distribution of annual maximum response based on the data values and the duration, T, over which they were recorded. To focus on the upper tails of interest, the user can also supply an arbitrary lower-bound threshold, {chi}{sub low}, above which a shifted distribution model--exponential or Weibull--is fit.
Das, Jayajit; Mukherjee, Sayak; Hodge, Susan E
2015-07-01
A common statistical situation concerns inferring an unknown distribution Q(x) from a known distribution P(y), where X (dimension n), and Y (dimension m) have a known functional relationship. Most commonly, n ≤ m, and the task is relatively straightforward for well-defined functional relationships. For example, if Y1 and Y2 are independent random variables, each uniform on [0, 1], one can determine the distribution of X = Y1 + Y2; here m = 2 and n = 1. However, biological and physical situations can arise where n > m and the functional relation Y→X is non-unique. In general, in the absence of additional information, there is no unique solution to Q in those cases. Nevertheless, one may still want to draw some inferences about Q. To this end, we propose a novel maximum entropy (MaxEnt) approach that estimates Q(x) based only on the available data, namely, P(y). The method has the additional advantage that one does not need to explicitly calculate the Lagrange multipliers. In this paper we develop the approach, for both discrete and continuous probability distributions, and demonstrate its validity. We give an intuitive justification as well, and we illustrate with examples.
A New Approach to Estimating the Probability for β-delayed Neutron Emission
McCutchan, E.A.; Sonzogni, A.A.; Johnson, T.D.; Abriola, D.; Birch, M.; Singh, B.
2014-06-15
The probability for neutron emission following β decay, Pn, is a crucial property for a wide range of physics and applications including nuclear structure, r-process nucleosynthesis, the control of nuclear reactors, and the post-processing of nuclear fuel. Despite much experimental effort, knowledge of Pn values is still lacking in very neutron-rich nuclei, requiring predictions from either systematics or theoretical models. Traditionally, systematic predictions were made by investigating the Pn value as a function of the decay Q value and the neutron separation energy in the daughter nucleus. A new approach to Pn systematics is presented which incorporates the half-life of the decay and the Q value for β-delayed neutron emission. This prescription correlates the known data better, and thus improves the estimation of Pn values for neutron-rich nuclei. Such an approach can be applied to generate input values for r-process network calculations or in the modeling of advanced fuel cycles.
Dictionary-based probability density function estimation for high-resolution SAR data
NASA Astrophysics Data System (ADS)
Krylov, Vladimir; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane
2009-02-01
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes.
Nilsson, Håkan; Juslin, Peter; Winman, Anders
2016-01-01
Costello and Watts (2014) present a model assuming that people's knowledge of probabilities adheres to probability theory, but that their probability judgments are perturbed by a random noise in the retrieval from memory. Predictions for the relationships between probability judgments for constituent events and their disjunctions and conjunctions, as well as for sums of such judgments were derived from probability theory. Costello and Watts (2014) report behavioral data showing that subjective probability judgments accord with these predictions. Based on the finding that subjective probability judgments follow probability theory, Costello and Watts (2014) conclude that the results imply that people's probability judgments embody the rules of probability theory and thereby refute theories of heuristic processing. Here, we demonstrate the invalidity of this conclusion by showing that all of the tested predictions follow straightforwardly from an account assuming heuristic probability integration (Nilsson, Winman, Juslin, & Hansson, 2009). We end with a discussion of a number of previous findings that harmonize very poorly with the predictions by the model suggested by Costello and Watts (2014).
The report evaluates approaches for estimating the probability of ingestion by birds of contaminated particles such as pesticide granules or lead particles (i.e. shot or bullet fragments). In addition, it presents an approach for using this information to estimate the risk of mo...
Multivariate injury risk criteria and injury probability scores for fractures to the distal radius.
Burkhart, Timothy A; Andrews, David M; Dunning, Cynthia E
2013-03-15
The purpose of this study was to develop a multivariate distal radius injury risk prediction model that incorporates dynamic loading variables in multiple directions, and interpret the distal radius failure data in order to establish injury probability thresholds. Repeated impacts with increasing intensity were applied to the distal third of eight human cadaveric radius specimens (mean (SD) age=61.9 (9.7)) until injury occurred. Crack (non-propagating damage) and fracture (specimen separated into at least two fragments) injury events were recorded. Best subsets analysis was performed to find the best multivariate injury risk model. Force-only risk models were also determined for comparison. Cumulative distribution functions were developed from the parameters of a Weibull analysis and the forces and risk scores (i.e., values calculated from the injury risk models) from 10% to 90% probability were calculated. According to the adjusted R(2), variance inflation factor and p-values, the model that best predicted the crack event included medial/lateral impulse, Fz load rate, impact velocity and the natural logarithm of Fz (Adj. R(2)=0.698), while the best predictive model of the fracture event included medial/lateral impulse, impact velocity and peak Fz (Adj. R(2)=0.845). The multivariate models predicted injury risk better than both the Fz-only crack (Adj. R(2)=0.551) and fracture (Adj. R(2)=0.293) models. Risk scores of 0.5 and 0.6 corresponded to 10% failure probability for the crack and fracture events, respectively. The inclusion of medial/lateral impulse and impact velocity in both crack and fracture models, and Fz load rate in the crack model, underscores the dynamic nature of these events. This study presents a method capable of developing a set of distal radius fracture prediction models that can be used in the assessment and development of distal radius injury prevention interventions.
Auditory risk estimates for youth target shooting
Meinke, Deanna K.; Murphy, William J.; Finan, Donald S.; Lankford, James E.; Flamme, Gregory A.; Stewart, Michael; Soendergaard, Jacob; Jerome, Trevor W.
2015-01-01
Objective To characterize the impulse noise exposure and auditory risk for youth recreational firearm users engaged in outdoor target shooting events. The youth shooting positions are typically standing or sitting at a table, which places the firearm closer to the ground or reflective surface when compared to adult shooters. Design Acoustic characteristics were examined and the auditory risk estimates were evaluated using contemporary damage-risk criteria for unprotected adult listeners and the 120-dB peak limit suggested by the World Health Organization (1999) for children. Study sample Impulses were generated by 26 firearm/ammunition configurations representing rifles, shotguns, and pistols used by youth. Measurements were obtained relative to a youth shooter’s left ear. Results All firearms generated peak levels that exceeded the 120 dB peak limit suggested by the WHO for children. In general, shooting from the seated position over a tabletop increases the peak levels, LAeq8 and reduces the unprotected maximum permissible exposures (MPEs) for both rifles and pistols. Pistols pose the greatest auditory risk when fired over a tabletop. Conclusion Youth should utilize smaller caliber weapons, preferably from the standing position, and always wear hearing protection whenever engaging in shooting activities to reduce the risk for auditory damage. PMID:24564688
NASA Astrophysics Data System (ADS)
Suligowski, Roman
2014-05-01
Probable Maximum Precipitation based upon the physical mechanisms of precipitation formation at the Kielce Upland. This estimation stems from meteorological analysis of extremely high precipitation events, which occurred in the area between 1961 and 2007 causing serious flooding from rivers that drain the entire Kielce Upland. Meteorological situation has been assessed drawing on the synoptic maps, baric topography charts, satellite and radar images as well as the results of meteorological observations derived from surface weather observation stations. Most significant elements of this research include the comparison between distinctive synoptic situations over Europe and subsequent determination of typical rainfall generating mechanism. This allows the author to identify the source areas of air masses responsible for extremely high precipitation at the Kielce Upland. Analysis of the meteorological situations showed, that the source areas for humid air masses which cause the largest rainfalls at the Kielce Upland are the area of northern Adriatic Sea and the north-eastern coast of the Black Sea. Flood hazard at the Kielce Upland catchments was triggered by daily precipitation of over 60 mm. The highest representative dew point temperature in source areas of warm air masses (these responsible for high precipitation at the Kielce Upland) exceeded 20 degrees Celsius with a maximum of 24.9 degrees Celsius while precipitable water amounted to 80 mm. The value of precipitable water is also used for computation of factors featuring the system, namely the mass transformation factor and the system effectiveness factor. The mass transformation factor is computed based on precipitable water in the feeding mass and precipitable water in the source area. The system effectiveness factor (as the indicator of the maximum inflow velocity and the maximum velocity in the zone of front or ascending currents, forced by orography) is computed from the quotient of precipitable water in
Relating space radiation environments to risk estimates
NASA Technical Reports Server (NTRS)
Curtis, Stanley B.
1993-01-01
A number of considerations must go into the process of determining the risk of deleterious effects of space radiation to travelers. Among them are (1) determination of the components of the radiation environment (particle species, fluxes and energy spectra) which will encounter, (2) determination of the effects of shielding provided by the spacecraft and the bodies of the travelers which modify the incident particle spectra and mix of particles, and (3) determination of relevant biological effects of the radiation in the organs of interest. The latter can then lead to an estimation of risk from a given space scenario. Clearly, the process spans many scientific disciplines from solar and cosmic ray physics to radiation transport theeory to the multistage problem of the induction by radiation of initial lesions in living material and their evolution via physical, chemical, and biological processes at the molecular, cellular, and tissue levels to produce the end point of importance.
Assessing uncertainty in published risk estimates using ...
Introduction: The National Research Council recommended quantitative evaluation of uncertainty in effect estimates for risk assessment. This analysis considers uncertainty across model forms and model parameterizations with hexavalent chromium [Cr(VI)] and lung cancer mortality as an example. The objective is to characterize model uncertainty by evaluating estimates across published epidemiologic studies of the same cohort.Methods: This analysis was based on 5 studies analyzing a cohort of 2,357 workers employed from 1950-74 in a chromate production plant in Maryland. Cox and Poisson models were the only model forms considered by study authors to assess the effect of Cr(VI) on lung cancer mortality. All models adjusted for smoking and included a 5-year exposure lag, however other latency periods and model covariates such as age and race were considered. Published effect estimates were standardized to the same units and normalized by their variances to produce a standardized metric to compare variability within and between model forms. A total of 5 similarly parameterized analyses were considered across model form, and 16 analyses with alternative parameterizations were considered within model form (10 Cox; 6 Poisson). Results: Across Cox and Poisson model forms, adjusted cumulative exposure coefficients (betas) for 5 similar analyses ranged from 2.47 to 4.33 (mean=2.97, σ2=0.63). Within the 10 Cox models, coefficients ranged from 2.53 to 4.42 (mean=3.29, σ2=0.
NASA Astrophysics Data System (ADS)
Zhong, H.; van Overloop, P.-J.; van Gelder, P. H. A. J. M.
2013-07-01
The Lower Rhine Delta, a transitional area between the River Rhine and Meuse and the North Sea, is at risk of flooding induced by infrequent events of a storm surge or upstream flooding, or by more infrequent events of a combination of both. A joint probability analysis of the astronomical tide, the wind induced storm surge, the Rhine flow and the Meuse flow at the boundaries is established in order to produce the joint probability distribution of potential flood events. Three individual joint probability distributions are established corresponding to three potential flooding causes: storm surges and normal Rhine discharges, normal sea levels and high Rhine discharges, and storm surges and high Rhine discharges. For each category, its corresponding joint probability distribution is applied, in order to stochastically simulate a large number of scenarios. These scenarios can be used as inputs to a deterministic 1-D hydrodynamic model in order to estimate the high water level frequency curves at the transitional locations. The results present the exceedance probability of the present design water level for the economically important cities of Rotterdam and Dordrecht. The calculated exceedance probability is evaluated and compared to the governmental norm. Moreover, the impact of climate change on the high water level frequency curves is quantified for the year 2050 in order to assist in decisions regarding the adaptation of the operational water management system and the flood defense system.
Improving Estimation of Ground Casualty Risk From Reentering Space Objects
NASA Technical Reports Server (NTRS)
Ostrom, Chris L.
2017-01-01
A recent improvement to the long-term estimation of ground casualties from reentering space debris is the further refinement and update to the human population distribution. Previous human population distributions were based on global totals with simple scaling factors for future years, or a coarse grid of population counts in a subset of the world's countries, each cell having its own projected growth rate. The newest population model includes a 5-fold refinement in both latitude and longitude resolution. All areas along a single latitude are combined to form a global population distribution as a function of latitude, creating a more accurate population estimation based on non-uniform growth at the country and area levels. Previous risk probability calculations used simplifying assumptions that did not account for the ellipsoidal nature of the Earth. The new method uses first, a simple analytical method to estimate the amount of time spent above each latitude band for a debris object with a given orbit inclination and second, a more complex numerical method that incorporates the effects of a non-spherical Earth. These new results are compared with the prior models to assess the magnitude of the effects on reentry casualty risk.
Improving Estimation of Ground Casualty Risk from Reentering Space Objects
NASA Technical Reports Server (NTRS)
Ostrom, C.
2017-01-01
A recent improvement to the long-term estimation of ground casualties from reentering space debris is the further refinement and update to the human population distribution. Previous human population distributions were based on global totals with simple scaling factors for future years, or a coarse grid of population counts in a subset of the world's countries, each cell having its own projected growth rate. The newest population model includes a 5-fold refinement in both latitude and longitude resolution. All areas along a single latitude are combined to form a global population distribution as a function of latitude, creating a more accurate population estimation based on non-uniform growth at the country and area levels. Previous risk probability calculations used simplifying assumptions that did not account for the ellipsoidal nature of the earth. The new method uses first, a simple analytical method to estimate the amount of time spent above each latitude band for a debris object with a given orbit inclination, and second, a more complex numerical method that incorporates the effects of a non-spherical Earth. These new results are compared with the prior models to assess the magnitude of the effects on reentry casualty risk.
NASA Technical Reports Server (NTRS)
Wiegmann, Douglas A.a
2005-01-01
The NASA Aviation Safety Program (AvSP) has defined several products that will potentially modify airline and/or ATC operations, enhance aircraft systems, and improve the identification of potential hazardous situations within the National Airspace System (NAS). Consequently, there is a need to develop methods for evaluating the potential safety benefit of each of these intervention products so that resources can be effectively invested to produce the judgments to develop Bayesian Belief Networks (BBN's) that model the potential impact that specific interventions may have. Specifically, the present report summarizes methodologies for improving the elicitation of probability estimates during expert evaluations of AvSP products for use in BBN's. The work involved joint efforts between Professor James Luxhoj from Rutgers University and researchers at the University of Illinois. The Rutgers' project to develop BBN's received funding by NASA entitled "Probabilistic Decision Support for Evaluating Technology Insertion and Assessing Aviation Safety System Risk." The proposed project was funded separately but supported the existing Rutgers' program.
Remote sensing techniques for vegetation moisture and fire risk estimation
NASA Astrophysics Data System (ADS)
Dasgupta, Swarvanu
Moisture Experiment 2003 field campaign. Finally as an alternative to current subjective fire risk indices a new Fire Susceptibility Index (FSI) based on physical concept of pre-ignition energy was proposed. FSI uses remotely sensed estimations of fuel temperature and LFMC. Its physical basis is expected to allow computations of ignition probabilities and fire spread rates. FSI can be used compare fire risk across ecoregions and yet has the flexibility to be localized for an ecoregion for improved performance. A good agreement with the well tested FPI (Fire Potential Index) over Georgia, suggests the validity of FSI as a fire risk estimator. These new approaches would be helpful in fire risk monitoring, agriculture and climate studies.
Large, Matthew
2013-12-01
Probability theory is at the base of modern concepts of risk assessment in mental health. The aim of the current paper is to review the key developments in the early history of probability theory in order to enrich our understanding of current risk assessment practices.
Keiter, David A; Davis, Amy J; Rhodes, Olin E; Cunningham, Fred L; Kilgo, John C; Pepin, Kim M; Beasley, James C
2017-08-25
Knowledge of population density is necessary for effective management and conservation of wildlife, yet rarely are estimators compared in their robustness to effects of ecological and observational processes, which can greatly influence accuracy and precision of density estimates. In this study, we simulate biological and observational processes using empirical data to assess effects of animal scale of movement, true population density, and probability of detection on common density estimators. We also apply common data collection and analytical techniques in the field and evaluate their ability to estimate density of a globally widespread species. We find that animal scale of movement had the greatest impact on accuracy of estimators, although all estimators suffered reduced performance when detection probability was low, and we provide recommendations as to when each field and analytical technique is most appropriately employed. The large influence of scale of movement on estimator accuracy emphasizes the importance of effective post-hoc calculation of area sampled or use of methods that implicitly account for spatial variation. In particular, scale of movement impacted estimators substantially, such that area covered and spacing of detectors (e.g. cameras, traps, etc.) must reflect movement characteristics of the focal species to reduce bias in estimates of movement and thus density.
Neugebauer, Romain; Schmittdiel, Julie A; van der Laan, Mark J
2016-05-01
Consistent estimation of causal effects with inverse probability weighting estimators is known to rely on consistent estimation of propensity scores. To alleviate the bias expected from incorrect model specification for these nuisance parameters in observational studies, data-adaptive estimation and in particular an ensemble learning approach known as Super Learning has been proposed as an alternative to the common practice of estimation based on arbitrary model specification. While the theoretical arguments against the use of the latter haphazard estimation strategy are evident, the extent to which data-adaptive estimation can improve inferences in practice is not. Some practitioners may view bias concerns over arbitrary parametric assumptions as academic considerations that are inconsequential in practice. They may also be wary of data-adaptive estimation of the propensity scores for fear of greatly increasing estimation variability due to extreme weight values. With this report, we aim to contribute to the understanding of the potential practical consequences of the choice of estimation strategy for the propensity scores in real-world comparative effectiveness research. We implement secondary analyses of Electronic Health Record data from a large cohort of type 2 diabetes patients to evaluate the effects of four adaptive treatment intensification strategies for glucose control (dynamic treatment regimens) on subsequent development or progression of urinary albumin excretion. Three Inverse Probability Weighting estimators are implemented using both model-based and data-adaptive estimation strategies for the propensity scores. Their practical performances for proper confounding and selection bias adjustment are compared and evaluated against results from previous randomized experiments. Results suggest both potential reduction in bias and increase in efficiency at the cost of an increase in computing time when using Super Learning to implement Inverse Probability
Seismic Risk Assessment and Loss Estimation for Tbilisi City
NASA Astrophysics Data System (ADS)
Tsereteli, Nino; Alania, Victor; Varazanashvili, Otar; Gugeshashvili, Tengiz; Arabidze, Vakhtang; Arevadze, Nika; Tsereteli, Emili; Gaphrindashvili, Giorgi; Gventcadze, Alexander; Goguadze, Nino; Vephkhvadze, Sophio
2013-04-01
The proper assessment of seismic risk is of crucial importance for society protection and city sustainable economic development, as it is the essential part to seismic hazard reduction. Estimation of seismic risk and losses is complicated tasks. There is always knowledge deficiency on real seismic hazard, local site effects, inventory on elements at risk, infrastructure vulnerability, especially for developing countries. Lately great efforts was done in the frame of EMME (earthquake Model for Middle East Region) project, where in the work packages WP1, WP2 , WP3 and WP4 where improved gaps related to seismic hazard assessment and vulnerability analysis. Finely in the frame of work package wp5 "City Scenario" additional work to this direction and detail investigation of local site conditions, active fault (3D) beneath Tbilisi were done. For estimation economic losses the algorithm was prepared taking into account obtained inventory. The long term usage of building is very complex. It relates to the reliability and durability of buildings. The long term usage and durability of a building is determined by the concept of depreciation. Depreciation of an entire building is calculated by summing the products of individual construction unit' depreciation rates and the corresponding value of these units within the building. This method of calculation is based on an assumption that depreciation is proportional to the building's (constructions) useful life. We used this methodology to create a matrix, which provides a way to evaluate the depreciation rates of buildings with different type and construction period and to determine their corresponding value. Finally loss was estimated resulting from shaking 10%, 5% and 2% exceedance probability in 50 years. Loss resulting from scenario earthquake (earthquake with possible maximum magnitude) also where estimated.
Cook, D A
2006-04-01
Models that estimate the probability of death of intensive care unit patients can be used to stratify patients according to the severity of their condition and to control for casemix and severity of illness. These models have been used for risk adjustment in quality monitoring, administration, management and research and as an aid to clinical decision making. Models such as the Mortality Prediction Model family, SAPS II, APACHE II, APACHE III and the organ system failure models provide estimates of the probability of in-hospital death of ICU patients. This review examines methods to assess the performance of these models. The key attributes of a model are discrimination (the accuracy of the ranking in order of probability of death) and calibration (the extent to which the model's prediction of probability of death reflects the true risk of death). These attributes should be assessed in existing models that predict the probability of patient mortality, and in any subsequent model that is developed for the purposes of estimating these probabilities. The literature contains a range of approaches for assessment which are reviewed and a survey of the methodologies used in studies of intensive care mortality models is presented. The systematic approach used by Standards for Reporting Diagnostic Accuracy provides a framework to incorporate these theoretical considerations of model assessment and recommendations are made for evaluation and presentation of the performance of models that estimate the probability of death of intensive care patients.
IMPROVED RISK ESTIMATES FOR CARBON TETRACHLORIDE
Benson, Janet M.; Springer, David L.
1999-12-31
Carbon tetrachloride has been used extensively within the DOE nuclear weapons facilities. Rocky Flats was formerly the largest volume consumer of CCl4 in the United States using 5000 gallons in 1977 alone (Ripple, 1992). At the Hanford site, several hundred thousand gallons of CCl4 were discharged between 1955 and 1973 into underground cribs for storage. Levels of CCl4 in groundwater at highly contaminated sites at the Hanford facility have exceeded 8 the drinking water standard of 5 ppb by several orders of magnitude (Illman, 1993). High levels of CCl4 at these facilities represent a potential health hazard for workers conducting cleanup operations and for surrounding communities. The level of CCl4 cleanup required at these sites and associated costs are driven by current human health risk estimates, which assume that CCl4 is a genotoxic carcinogen. The overall purpose of these studies was to improve the scientific basis for assessing the health risk associated with human exposure to CCl4. Specific research objectives of this project were to: (1) compare the rates of CCl4 metabolism by rats, mice and hamsters in vivo and extrapolate those rates to man based on parallel studies on the metabolism of CCl4 by rat, mouse, hamster and human hepatic microsomes in vitro; (2) using hepatic microsome preparations, determine the role of specific cytochrome P450 isoforms in CCl4-mediated toxicity and the effects of repeated inhalation and ingestion of CCl4 on these isoforms; and (3) evaluate the toxicokinetics of inhaled CCl4 in rats, mice and hamsters. This information has been used to improve the physiologically based pharmacokinetic (PBPK) model for CCl4 originally developed by Paustenbach et al. (1988) and more recently revised by Thrall and Kenny (1996). Another major objective of the project was to provide scientific evidence that CCl4, like chloroform, is a hepatocarcinogen only when exposure results in cell damage, cell killing and regenerative proliferation. In
The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors
Duffey, Romney B.; Saull, John W.
2006-07-01
Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum
Variance computations for functional of absolute risk estimates.
Pfeiffer, R M; Petracci, E
2011-07-01
We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.
Variance computations for functional of absolute risk estimates
Pfeiffer, R.M.; Petracci, E.
2011-01-01
We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates. PMID:21643476
Software risk estimation and management techniques at JPL
NASA Technical Reports Server (NTRS)
Hihn, J.; Lum, K.
2002-01-01
In this talk we will discuss how uncertainty has been incorporated into the JPL software model, probabilistic-based estimates, and how risk is addressed, how cost risk is currently being explored via a variety of approaches, from traditional risk lists, to detailed WBS-based risk estimates to the Defect Detection and Prevention (DDP) tool.
Probability of fracture and life extension estimate of the high-flux isotope reactor vessel
Chang, S.J.
1998-08-01
The state of the vessel steel embrittlement as a result of neutron irradiation can be measured by its increase in ductile-brittle transition temperature (DBTT) for fracture, often denoted by RT{sub NDT} for carbon steel. This transition temperature can be calibrated by the drop-weight test and, sometimes, by the Charpy impact test. The life extension for the high-flux isotope reactor (HFIR) vessel is calculated by using the method of fracture mechanics that is incorporated with the effect of the DBTT change. The failure probability of the HFIR vessel is limited as the life of the vessel by the reactor core melt probability of 10{sup {minus}4}. The operating safety of the reactor is ensured by periodic hydrostatic pressure test (hydrotest). The hydrotest is performed in order to determine a safe vessel static pressure. The fracture probability as a result of the hydrostatic pressure test is calculated and is used to determine the life of the vessel. Failure to perform hydrotest imposes the limit on the life of the vessel. The conventional method of fracture probability calculations such as that used by the NRC-sponsored PRAISE CODE and the FAVOR CODE developed in this Laboratory are based on the Monte Carlo simulation. Heavy computations are required. An alternative method of fracture probability calculation by direct probability integration is developed in this paper. The present approach offers simple and expedient ways to obtain numerical results without losing any generality. In this paper, numerical results on (1) the probability of vessel fracture, (2) the hydrotest time interval, and (3) the hydrotest pressure as a result of the DBTT increase are obtained.
Neumann, Anke; Billionnet, Cécile
2016-06-01
In observational studies without random assignment of the treatment, the unadjusted comparison between treatment groups may be misleading due to confounding. One method to adjust for measured confounders is inverse probability of treatment weighting. This method can also be used in the analysis of time to event data with competing risks. Competing risks arise if for some individuals the event of interest is precluded by a different type of event occurring before, or if only the earliest of several times to event, corresponding to different event types, is observed or is of interest. In the presence of competing risks, time to event data are often characterized by cumulative incidence functions, one for each event type of interest. We describe the use of inverse probability of treatment weighting to create adjusted cumulative incidence functions. This method is equivalent to direct standardization when the weight model is saturated. No assumptions about the form of the cumulative incidence functions are required. The method allows studying associations between treatment and the different types of event under study, while focusing on the earliest event only. We present a SAS macro implementing this method and we provide a worked example. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Probability-Weighted Ensembles of U.S. County-Level Climate Projections for Climate Risk Analysis
NASA Astrophysics Data System (ADS)
Rasmussen, D. J.; Meinshausen, Malte; Kopp, Robert E.
2016-10-01
Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce $likely$ (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8$^\\circ$C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.
Petushek, Erich J; Ward, Paul; Cokely, Edward T; Myer, Gregory D
2015-11-01
Simple observational assessment of movement is a potentially low-cost method for anterior cruciate ligament (ACL) injury screening and prevention. Although many individuals utilize some form of observational assessment of movement, there are currently no substantial data on group skill differences in observational screening of ACL injury risk. The purpose of this study was to compare various groups' abilities to visually assess ACL injury risk as well as the associated strategies and ACL knowledge levels. The hypothesis was that sports medicine professionals would perform better than coaches and exercise science academics/students and that these subgroups would all perform better than parents and other general population members. Cross-sectional study; Level of evidence, 3. A total of 428 individuals, including physicians, physical therapists, athletic trainers, strength and conditioning coaches, exercise science researchers/students, athletes, parents, and members of the general public participated in the study. Participants completed the ACL Injury Risk Estimation Quiz (ACL-IQ) and answered questions related to assessment strategy and ACL knowledge. Strength and conditioning coaches, athletic trainers, physical therapists, and exercise science students exhibited consistently superior ACL injury risk estimation ability (+2 SD) as compared with sport coaches, parents of athletes, and members of the general public. The performance of a substantial number of individuals in the exercise sciences/sports medicines (approximately 40%) was similar to or exceeded clinical instrument-based biomechanical assessment methods (eg, ACL nomogram). Parents, sport coaches, and the general public had lower ACL-IQ, likely due to their lower ACL knowledge and to rating the importance of knee/thigh motion lower and weight and jump height higher. Substantial cross-professional/group differences in visual ACL injury risk estimation exist. The relatively profound differences in injury
NASA Astrophysics Data System (ADS)
Schumacher, Sandra; Pierau, Roberto; Wirth, Wolfgang
2017-04-01
In recent years, the development of geothermal plants in Germany has increased significantly due to a favorable political setting and resulting financial incentives. However, most projects are developed by local communities or private investors, which cannot afford a project to fail. To cover the risk of total loss if the geothermal well should not provide the energy output necessary for an economically viable project, investors try to procure insurances for this worst case scenario. In order to issue such insurances, the insurance companies insist on so called probability-of-success studies (POS studies), in which the geological risk for not achieving the necessary temperatures and/or flow rates for an economically successful project is quantified. Quantifying the probability of reaching a minimum temperature, which has to be defined by the project investors, is relatively straight forward as subsurface temperatures in Germany are comparatively well known due tens of thousands of hydrocarbon wells. Moreover, for the German Molasse Basin a method to characterize the hydraulic potential of a site based on pump test analysis has been developed and refined in recent years. However, to quantify the probability of reaching a given flow rate with a given drawdown is much more challenging in areas where pump test data are generally not available (e.g. the North German Basin). Therefore, a new method based on log and core derived porosity and permeability data was developed to quantify the geological risk of reaching a determined flow rate in such areas. We present both methods for POS studies and show how subsurface data such as pump tests or log and core measurements can be used to predict the chances of a potential geothermal project from a geological point of view.
Joseph, Pawlin Vasanthi; Balan, Brindha; Rajendran, Vidhyalakshmi; Prashanthi, Devi Marimuthu; Somnathan, Balasubramanian
2015-01-01
Maps show well the spatial configuration of information. Considerable effort is devoted to the development of geographical information systems (GIS) that increase understanding of public health problems and in particular to collaborate efforts among clinicians, epidemiologists, ecologists, and geographers to map and forecast disease risk. Small populations tend to give rise to the most extreme disease rates, even if the actual rates are similar across the areas. Such situations will follow the decision-maker's attention on these areas when they scrutinize the map for decision making or resource allocation. As an alternative, maps can be prepared using P-values (probabilistic values). The statistical significance of rates rather than the rates themselves are used to map the results. The incidence rates calculated for each village from 2000 to 2009 is used to estimate λ, the expected number of cases in the study area. The obtained results are mapped using Arc GIS 10.0. The likelihood of infections from low to high is depicted in the map and it is observed that five villages namely, Odanthurai, Coimbatore Corporation, Ikkaraiboluvampatti, Puliakulam, and Pollachi Corporation are more likely to have significantly high incidences. In the probability map, some of the areas with exceptionally high or low rates disappear. These are typically small unpopulated areas, whose rates are unstable due to the small numbers problem. The probability map shows more specific regions of relative risks and expected outcomes.
Nongpiur, Monisha E; Haaland, Benjamin A; Perera, Shamira A; Friedman, David S; He, Mingguang; Sakata, Lisandro M; Baskaran, Mani; Aung, Tin
2014-01-01
To develop a score along with an estimated probability of disease for detecting angle closure based on anterior segment optical coherence tomography (AS OCT) imaging. Cross-sectional study. A total of 2047 subjects 50 years of age and older were recruited from a community polyclinic in Singapore. All subjects underwent standardized ocular examination including gonioscopy and imaging by AS OCT (Carl Zeiss Meditec). Customized software (Zhongshan Angle Assessment Program) was used to measure AS OCT parameters. Complete data were available for 1368 subjects. Data from the right eyes were used for analysis. A stepwise logistic regression model with Akaike information criterion was used to generate a score that then was converted to an estimated probability of the presence of gonioscopic angle closure, defined as the inability to visualize the posterior trabecular meshwork for at least 180 degrees on nonindentation gonioscopy. Of the 1368 subjects, 295 (21.6%) had gonioscopic angle closure. The angle closure score was calculated from the shifted linear combination of the AS OCT parameters. The score can be converted to an estimated probability of having angle closure using the relationship: estimated probability = e(score)/(1 + e(score)), where e is the natural exponential. The score performed well in a second independent sample of 178 angle-closure subjects and 301 normal controls, with an area under the receiver operating characteristic curve of 0.94. A score derived from a single AS OCT image, coupled with an estimated probability, provides an objective platform for detection of angle closure. Copyright © 2014 Elsevier Inc. All rights reserved.
Sawosz, P; Kacprzak, M; Weigl, W; Borowska-Solonynko, A; Krajewski, P; Zolek, N; Ciszek, B; Maniewski, R; Liebert, A
2012-12-07
A time-gated intensified CCD camera was applied for time-resolved imaging of light penetrating in an optically turbid medium. Spatial distributions of light penetration probability in the plane perpendicular to the axes of the source and the detector were determined at different source positions. Furthermore, visiting probability profiles of diffuse reflectance measurement were obtained by the convolution of the light penetration distributions recorded at different source positions. Experiments were carried out on homogeneous phantoms, more realistic two-layered tissue phantoms based on the human skull filled with Intralipid-ink solution and on cadavers. It was noted that the photons visiting probability profiles depend strongly on the source-detector separation, the delay between the laser pulse and the photons collection window and the complex tissue composition of the human head.
A model selection algorithm for a posteriori probability estimation with neural networks.
Arribas, Juan Ignacio; Cid-Sueiro, Jesús
2005-07-01
This paper proposes a novel algorithm to jointly determine the structure and the parameters of a posteriori probability model based on neural networks (NNs). It makes use of well-known ideas of pruning, splitting, and merging neural components and takes advantage of the probabilistic interpretation of these components. The algorithm, so called a posteriori probability model selection (PPMS), is applied to an NN architecture called the generalized softmax perceptron (GSP) whose outputs can be understood as probabilities although results shown can be extended to more general network architectures. Learning rules are derived from the application of the expectation-maximization algorithm to the GSP-PPMS structure. Simulation results show the advantages of the proposed algorithm with respect to other schemes.
NASA Astrophysics Data System (ADS)
Sawosz, P.; Kacprzak, M.; Weigl, W.; Borowska-Solonynko, A.; Krajewski, P.; Zolek, N.; Ciszek, B.; Maniewski, R.; Liebert, A.
2012-12-01
A time-gated intensified CCD camera was applied for time-resolved imaging of light penetrating in an optically turbid medium. Spatial distributions of light penetration probability in the plane perpendicular to the axes of the source and the detector were determined at different source positions. Furthermore, visiting probability profiles of diffuse reflectance measurement were obtained by the convolution of the light penetration distributions recorded at different source positions. Experiments were carried out on homogeneous phantoms, more realistic two-layered tissue phantoms based on the human skull filled with Intralipid-ink solution and on cadavers. It was noted that the photons visiting probability profiles depend strongly on the source-detector separation, the delay between the laser pulse and the photons collection window and the complex tissue composition of the human head.
NASA Technical Reports Server (NTRS)
Frigm, Ryan C.; Hejduk, Matthew D.; Johnson, Lauren C.; Plakalovic, Dragan
2015-01-01
On-orbit collision risk is becoming an increasing mission risk to all operational satellites in Earth orbit. Managing this risk can be disruptive to mission and operations, present challenges for decision-makers, and is time-consuming for all parties involved. With the planned capability improvements to detecting and tracking smaller orbital debris and capacity improvements to routinely predict on-orbit conjunctions, this mission risk will continue to grow in terms of likelihood and effort. It is very real possibility that the future space environment will not allow collision risk management and mission operations to be conducted in the same manner as it is today. This paper presents the concept of a finite conjunction assessment-one where each discrete conjunction is not treated separately but, rather, as a continuous event that must be managed concurrently. The paper also introduces the Total Probability of Collision as an analogous metric for finite conjunction assessment operations and provides several options for its usage in a Concept of Operations.
Berlanga, J.M.; Harbaugh, J.W.
1981-03-01
Seismic data can be used for statistical estimation of the exploration outcomes of specific prospects. This study involves estimation of the outcome probabilities for complexly faulted domal structures in the Tabasco basin of Mexico. Seismic reflection times were contoured by computer throughout much of the Tabasco basin, employing a special computer algorithm to accommodate the complex system of faults. Computer contouring was deemed essential for systematic statistical treatment. The probability estimates relating presence of petroleum to seismically interpreted structures involved combining two independent sources of uncertainty, namely, uncertainty in the contoured representation of the structures, and uncertainty as to the presence of petroleum in view of the specific attributes of the structures. Residuals from third-degree polynomial trend surfaces. The procedures developed could be used in other regions.
Sample Size Determination for Estimation of Sensor Detection Probabilities Based on a Test Variable
2007-06-01
interest. 15. NUMBER OF PAGES 121 14. SUBJECT TERMS Sample Size, Binomial Proportion, Confidence Interval , Coverage Probability, Experimental...THE STUDY ..........................5 II. LITERATURE REVIEW .......................................7 A. CONFIDENCE INTERVAL METHODS FOR THE...BINOMIAL PROPORTION .........................................7 1. The Wald Confidence Interval ..................7 2. The Wilson Score Confidence Interval .........13
Predicting Human Performance. I. Estimating the Probability of Visual Detection. Final Report.
ERIC Educational Resources Information Center
Teichner, Warren H.; Krebs, Marjorie J.
This review is one in a series intended to develop methods which maximize the use of the existing scientific literature as a basis for predicting human performance. It is concerned with sensory performance in target detection, defined in terms of the "probability of detection" of a flash of light. Two conditions of detection are…
How does new evidence change our estimates of probabilities? Carnap's formula revisited
NASA Technical Reports Server (NTRS)
Kreinovich, Vladik; Quintana, Chris
1992-01-01
The formula originally proposed by R. Carnap in his analysis of induction is reviewed and its natural generalization is presented. A situation is considered where the probability of a certain event is determined without using standard statistical methods due to the lack of observation.
Predicting Human Performance. I. Estimating the Probability of Visual Detection. Final Report.
ERIC Educational Resources Information Center
Teichner, Warren H.; Krebs, Marjorie J.
This review is one in a series intended to develop methods which maximize the use of the existing scientific literature as a basis for predicting human performance. It is concerned with sensory performance in target detection, defined in terms of the "probability of detection" of a flash of light. Two conditions of detection are…
NASA Astrophysics Data System (ADS)
Denzler, Stefan M.; Dacorogna, Michel M.; Muller, Ulrich A.; McNeil, Alexander J.
2005-05-01
Credit risk models like Moody's KMV are now well established in the market and give bond managers reliable default probabilities for individual firms. Until now it has been hard to relate those probabilities to the actual credit spreads observed on the market for corporate bonds. Inspired by the existence of scaling laws in financial markets by Dacorogna et al. 2001 and DiMatteo et al. 2005 deviating from the Gaussian behavior, we develop a model that quantitatively links those default probabilities to credit spreads (market prices). The main input quantities to this study are merely industry yield data of different times to maturity and expected default frequencies (EDFs) of Moody's KMV. The empirical results of this paper clearly indicate that the model can be used to calculate approximate credit spreads (market prices) from EDFs, independent of the time to maturity and the industry sector under consideration. Moreover, the model is effective in an out-of-sample setting, it produces consistent results on the European bond market where data are scarce and can be adequately used to approximate credit spreads on the corporate level.
Farmer, William H.; Koltun, Greg
2017-01-01
Study regionThe state of Ohio in the United States, a humid, continental climate.Study focusThe estimation of nonexceedance probabilities of daily streamflows as an alternative means of establishing the relative magnitudes of streamflows associated with hydrologic and water-quality observations.New hydrological insights for the regionSeveral methods for estimating nonexceedance probabilities of daily mean streamflows are explored, including single-index methodologies (nearest-neighboring index) and geospatial tools (kriging and topological kriging). These methods were evaluated by conducting leave-one-out cross-validations based on analyses of nearly 7 years of daily streamflow data from 79 unregulated streamgages in Ohio and neighboring states. The pooled, ordinary kriging model, with a median Nash–Sutcliffe performance of 0.87, was superior to the single-site index methods, though there was some bias in the tails of the probability distribution. Incorporating network structure through topological kriging did not improve performance. The pooled, ordinary kriging model was applied to 118 locations without systematic streamgaging across Ohio where instantaneous streamflow measurements had been made concurrent with water-quality sampling on at least 3 separate days. Spearman rank correlations between estimated nonexceedance probabilities and measured streamflows were high, with a median value of 0.76. In consideration of application, the degree of regulation in a set of sample sites helped to specify the streamgages required to implement kriging approaches successfully.
Kline, Jeffrey A; Stubblefield, William B
2014-03-01
Pretest probability helps guide diagnostic testing for patients with suspected acute coronary syndrome and pulmonary embolism. Pretest probability derived from the clinician's unstructured gestalt estimate is easier and more readily available than methods that require computation. We compare the diagnostic accuracy of physician gestalt estimate for the pretest probability of acute coronary syndrome and pulmonary embolism with a validated, computerized method. This was a secondary analysis of a prospectively collected, multicenter study. Patients (N=840) had chest pain, dyspnea, nondiagnostic ECGs, and no obvious diagnosis. Clinician gestalt pretest probability for both acute coronary syndrome and pulmonary embolism was assessed by visual analog scale and from the method of attribute matching using a Web-based computer program. Patients were followed for outcomes at 90 days. Clinicians had significantly higher estimates than attribute matching for both acute coronary syndrome (17% versus 4%; P<.001, paired t test) and pulmonary embolism (12% versus 6%; P<.001). The 2 methods had poor correlation for both acute coronary syndrome (r(2)=0.15) and pulmonary embolism (r(2)=0.06). Areas under the receiver operating characteristic curve were lower for clinician estimate compared with the computerized method for acute coronary syndrome: 0.64 (95% confidence interval [CI] 0.51 to 0.77) for clinician gestalt versus 0.78 (95% CI 0.71 to 0.85) for attribute matching. For pulmonary embolism, these values were 0.81 (95% CI 0.79 to 0.92) for clinician gestalt and 0.84 (95% CI 0.76 to 0.93) for attribute matching. Compared with a validated machine-based method, clinicians consistently overestimated pretest probability but on receiver operating curve analysis were as accurate for pulmonary embolism but not acute coronary syndrome. Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.
Bureau, Alexandre; Younkin, Samuel G.; Parker, Margaret M.; Bailey-Wilson, Joan E.; Marazita, Mary L.; Murray, Jeffrey C.; Mangold, Elisabeth; Albacha-Hejazi, Hasan; Beaty, Terri H.; Ruczinski, Ingo
2014-01-01
Motivation: Family-based designs are regaining popularity for genomic sequencing studies because they provide a way to test cosegregation with disease of variants that are too rare in the population to be tested individually in a conventional case–control study. Results: Where only a few affected subjects per family are sequenced, the probability that any variant would be shared by all affected relatives—given it occurred in any one family member—provides evidence against the null hypothesis of a complete absence of linkage and association. A P-value can be obtained as the sum of the probabilities of sharing events as (or more) extreme in one or more families. We generalize an existing closed-form expression for exact sharing probabilities to more than two relatives per family. When pedigree founders are related, we show that an approximation of sharing probabilities based on empirical estimates of kinship among founders obtained from genome-wide marker data is accurate for low levels of kinship. We also propose a more generally applicable approach based on Monte Carlo simulations. We applied this method to a study of 55 multiplex families with apparent non-syndromic forms of oral clefts from four distinct populations, with whole exome sequences available for two or three affected members per family. The rare single nucleotide variant rs149253049 in ADAMTS9 shared by affected relatives in three Indian families achieved significance after correcting for multiple comparisons (p=2×10−6). Availability and implementation: Source code and binaries of the R package RVsharing are freely available for download at http://cran.r-project.org/web/packages/RVsharing/index.html. Contact: alexandre.bureau@msp.ulaval.ca or ingo@jhu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24740360
Accurate Estimation of the Entropy of Rotation-Translation Probability Distributions.
Fogolari, Federico; Dongmo Foumthuim, Cedrix Jurgal; Fortuna, Sara; Soler, Miguel Angel; Corazza, Alessandra; Esposito, Gennaro
2016-01-12
The estimation of rotational and translational entropies in the context of ligand binding has been the subject of long-time investigations. The high dimensionality (six) of the problem and the limited amount of sampling often prevent the required resolution to provide accurate estimates by the histogram method. Recently, the nearest-neighbor distance method has been applied to the problem, but the solutions provided either address rotation and translation separately, therefore lacking correlations, or use a heuristic approach. Here we address rotational-translational entropy estimation in the context of nearest-neighbor-based entropy estimation, solve the problem numerically, and provide an exact and an approximate method to estimate the full rotational-translational entropy.
Estimation of health risks from radiation exposures
Randolph, M.L.
1983-08-01
An informal presentation is given of the cancer and genetic risks from exposures to ionizing radiations. The risks from plausible radiation exposures are shown to be comparable to other commonly encountered risks.
NASA Technical Reports Server (NTRS)
Pierson, Willard J., Jr.
1989-01-01
The values of the Normalized Radar Backscattering Cross Section (NRCS), sigma (o), obtained by a scatterometer are random variables whose variance is a known function of the expected value. The probability density function can be obtained from the normal distribution. Models for the expected value obtain it as a function of the properties of the waves on the ocean and the winds that generated the waves. Point estimates of the expected value were found from various statistics given the parameters that define the probability density function for each value. Random intervals were derived with a preassigned probability of containing that value. A statistical test to determine whether or not successive values of sigma (o) are truly independent was derived. The maximum likelihood estimates for wind speed and direction were found, given a model for backscatter as a function of the properties of the waves on the ocean. These estimates are biased as a result of the terms in the equation that involve natural logarithms, and calculations of the point estimates of the maximum likelihood values are used to show that the contributions of the logarithmic terms are negligible and that the terms can be omitted.
Probable Health Risks Due to Exposure to Outdoor PM2.5 in India
NASA Astrophysics Data System (ADS)
Dey, S.; Chowdhury, S.
2014-12-01
Particulate matter of size <2.5 μm (commonly referred to as PM2.5) is considered to be the best indicator of health risks due to exposure to particulate pollution. Unlike the decreasing trends in the developed countries, aerosol loading continues to increase over the Indian subcontinent in the recent past, exposing ~1.6 billion population at risk. Lack of direct measurements prompted us to utilize satellite data in establishing a robust long-term database of surface PM2.5 at high spatial resolution. The hybrid approach utilizes a chemical transport model to constrain the relation between columnar aerosol optical depth (AOD) and surface PM2.5 and establish mean monthly conversion factor. Satellite-derived daily AODs for the period 2000-2012 are then converted to PM2.5 using the conversion factors. The dataset (after validation against coincident in-situ measurements and bias-correction) was used to carry out the exposure assessment. 51% of the population is exposed to PM2.5 concentration exceeding WHO air quality interim target-3 threshold (35 μg m-3). The health impacts are categorized in terms of four diseases - cardio ortho-pulmonary disease (COPD), stroke, ischemic heart disease (IHD) and lung cancer (LC). In absence of any region-specific cohort study, published studies are consulted to estimate risk. The risks relative to the background concentration of 10 μg m-3 are estimated by logarithmic fitting of the individual cohort studies against the corresponding PM2.5 concentration. This approach considers multiple (>100) cohort studies across a wide variety of adult population from various socio-economic backgrounds. Therefore, the calculated risks are considered to be better estimates in relative to any one particular type of risk function model (e.g. linear 50 or linear 70 or exponential). The risk values are used to calculate the additional mortality due to exposure to PM2.5 in each of the administrative districts in India to identify the vulnerable regions
O'Donnell, Matthew J.; Horton, Gregg E.; Letcher, Benjamin H.
2010-01-01
Portable passive integrated transponder (PIT) tag antenna systems can be valuable in providing reliable estimates of the abundance of tagged Atlantic salmon Salmo salar in small streams under a wide range of conditions. We developed and employed PIT tag antenna wand techniques in two controlled experiments and an additional case study to examine the factors that influenced our ability to estimate population size. We used Pollock's robust-design capture–mark–recapture model to obtain estimates of the probability of first detection (p), the probability of redetection (c), and abundance (N) in the two controlled experiments. First, we conducted an experiment in which tags were hidden in fixed locations. Although p and c varied among the three observers and among the three passes that each observer conducted, the estimates of N were identical to the true values and did not vary among observers. In the second experiment using free-swimming tagged fish, p and c varied among passes and time of day. Additionally, estimates of N varied between day and night and among age-classes but were within 10% of the true population size. In the case study, we used the Cormack–Jolly–Seber model to examine the variation in p, and we compared counts of tagged fish found with the antenna wand with counts collected via electrofishing. In that study, we found that although p varied for age-classes, sample dates, and time of day, antenna and electrofishing estimates of N were similar, indicating that population size can be reliably estimated via PIT tag antenna wands. However, factors such as the observer, time of day, age of fish, and stream discharge can influence the initial and subsequent detection probabilities.
A novel approach to estimate the eruptive potential and probability in open conduit volcanoes
De Gregorio, Sofia; Camarda, Marco
2016-01-01
In open conduit volcanoes, volatile-rich magma continuously enters into the feeding system nevertheless the eruptive activity occurs intermittently. From a practical perspective, the continuous steady input of magma in the feeding system is not able to produce eruptive events alone, but rather surplus of magma inputs are required to trigger the eruptive activity. The greater the amount of surplus of magma within the feeding system, the higher is the eruptive probability.Despite this observation, eruptive potential evaluations are commonly based on the regular magma supply, and in eruptive probability evaluations, generally any magma input has the same weight. Conversely, herein we present a novel approach based on the quantification of surplus of magma progressively intruded in the feeding system. To quantify the surplus of magma, we suggest to process temporal series of measurable parameters linked to the magma supply. We successfully performed a practical application on Mt Etna using the soil CO2 flux recorded over ten years. PMID:27456812
A novel approach to estimate the eruptive potential and probability in open conduit volcanoes.
De Gregorio, Sofia; Camarda, Marco
2016-07-26
In open conduit volcanoes, volatile-rich magma continuously enters into the feeding system nevertheless the eruptive activity occurs intermittently. From a practical perspective, the continuous steady input of magma in the feeding system is not able to produce eruptive events alone, but rather surplus of magma inputs are required to trigger the eruptive activity. The greater the amount of surplus of magma within the feeding system, the higher is the eruptive probability.Despite this observation, eruptive potential evaluations are commonly based on the regular magma supply, and in eruptive probability evaluations, generally any magma input has the same weight. Conversely, herein we present a novel approach based on the quantification of surplus of magma progressively intruded in the feeding system. To quantify the surplus of magma, we suggest to process temporal series of measurable parameters linked to the magma supply. We successfully performed a practical application on Mt Etna using the soil CO2 flux recorded over ten years.
Valero, Antonio; Pasquali, Frédérique; De Cesare, Alessandra; Manfreda, Gerardo
2014-08-01
Current sampling plans assume a random distribution of microorganisms in food. However, food-borne pathogens are estimated to be heterogeneously distributed in powdered foods. This spatial distribution together with very low level of contaminations raises concern of the efficiency of current sampling plans for the detection of food-borne pathogens like Cronobacter and Salmonella in powdered foods such as powdered infant formula or powdered eggs. An alternative approach based on a Poisson distribution of the contaminated part of the lot (Habraken approach) was used in order to evaluate the probability of falsely accepting a contaminated lot of powdered food when different sampling strategies were simulated considering variables such as lot size, sample size, microbial concentration in the contaminated part of the lot and proportion of contaminated lot. The simulated results suggest that a sample size of 100g or more corresponds to the lower number of samples to be tested in comparison with sample sizes of 10 or 1g. Moreover, the number of samples to be tested greatly decrease if the microbial concentration is 1CFU/g instead of 0.1CFU/g or if the proportion of contamination is 0.05 instead of 0.01. Mean contaminations higher than 1CFU/g or proportions higher than 0.05 did not impact on the number of samples. The Habraken approach represents a useful tool for risk management in order to design a fit-for-purpose sampling plan for the detection of low levels of food-borne pathogens in heterogeneously contaminated powdered food. However, it must be outlined that although effective in detecting pathogens, these sampling plans are difficult to be applied since the huge number of samples that needs to be tested. Sampling does not seem an effective measure to control pathogens in powdered food.
Timonina, Anna; Hochrainer-Stigler, Stefan; Pflug, Georg; Jongman, Brenden; Rojas, Rodrigo
2015-11-01
Losses due to natural hazard events can be extraordinarily high and difficult to cope with. Therefore, there is considerable interest to estimate the potential impact of current and future extreme events at all scales in as much detail as possible. As hazards typically spread over wider areas, risk assessment must take into account interrelations between regions. Neglecting such interdependencies can lead to a severe underestimation of potential losses, especially for extreme events. This underestimation of extreme risk can lead to the failure of riskmanagement strategies when they are most needed, namely, in times of unprecedented events. In this article, we suggest a methodology to incorporate such interdependencies in risk via the use of copulas. We demonstrate that by coupling losses, dependencies can be incorporated in risk analysis, avoiding the underestimation of risk. Based on maximum discharge data of river basins and stream networks, we present and discuss different ways to couple loss distributions of basins while explicitly incorporating tail dependencies. We distinguish between coupling methods that require river structure data for the analysis and those that do not. For the later approach we propose a minimax algorithm to choose coupled basin pairs so that the underestimation of risk is avoided and the use of river structure data is not needed. The proposed methodology is especially useful for large-scale analysis and we motivate and apply our method using the case of Romania. The approach can be easily extended to other countries and natural hazards.
INCLUDING TRANSITION PROBABILITIES IN NEST SURVIVAL ESTIMATION: A MAYFIELD MARKOV CHAIN
This manuscript is primarily an exploration of the statistical properties of nest-survival estimates for terrestrial songbirds. The Mayfield formulation described herein should allow researchers to test for complicated effects of stressors on daily survival and overall success, i...
Uncertainty of Calculated Risk Estimates for Secondary Malignancies After Radiotherapy
Kry, Stephen F. . E-mail: sfkry@mdanderson.org; Followill, David; White, R. Allen; Stovall, Marilyn; Kuban, Deborah A.; Salehpour, Mohammad
2007-07-15
Purpose: The significance of risk estimates for fatal secondary malignancies caused by out-of-field radiation exposure remains unresolved because the uncertainty in calculated risk estimates has not been established. This work examines the uncertainty in absolute risk estimates and in the ratio of risk estimates between different treatment modalities. Methods and Materials: Clinically reasonable out-of-field doses and calculated risk estimates were taken from the literature for several prostate treatment modalities, including intensity-modulated radiotherapy (IMRT), and were recalculated using the most recent risk model. The uncertainties in this risk model and uncertainties in the linearity of the dose-response model were considered in generating 90% confidence intervals for the uncertainty in the absolute risk estimates and in the ratio of the risk estimates. Results: The absolute risk estimates of fatal secondary malignancy were associated with very large uncertainties, which precluded distinctions between the risks associated with the different treatment modalities considered. However, a much smaller confidence interval exists for the ratio of risk estimates, and this ratio between different treatment modalities may be statistically significant when there is an effective dose equivalent difference of at least 50%. Such a difference may exist between clinically reasonable treatment options, including 6-MV IMRT versus 18-MV IMRT for prostate therapy. Conclusion: The ratio of the risk between different treatment modalities may be significantly different. Consequently risk models and associated risk estimates may be useful and meaningful for evaluating different treatment options. The calculated risk of secondary malignancy should be considered in the selection of an optimal treatment plan.
NASA Astrophysics Data System (ADS)
Tan, Elcin
A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the
Viele, Kert; Stromberg, Arnold J; Cooper, Robin L
2003-01-01
Investigating the function of individual synapses is essential to understanding the mechanisms that influence the efficacy of chemical synaptic transmission. The known simplicity of the synaptic structure at the crayfish neuromuscular junction (NMJ) and its quantal nature of release allows an assessment of discrete synapses within the motor nerve terminals. Our goal in this article is to investigate the effect of the stimulation frequency on the number of active release sites (n) and the probability of release (p) at those active sites. Because methods based on direct counts often provide unstable joint estimates of (n) and (p), we base our analysis on mixture modeling. In particular, the mixture modeling approach is used to estimate (n) and (p) for stimulation frequencies of 1 Hz, 2 Hz, and 3 Hz. Our results indicate that as the stimulation frequency increases, new sites are recruited (thus increasing n) and the probability of release (p) increases. Copyright 2002 Wiley-Liss, Inc.
Anderson, Christian C; Bauer, Adam Q; Holland, Mark R; Pakula, Michal; Laugier, Pascal; Bretthorst, G Larry; Miller, James G
2010-11-01
Quantitative ultrasonic characterization of cancellous bone can be complicated by artifacts introduced by analyzing acquired data consisting of two propagating waves (a fast wave and a slow wave) as if only one wave were present. Recovering the ultrasonic properties of overlapping fast and slow waves could therefore lead to enhancement of bone quality assessment. The current study uses Bayesian probability theory to estimate phase velocity and normalized broadband ultrasonic attenuation (nBUA) parameters in a model of fast and slow wave propagation. Calculations are carried out using Markov chain Monte Carlo with simulated annealing to approximate the marginal posterior probability densities for parameters in the model. The technique is applied to simulated data, to data acquired on two phantoms capable of generating two waves in acquired signals, and to data acquired on a human femur condyle specimen. The models are in good agreement with both the simulated and experimental data, and the values of the estimated ultrasonic parameters fall within expected ranges.
Zhang Yumin; Lum, Kai-Yew; Wang Qingguo
2009-03-05
In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.
Davies, Christopher E; Giles, Lynne C; Glonek, Gary Fv
2017-01-01
One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this '1-step' approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the '3-step' approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt's improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.
NASA Astrophysics Data System (ADS)
Sun, Pengfei; Qin, Jun
2017-02-01
In this paper, a two-stage dual tree complex wavelet packet transform (DTCWPT) based speech enhancement algorithm has been proposed, in which a speech presence probability (SPP) estimator and a generalized minimum mean squared error (MMSE) estimator are developed. To overcome the drawback of signal distortions caused by down sampling of WPT, a two-stage analytic decomposition concatenating undecimated WPT (UWPT) and decimated WPT is employed. An SPP estimator in the DTCWPT domain is derived based on a generalized Gamma distribution of speech, and Gaussian noise assumption. The validation results show that the proposed algorithm can obtain enhanced perceptual evaluation of speech quality (PESQ), and segmental signal-to-noise ratio (SegSNR) at low SNR nonstationary noise, compared with other four state-of-the-art speech enhancement algorithms, including optimally modified LSA (OM-LSA), soft masking using a posteriori SNR uncertainty (SMPO), a posteriori SPP based MMSE estimation (MMSE-SPP), and adaptive Bayesian wavelet thresholding (BWT).
Wang, Bei; Wang, Xingyu; Zhang, Tao; Nakamura, Masatoshi
2013-01-01
An automatic sleep level estimation method was developed for monitoring and regulation of day time nap sleep. The recorded nap data is separated into continuous 5-second segments. Features are extracted from EEGs, EOGs and EMG. A parameter of sleep level is defined which is estimated based on the conditional probability of sleep stages. An exponential smoothing method is applied for the estimated sleep level. There were totally 12 healthy subjects, with an averaged age of 22 yeas old, participated into the experimental work. Comparing with sleep stage determination, the presented sleep level estimation method showed better performance for nap sleep interpretation. Real time monitoring and regulation of nap is realizable based on the developed technique.
NASA Technical Reports Server (NTRS)
Watson, Clifford
2010-01-01
Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the twodimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the least-well-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and threedimensional charting gives a visual confirmation of the relationship between causes and their controls
NASA Technical Reports Server (NTRS)
Watson, Clifford C.
2011-01-01
Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the two-dimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the least-well-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and three-dimensional charting gives a visual confirmation of the relationship between causes and their controls.
Dorval, Alan D
2008-08-15
The maximal information that the spike train of any neuron can pass on to subsequent neurons can be quantified as the neuronal firing pattern entropy. Difficulties associated with estimating entropy from small datasets have proven an obstacle to the widespread reporting of firing pattern entropies and more generally, the use of information theory within the neuroscience community. In the most accessible class of entropy estimation techniques, spike trains are partitioned linearly in time and entropy is estimated from the probability distribution of firing patterns within a partition. Ample previous work has focused on various techniques to minimize the finite dataset bias and standard deviation of entropy estimates from under-sampled probability distributions on spike timing events partitioned linearly in time. In this manuscript we present evidence that all distribution-based techniques would benefit from inter-spike intervals being partitioned in logarithmic time. We show that with logarithmic partitioning, firing rate changes become independent of firing pattern entropy. We delineate the entire entropy estimation process with two example neuronal models, demonstrating the robust improvements in bias and standard deviation that the logarithmic time method yields over two widely used linearly partitioned time approaches.
Development of a statistical tool for the estimation of riverbank erosion probability
NASA Astrophysics Data System (ADS)
Varouchakis, E. A.; Giannakis, G. V.; Lilli, M. A.; Ioannidou, E.; Nikolaidis, N. P.; Karatzas, G. P.
2016-01-01
Riverbank erosion affects river morphology and local habitat, and results in riparian land loss, property and infrastructure damage, and ultimately flood defence weakening. An important issue concerning riverbank erosion is the identification of the vulnerable areas in order to predict river changes and assist stream management/restoration. An approach to predict areas vulnerable to erosion is to quantify the erosion probability by identifying the underlying relations between riverbank erosion and geomorphological or hydrological variables that prevent or stimulate erosion. In the present work, a statistical methodology is proposed to predict the probability of the presence or absence of erosion in a river section. A physically based model determines the locations vulnerable to erosion by quantifying the potential eroded area. The derived results are used to determine validation locations for the evaluation of the statistical tool performance. The statistical tool is based on a series of independent local variables and employs the logistic regression methodology. It is developed in two forms, logistic regression and locally weighted logistic regression, which both deliver useful and accurate results. The second form, though, provides the most accurate results as it validates the presence or absence of erosion at all validation locations. The proposed tool is easy to use and accurate and can be applied to any region and river.
Development of a statistical tool for the estimation of riverbank erosion probability
NASA Astrophysics Data System (ADS)
Varouchakis, E. A.; Giannakis, G. V.; Lilli, M. A.; Ioannidou, E.; Nikolaidis, N. P.; Karatzas, G. P.
2015-06-01
Riverbank erosion affects river morphology and local habitat and results in riparian land loss, property and infrastructure damage, and ultimately flood defence weakening. An important issue concerning riverbank erosion is the identification of the vulnerable areas in order to predict river changes and assist stream management/restoration. An approach to predict vulnerable to erosion areas is to quantify the erosion probability by identifying the underlying relations between riverbank erosion and geomorphological or hydrological variables that prevent or stimulate erosion. In the present work, a combined deterministic and statistical methodology is proposed to predict the probability of presence or absence of erosion in a river section. A physically based model determines the vulnerable to erosion locations by quantifying the potential eroded area. The derived results are used to determine validation locations for the statistical tool performance evaluation. The statistical tool is based on a series of independent local variables and employs the Logistic Regression methodology. It is developed in two forms, Logistic Regression and Locally Weighted Logistic Regression, which both deliver useful and accurate results. The second form though provides the most accurate results as it validates the presence or absence of erosion at all validation locations. The proposed methodology is easy to use, accurate and can be applied to any region and river.
How are flood risk estimates affected by the choice of return-periods?
NASA Astrophysics Data System (ADS)
Ward, P. J.; Aerts, J. C. J. H.; De Moel, H.; Poussin, J. K.
2012-04-01
Flood management is more and more adopting a risk based approach, whereby flood risk is the product of the probability and consequences of flooding. One of the most common approaches in flood risk assessment is to estimate the damage that would occur for floods of several exceedance probabilities (or return periods), to plot these on an exceedance probability-loss curve (risk curve) and to estimate risk as the area under the curve. However, there is little insight into how the selection of the return-periods (which ones and how many) used to calculate risk actually affects the final risk calculation. To gain such insights, we developed and validated an inundation model capable of rapidly simulating inundation extent and depth, and dynamically coupled this to an existing damage model. The method was applied to a section of the River Meuse in the southeast of the Netherlands. Firstly, we estimated risk based on a risk curve using yearly return periods from 2 to 10 000 yr (€ 34 million p.a.). We found that the overall risk is greatly affected by the number of return periods used to construct the risk curve, with over-estimations of annual risk between 33% and 100% when only three return periods are used. Also, the final risk estimate is greatly dependent on the minimum and maximum return periods (and their associated damages) used in the construction of the risk curve. In addition, binary assumptions on dike failure can have a large effect (a factor two difference) on risk estimates. The results suggest that more research is needed to develop relatively simple inundation models that can be used to produce large numbers of inundation maps, complementary to more complex 2D-3D hydrodynamic models. We then used the insights and models described above to assess the relative change in risk between current conditions and several scenarios of land use and climate change. For the case study region, we found that future land use change has a larger impact than future climate
ARMA Estimators of Probability Densities with Exponential or Regularly Varying Fourier Coefficients.
1987-06-01
of the smoothing parameter of fn (’m) (see Hart 1985 and Diggle and Hall 1986 for more on this subject). The integrated squared errors of the cross...Statist. 5 530-535. Diggle , P.J. and Hall, P. (1986). The selection of terms in an orthogonal series density estimator. J. Amer. Statist. Assoc. 81 230-233
Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses...
Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses...
Jean-Yves Courbois; Stephen L. Katz; Daniel J. Isaak; E. Ashley Steel; Russell F. Thurow; A. Michelle Wargo Rub; Tony Olsen; Chris E. Jordan
2008-01-01
Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses of Chinook salmon (Oncorhynchus tshawytscha) redds from central Idaho,...
Herts, Brian R; Schneider, Erika; Obuchowski, Nancy; Poggio, Emilio; Jain, Anil; Baker, Mark E
2009-08-01
probability of a reduced estimated GFR after CECT can be predicted by the pre-CT estimated GFR using the four-variable MDRD equation. Furthermore, standard criteria for contrast-induced nephropathy are poor predictors of poor renal function after CECT. Criteria need to be established for what is an acceptable risk to manage patients undergoing CECT.
Voon, Valerie; Morris, Laurel S; Irvine, Michael A; Ruck, Christian; Worbe, Yulia; Derbyshire, Katherine; Rankov, Vladan; Schreiber, Liana Rn; Odlaug, Brian L; Harrison, Neil A; Wood, Jonathan; Robbins, Trevor W; Bullmore, Edward T; Grant, Jon E
2015-03-01
Pathological behaviors toward drugs and food rewards have underlying commonalities. Risk-taking has a fourfold pattern varying as a function of probability and valence leading to the nonlinearity of probability weighting with overweighting of small probabilities and underweighting of large probabilities. Here we assess these influences on risk-taking in patients with pathological behaviors toward drug and food rewards and examine structural neural correlates of nonlinearity of probability weighting in healthy volunteers. In the anticipation of rewards, subjects with binge eating disorder show greater risk-taking, similar to substance-use disorders. Methamphetamine-dependent subjects had greater nonlinearity of probability weighting along with impaired subjective discrimination of probability and reward magnitude. Ex-smokers also had lower risk-taking to rewards compared with non-smokers. In the anticipation of losses, obesity without binge eating had a similar pattern to other substance-use disorders. Obese subjects with binge eating also have impaired discrimination of subjective value similar to that of the methamphetamine-dependent subjects. Nonlinearity of probability weighting was associated with lower gray matter volume in dorsolateral and ventromedial prefrontal cortex and orbitofrontal cortex in healthy volunteers. Our findings support a distinct subtype of binge eating disorder in obesity with similarities in risk-taking in the reward domain to substance use disorders. The results dovetail with the current approach of defining mechanistically based dimensional approaches rather than categorical approaches to psychiatric disorders. The relationship to risk probability and valence may underlie the propensity toward pathological behaviors toward different types of rewards.
Voon, Valerie; Morris, Laurel S; Irvine, Michael A; Ruck, Christian; Worbe, Yulia; Derbyshire, Katherine; Rankov, Vladan; Schreiber, Liana RN; Odlaug, Brian L; Harrison, Neil A; Wood, Jonathan; Robbins, Trevor W; Bullmore, Edward T; Grant, Jon E
2015-01-01
Pathological behaviors toward drugs and food rewards have underlying commonalities. Risk-taking has a fourfold pattern varying as a function of probability and valence leading to the nonlinearity of probability weighting with overweighting of small probabilities and underweighting of large probabilities. Here we assess these influences on risk-taking in patients with pathological behaviors toward drug and food rewards and examine structural neural correlates of nonlinearity of probability weighting in healthy volunteers. In the anticipation of rewards, subjects with binge eating disorder show greater risk-taking, similar to substance-use disorders. Methamphetamine-dependent subjects had greater nonlinearity of probability weighting along with impaired subjective discrimination of probability and reward magnitude. Ex-smokers also had lower risk-taking to rewards compared with non-smokers. In the anticipation of losses, obesity without binge eating had a similar pattern to other substance-use disorders. Obese subjects with binge eating also have impaired discrimination of subjective value similar to that of the methamphetamine-dependent subjects. Nonlinearity of probability weighting was associated with lower gray matter volume in dorsolateral and ventromedial prefrontal cortex and orbitofrontal cortex in healthy volunteers. Our findings support a distinct subtype of binge eating disorder in obesity with similarities in risk-taking in the reward domain to substance use disorders. The results dovetail with the current approach of defining mechanistically based dimensional approaches rather than categorical approaches to psychiatric disorders. The relationship to risk probability and valence may underlie the propensity toward pathological behaviors toward different types of rewards. PMID:25270821
NASA Technical Reports Server (NTRS)
Huddleston, Lisa; Roeder, WIlliam P.; Merceret, Francis J.
2011-01-01
A new technique has been developed to estimate the probability that a nearby cloud-to-ground lightning stroke was within a specified radius of any point of interest. This process uses the bivariate Gaussian distribution of probability density provided by the current lightning location error ellipse for the most likely location of a lightning stroke and integrates it to determine the probability that the stroke is inside any specified radius of any location, even if that location is not centered on or even within the location error ellipse. This technique is adapted from a method of calculating the probability of debris collision with spacecraft. Such a technique is important in spaceport processing activities because it allows engineers to quantify the risk of induced current damage to critical electronics due to nearby lightning strokes. This technique was tested extensively and is now in use by space launch organizations at Kennedy Space Center and Cape Canaveral Air Force station. Future applications could include forensic meteorology.
Rønjom, Marianne F; Brink, Carsten; Lorenzen, Ebbe L; Hegedüs, Laszlo; Johansen, Jørgen
2015-01-01
To examine the variations of risk-estimates of radiation-induced hypothyroidism (HT) from our previously developed normal tissue complication probability (NTCP) model in patients with head and neck squamous cell carcinoma (HNSCC) in relation to variability of delineation of the thyroid gland. In a previous study for development of an NTCP model for HT, the thyroid gland was delineated in 246 treatment plans of patients with HNSCC. Fifty of these plans were randomly chosen for re-delineation for a study of the intra- and inter-observer variability of thyroid volume, Dmean and estimated risk of HT. Bland-Altman plots were used for assessment of the systematic (mean) and random [standard deviation (SD)] variability of the three parameters, and a method for displaying the spatial variation in delineation differences was developed. Intra-observer variability resulted in a mean difference in thyroid volume and Dmean of 0.4 cm(3) (SD ± 1.6) and -0.5 Gy (SD ± 1.0), respectively, and 0.3 cm(3) (SD ± 1.8) and 0.0 Gy (SD ± 1.3) for inter-observer variability. The corresponding mean differences of NTCP values for radiation-induced HT due to intra- and inter-observer variations were insignificantly small, -0.4% (SD ± 6.0) and -0.7% (SD ± 4.8), respectively, but as the SDs show, for some patients the difference in estimated NTCP was large. For the entire study population, the variation in predicted risk of radiation-induced HT in head and neck cancer was small and our NTCP model was robust against observer variations in delineation of the thyroid gland. However, for the individual patient, there may be large differences in estimated risk which calls for precise delineation of the thyroid gland to obtain correct dose and NTCP estimates for optimized treatment planning in the individual patient.
Schiebener, Johannes; Zamarian, Laura; Delazer, Margarete; Brand, Matthias
2011-11-01
In two experiments with healthy subjects, we used the Game of Dice Task (GDT), the Probability-Associated Gambling (PAG) task, the Iowa Gambling Task (IGT), and executive-function and logical thinking tasks to shed light on the underlying processes of decision making under risk. Results indicate that handling probabilities, as in the PAG task, is an important ingredient of GDT performance. Executive functions and logical thinking also play major roles in deciding in the GDT. Implicit feedback learning, as measured by the IGT, has little impact. Results suggest that good probability handling may compensate for the effects of weak executive functions in decisions under risk.
Arnold, W Ray; Warren-Hicks, William J
2007-01-01
The object of this study was to estimate site- and region-specific dissolved copper criteria for a large embayment, the Chesapeake Bay, USA. The intent is to show the utility of 2 copper saltwater quality site-specific criteria estimation models and associated region-specific criteria selection methods. The criteria estimation models and selection methods are simple, efficient, and cost-effective tools for resource managers. The methods are proposed as potential substitutes for the US Environmental Protection Agency's water effect ratio methods. Dissolved organic carbon data and the copper criteria models were used to produce probability-based estimates of site-specific copper saltwater quality criteria. Site- and date-specific criteria estimations were made for 88 sites (n = 5,296) in the Chesapeake Bay. The average and range of estimated site-specific chronic dissolved copper criteria for the Chesapeake Bay were 7.5 and 5.3 to 16.9 microg Cu/L. The average and range of estimated site-specific acute dissolved copper criteria for the Chesapeake Bay were 11.7 and 8.3 to 26.4 microg Cu/L. The results suggest that applicable national and state copper criteria can increase in much of the Chesapeake Bay and remain protective. Virginia Department of Environmental Quality copper criteria near the mouth of the Chesapeake Bay, however, need to decrease to protect species of equal or greater sensitivity to that of the marine mussel, Mytilus sp.
Park, Dong-Uk; Colt, Joanne S.; Baris, Dalsu; Schwenn, Molly; Karagas, Margaret R.; Armenti, Karla R.; Johnson, Alison; Silverman, Debra T; Stewart, Patricia A
2014-01-01
We describe here an approach for estimating the probability that study subjects were exposed to metalworking fluids (MWFs) in a population-based case-control study of bladder cancer. Study subject reports on the frequency of machining and use of specific MWFs (straight, soluble, and synthetic/semi-synthetic) were used to estimate exposure probability when available. Those reports also were used to develop estimates for job groups, which were then applied to jobs without MWF reports. Estimates using both cases and controls and controls only were developed. The prevalence of machining varied substantially across job groups (10-90%), with the greatest percentage of jobs that machined being reported by machinists and tool and die workers. Reports of straight and soluble MWF use were fairly consistent across job groups (generally, 50-70%). Synthetic MWF use was lower (13-45%). There was little difference in reports by cases and controls vs. controls only. Approximately, 1% of the entire study population was assessed as definitely exposed to straight or soluble fluids in contrast to 0.2% definitely exposed to synthetic/semi-synthetics. A comparison between the reported use of the MWFs and the US production levels by decade found high correlations (r generally >0.7). Overall, the method described here is likely to have provided a systematic and reliable ranking that better reflects the variability of exposure to three types of MWFs than approaches applied in the past. PMID:25256317
Goday, A; Gabriel, R; Ascaso, J F; Franch, J; Ortega, R; Martínez, O; Lerones, N
2008-09-01
The metabolic syndrome is an association of closely related alterations. The main objective of this study is to know the frequency of the metabolic syndrome and insulin resistance, and their role as cardiovascular risk indicators in an adult population assigned to Primary Care centers in Spain. Subjects > or = 45 years with basal glycemia > or = 90 mg/dl and abdominal circumference > or = 94 cm (men) or > or = 80 cm (women). ATP III modified-criteria were used for the metabolic syndrome and HOMA index > 3.29 was used for insulin resistance. Cardiovascular risk was estimated by the Framingham and SCORE models. A total of 2,341 subjects (62 +/- 10 years; 44.6% males) were included. Frequency of metabolic syndrome and insulin resistance was 54.6% (52.5; 56.8) and 56.6% (54.5; 58.7) respectively. Metabolic syndrome was associated to a higher cardiovascular risk score with both Framingham (16 [15; 16] vs 11 [11; 12] p < 0.0001) and SCORE (2.7 [2.4; 3] vs 2.4 [2.1; 2.8]; p = 0.006) models. The results were similar for the presence of insulin resistance. Metabolic syndrome and insulin resistance are cardiovascular risk predictors. Early identification of metabolic syndrome by the use of simple clinical measures (basal glycemia and waist circumference) would make the intervention on the different disorders of metabolic syndrome possible.
NASA Astrophysics Data System (ADS)
Katsura, K.; Ogata, Y.
2004-12-01
Reasenberg and Jones [Science, 1989, 1994] proposed the aftershock probability forecasting based on the joint distribution [Utsu, J. Fac. Sci. Hokkaido Univ., 1970] of the modified Omori formula of aftershock decay and Gutenberg-Richter law of magnitude frequency, where the respective parameters are estimated by the maximum likelihood method [Ogata, J. Phys. Earth, 1983; Utsu, Geophys Bull. Hokkaido Univ., 1965, Aki, Bull. Earthq. Res. Inst., 1965]. The public forecast has been implemented by the responsible agencies in California and Japan. However, a considerable difficulty in the above procedure is that, due to the contamination of arriving seismic waves, detection rate of aftershocks is extremely low during a period immediately after the main shock, say, during the first day, when the forecasting is most critical for public in the affected area. Therefore, for the forecasting of a probability during such a period, they adopt a generic model with a set of the standard parameter values in California or Japan. For an effective and realistic estimation, I propose to utilize the statistical model introduced by Ogata and Katsura [Geophys. J. Int., 1993] for the simultaneous estimation of the b-values of Gutenberg-Richter law together with detection-rate (probability) of earthquakes of each magnitude-band from the provided data of all detected events, where the both parameters are allowed for changing in time. Thus, by using all detected aftershocks from the beginning of the period, we can estimate the underlying modified Omori rate of both detected and undetected events and their b-value changes, taking the time-varying missing rates of events into account. The similar computation is applied to the ETAS model for complex aftershock activity or regional seismicity where substantial missing events are expected immediately after a large aftershock or another strong earthquake in the vicinity. Demonstrations of the present procedure will be shown for the recent examples
Fonnesbeck, Christopher J; McPheeters, Melissa L; Krishnaswami, Shanthi; Lindegren, Mary Louise; Reimschisel, Tyler
2013-09-01
Though the control of blood phenylalanine (Phe) levels is essential for minimizing impairment in individuals with phenylketonuria (PKU), the empirical basis for the selection of specific blood Phe levels as targets has not been evaluated. We evaluated the current evidence that particular Phe levels are optimal for minimizing or avoiding cognitive impairment in individuals with PKU. This work uses meta-estimates of blood Phe-IQ correlation to predict the probability of low IQ for a range of Phe levels. We believe this metric is easily interpretable by clinicians, and hence useful in making recommendations for Phe intake. The median baseline association of Phe with IQ was estimated to be negative, both in the context of historical (median = -0.026, 95 % BCI = [-0.040, -0.013]) and concurrent (-0.007, [-0.014, 0.000]) measurement of Phe relative to IQ. The estimated additive fixed effect of critical period Phe measurement was also nominally negative for historical measurement (-0.010, [-0.022, 0.003]) and positive for concurrent measurement (0.007, [-0.018, 0.035]). Probabilities corresponding to historical measures of blood Phe demonstrated an increasing chance of low IQ with increasing Phe, with a stronger association seen between blood Phe measured during the critical period than later. In contrast, concurrently-measured Phe was more weakly correlated with the probability of low IQ, though the correlation is still positive, irrespective of whether Phe was measured during the critical or non-critical period. This meta-analysis illustrates the utility of a Bayesian hierarchical approach for not only combining information from a set of candidate studies, but also for combining different types of data to estimate parameters of interest.
NASA Astrophysics Data System (ADS)
Gurov, V. V.
2017-01-01
Software tools for educational purposes, such as e-lessons, computer-based testing system, from the point of view of reliability, have a number of features. The main ones among them are the need to ensure a sufficiently high probability of their faultless operation for a specified time, as well as the impossibility of their rapid recovery by the way of replacing it with a similar running program during the classes. The article considers the peculiarities of reliability evaluation of programs in contrast to assessments of hardware reliability. The basic requirements to reliability of software used for carrying out practical and laboratory classes in the form of computer-based training programs are given. The essential requirements applicable to the reliability of software used for conducting the practical and laboratory studies in the form of computer-based teaching programs are also described. The mathematical tool based on Markov chains, which allows to determine the degree of debugging of the training program for use in the educational process by means of applying the graph of the software modules interaction, is presented.
A physically-based earthquake recurrence model for estimation of long-term earthquake probabilities
Ellsworth, William L.; Matthews, Mark V.; Nadeau, Robert M.; Nishenko, Stuart P.; Reasenberg, Paul A.; Simpson, Robert W.
1999-01-01
A physically-motivated model for earthquake recurrence based on the Brownian relaxation oscillator is introduced. The renewal process defining this point process model can be described by the steady rise of a state variable from the ground state to failure threshold as modulated by Brownian motion. Failure times in this model follow the Brownian passage time (BPT) distribution, which is specified by the mean time to failure, μ, and the aperiodicity of the mean, α (equivalent to the familiar coefficient of variation). Analysis of 37 series of recurrent earthquakes, M -0.7 to 9.2, suggests a provisional generic value of α = 0.5. For this value of α, the hazard function (instantaneous failure rate of survivors) exceeds the mean rate for times > μ⁄2, and is ~ ~ 2 ⁄ μ for all times > μ. Application of this model to the next M 6 earthquake on the San Andreas fault at Parkfield, California suggests that the annual probability of the earthquake is between 1:10 and 1:13.
Haapea, Marianne; Veijola, Juha; Tanskanen, Päivikki; Jääskeläinen, Erika; Isohanni, Matti; Miettunen, Jouko
2011-12-30
Low participation is a potential source of bias in population-based studies. This article presents use of inverse probability weighting (IPW) in adjusting for non-participation in estimation of brain volumes among subjects with schizophrenia. Altogether 101 schizophrenia subjects and 187 non-psychotic comparison subjects belonging to the Northern Finland 1966 Birth Cohort were invited to participate in a field study during 1999-2001. Volumes of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) were compared between the 54 participating schizophrenia subjects and 100 comparison subjects. IPW by illness-related auxiliary variables did not affect the estimated GM and WM mean volumes, but increased the estimated CSF mean volume in schizophrenia subjects. When adjusted for intracranial volume and family history of psychosis, IPW led to smaller estimated GM and WM mean volumes. Especially IPW by a disability pension and a higher amount of hospitalisation due to psychosis had effect on estimated mean brain volumes. The IPW method can be used to improve estimates affected by non-participation by reflecting the true differences in the target population.
Louwe, R. J. W.; Wendling, M.; Herk, M. B. van; Mijnheer, B. J.
2007-04-15
Irradiation of the heart is one of the major concerns during radiotherapy of breast cancer. Three-dimensional (3D) treatment planning would therefore be useful but cannot always be performed for left-sided breast treatments, because CT data may not be available. However, even if 3D dose calculations are available and an estimate of the normal tissue damage can be made, uncertainties in patient positioning may significantly influence the heart dose during treatment. Therefore, 3D reconstruction of the actual heart dose during breast cancer treatment using electronic imaging portal device (EPID) dosimetry has been investigated. A previously described method to reconstruct the dose in the patient from treatment portal images at the radiological midsurface was used in combination with a simple geometrical model of the irradiated heart volume to enable calculation of dose-volume histograms (DVHs), to independently verify this aspect of the treatment without using 3D data from a planning CT scan. To investigate the accuracy of our method, the DVHs obtained with full 3D treatment planning system (TPS) calculations and those obtained after resampling the TPS dose in the radiological midsurface were compared for fifteen breast cancer patients for whom CT data were available. In addition, EPID dosimetry as well as 3D dose calculations using our TPS, film dosimetry, and ionization chamber measurements were performed in an anthropomorphic phantom. It was found that the dose reconstructed using EPID dosimetry and the dose calculated with the TPS agreed within 1.5% in the lung/heart region. The dose-volume histograms obtained with EPID dosimetry were used to estimate the normal tissue complication probability (NTCP) for late excess cardiac mortality. Although the accuracy of these NTCP calculations might be limited due to the uncertainty in the NTCP model, in combination with our portal dosimetry approach it allows incorporation of the actual heart dose. For the anthropomorphic
Choi, Cheol Ung; Park, Chang Gyu
2010-09-08
The purpose of this study was to investigate the pattern of distribution of risk factors for stroke and the 10-year probability of stroke by the Framingham risk score in hypertensive patients visiting private clinics vs. tertiary hospitals. A total of 2,490 hypertensive patients who attended 61 private clinics (1088 patients) and 37 tertiary hospitals (1402 patients) were enrolled. The risk factors for stroke were evaluated using a series of laboratory tests and physical examinations, and the 10-year probability of stroke was determined by applying the Framingham stroke risk equation. The proportion of patients who had uncontrolled hypertension despite the use of antihypertensive agents was 49% (66 and 36% of patients cared for at private clinics and tertiary hospitals, respectively; p < 0.001). The average 10-year probability of stroke by the Framingham risk score in hypertensive patients was 21% (approximately 2.2 times higher than of the risk of stroke in the Korean Cancer Prevention Study [KCPS] cohort) and was higher in patients attending tertiary hospitals compared to private clinics (16 and 24% of patients attending private clinics and tertiary hospitals, respectively; p < 0.001). Since the 10-year probability of stroke by the Framingham risk score in hypertensive patients attending tertiary hospitals was higher than the risk for patients attending private clinics. We suggest that the more aggressive interventions are needed to prevent and early detect an attack of stroke in hypertensive patients attending tertiary hospitals.
Multiple primary tumours: incidence estimation in the presence of competing risks
Rosso, Stefano; Terracini, Lea; Ricceri, Fulvio; Zanetti, Roberto
2009-01-01
Background Estimating the risk of developing subsequent primary tumours in a population is difficult since the occurrence probability is conditioned to the survival probability. Methods We proposed to apply Markov models studying the transition intensities from first to second tumour with the Aalen-Johansen (AJ) estimators, as usually done in competing risk models. In a simulation study we applied the proposed method in different settings with constant or varying underlying intensities and applying age standardisation. In addition, we illustrated the method with data on breast cancer from the Piedmont Cancer Registry. Results The simulation study showed that the person-years approach led to a sensibly wider bias than the AJ estimators. The largest bias was observed assuming constantly increasing incidence rates. However, this situation is rather uncommon dealing with subsequent tumours incidence. In 9233 cases with breast cancer occurred in women resident in Turin, Italy, between 1985 and 1998 we observed a significant increased risk of 1.91 for subsequent cancer of corpus uteri, estimated with the age-standardised Aalen-Johansen incidence ratio (AJ-IRstand), and a significant increased risk of 1.29 for cancer possibly related to the radiotherapy of breast cancer. The peak of occurrence of those cancers was observed after 8 years of follow-up. Conclusion The increased risk of a cancer of the corpus uteri, also observed in other studies, is usually interpreted as the common shared risk factors such as low parity, early menarche and late onset of menopause. We also grouped together those cancers possibly associated to a previous local radiotherapy: the cumulative risk at 14 years is still not significant, however the AJ estimators showed a significant risk peak between the eighth and the ninth year. Finally, the proposed approach has been shown to be reliable and informative under several aspects. It allowed for a correct estimation of the risk, and for investigating
Lu, Dan; Zhang, Guannan; Webster, Clayton G.; Barbier, Charlotte N.
2016-12-30
In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and pra