Explorations in Statistics: Confidence Intervals
ERIC Educational Resources Information Center
Curran-Everett, Douglas
2009-01-01
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This third installment of "Explorations in Statistics" investigates confidence intervals. A confidence interval is a range that we expect, with some level of confidence, to include the true value of a population parameter…
Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes
ERIC Educational Resources Information Center
Thompson, Bruce
2007-01-01
The present article provides a primer on (a) effect sizes, (b) confidence intervals, and (c) confidence intervals for effect sizes. Additionally, various admonitions for reformed statistical practice are presented. For example, a very important implication of the realization that there are dozens of effect size statistics is that "authors must…
Confidence Trick: The Interpretation of Confidence Intervals
ERIC Educational Resources Information Center
Foster, Colin
2014-01-01
The frequent misinterpretation of the nature of confidence intervals by students has been well documented. This article examines the problem as an aspect of the learning of mathematical definitions and considers the tension between parroting mathematically rigorous, but essentially uninternalized, statements on the one hand and expressing…
Teaching Confidence Intervals Using Simulation
ERIC Educational Resources Information Center
Hagtvedt, Reidar; Jones, Gregory Todd; Jones, Kari
2008-01-01
Confidence intervals are difficult to teach, in part because most students appear to believe they understand how to interpret them intuitively. They rarely do. To help them abandon their misconception and achieve understanding, we have developed a simulation tool that encourages experimentation with multiple confidence intervals derived from the…
Minimax confidence intervals in geomagnetism
NASA Technical Reports Server (NTRS)
Stark, Philip B.
1992-01-01
The present paper uses theory of Donoho (1989) to find lower bounds on the lengths of optimally short fixed-length confidence intervals (minimax confidence intervals) for Gauss coefficients of the field of degree 1-12 using the heat flow constraint. The bounds on optimal minimax intervals are about 40 percent shorter than Backus' intervals: no procedure for producing fixed-length confidence intervals, linear or nonlinear, can give intervals shorter than about 60 percent the length of Backus' in this problem. While both methods rigorously account for the fact that core field models are infinite-dimensional, the application of the techniques to the geomagnetic problem involves approximations and counterfactual assumptions about the data errors, and so these results are likely to be extremely optimistic estimates of the actual uncertainty in Gauss coefficients.
Interpretation of Confidence Interval Facing the Conflict
ERIC Educational Resources Information Center
Andrade, Luisa; Fernández, Felipe
2016-01-01
As literature has reported, it is usual that university students in statistics courses, and even statistics teachers, interpret the confidence level associated with a confidence interval as the probability that the parameter value will be between the lower and upper interval limits. To confront this misconception, class activities have been…
Confidence Interval Procedures for Reliability Growth Analysis
1977-06-01
Plj2s tSAA - TECHNICAL RPORT NO. 197 CONFIDENCE INTERVAL PROCEDURES FOR RELIABILITY, GROWTH ANALYSIS LARRY H. CROW JUNE 1977 APPROVED FOR PUBLIC...dence Intervals for M(T). ¶-. fl [ ] 1 Siion IIS0III0N/AVAI Ale ITY ClOtS Next page is blank. So3 CONFIDENCE INTERVAL PROCIEDURIS• FOR RELTABILITY...and confidence interval procedures for the parameters B and P = X are presented in [l , [2], [4]. In the application of the Weibull process model to
ERIC Educational Resources Information Center
Du, Yunfei
This paper discusses the impact of sampling error on the construction of confidence intervals around effect sizes. Sampling error affects the location and precision of confidence intervals. Meta-analytic resampling demonstrates that confidence intervals can haphazardly bounce around the true population parameter. Special software with graphical…
Code of Federal Regulations, 2012 CFR
2012-07-01
... section 2, the specific DQO criterion is that the width of the two-sided 95 percent confidence interval of... average measured CE value to the endpoints of the 95-percent (two-sided) confidence interval for the... measured CE value to the endpoints of the 95-percent (two-sided) confidence interval, expressed as...
Code of Federal Regulations, 2014 CFR
2014-07-01
... section 2, the specific DQO criterion is that the width of the two-sided 95 percent confidence interval of... average measured CE value to the endpoints of the 95-percent (two-sided) confidence interval for the... measured CE value to the endpoints of the 95-percent (two-sided) confidence interval, expressed as...
Efficient Computation Of Confidence Intervals Of Parameters
NASA Technical Reports Server (NTRS)
Murphy, Patrick C.
1992-01-01
Study focuses on obtaining efficient algorithm for estimation of confidence intervals of ML estimates. Four algorithms selected to solve associated constrained optimization problem. Hybrid algorithms, following search and gradient approaches, prove best.
Asymptotic Theory for Nonparametric Confidence Intervals.
1982-07-01
distributions. Ann. Math Statist. 14, 56-62. 24. ROY, S.N. and POTTHOFF, R.F. (1958). Confidence bounds on vector analogues of the "ratio of the mean" and...fl c,~_________ 14L TITLE feed &MV) S. TYPE or REPORT a PeftOo COVx:REC Asympeocic Theory for Nonaparuetric Technical Report Confidence Intevals 6...S..C-0S78 UNCLASSIFIED TŗU *uuuuumuuumhhhhmhhhm_4 ASYMPTOTIC THEORY FOR NONPARAMETRIC CONFIDENCE INTERVALS by Peter W. Glynn TECHNICAL REPORT NO. 63
Coefficient Alpha Bootstrap Confidence Interval under Nonnormality
ERIC Educational Resources Information Center
Padilla, Miguel A.; Divers, Jasmin; Newton, Matthew
2012-01-01
Three different bootstrap methods for estimating confidence intervals (CIs) for coefficient alpha were investigated. In addition, the bootstrap methods were compared with the most promising coefficient alpha CI estimation methods reported in the literature. The CI methods were assessed through a Monte Carlo simulation utilizing conditions…
Coefficient Omega Bootstrap Confidence Intervals: Nonnormal Distributions
ERIC Educational Resources Information Center
Padilla, Miguel A.; Divers, Jasmin
2013-01-01
The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items.…
Toward Using Confidence Intervals to Compare Correlations
ERIC Educational Resources Information Center
Zou, Guang Yong
2007-01-01
Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate…
[Normal confidence interval for a summary measure].
Bernard, P M
2000-10-01
This paper proposes an approach for calculating the normal confidence interval of a weighted summary measure which requires a particular continuous transformation for its variance estimation. By using the transformation properties and applying the delta method, the variance of transformed measure is easily expressed in terms of the transformed specific measure variances and the squared weights. The confidence limits of the summary measure are easily deduced by inverse transformation of those of transformed measure. The method is illustrated by applying it to some well known epidemiological measures. It seems appropriate for application in stratified analysis context where size allows normal approximation.
Efficient computation of parameter confidence intervals
NASA Technical Reports Server (NTRS)
Murphy, Patrick C.
1987-01-01
An important step in system identification of aircraft is the estimation of stability and control derivatives from flight data along with an assessment of parameter accuracy. When the maximum likelihood estimation technique is used, parameter accuracy is commonly assessed by the Cramer-Rao lower bound. It is known, however, that in some cases the lower bound can be substantially different from the parameter variance. Under these circumstances the Cramer-Rao bounds may be misleading as an accuracy measure. This paper discusses the confidence interval estimation problem based on likelihood ratios, which offers a more general estimate of the error bounds. Four approaches are considered for computing confidence intervals of maximum likelihood parameter estimates. Each approach is applied to real flight data and compared.
A primer on confidence intervals in psychopharmacology.
Andrade, Chittaranjan
2015-02-01
Research papers and research summaries frequently present results in the form of data accompanied by 95% confidence intervals (CIs). Not all students and clinicians know how to interpret CIs. This article provides a nontechnical, nonmathematical discussion on how to understand and glean information from CIs; all explanations are accompanied by simple examples. A statistically accurate explanation about CIs is also provided. CIs are differentiated from standard deviations, standard errors, and confidence levels. The interpretation of narrow and wide CIs is discussed. Factors that influence the width of a CI are listed. Explanations are provided for how CIs can be used to assess statistical significance. The significance of overlapping and nonoverlapping CIs is considered. It is concluded that CIs are far more informative than, say, mere P values when drawing conclusions about a result.
A Robust Confidence Interval for Samples of Five Observations.
1979-11-01
A robust confidence interval using biweights for the case of five observations is proposed when the underlying distribution has somewhat heavier...probabilities, the intervals proposed are highly efficient, in terms of the expected length of the confidence interval . (Author)
IET. Aerial view of project, 95 percent complete. Camera facing ...
IET. Aerial view of project, 95 percent complete. Camera facing east. Left to right: stack, duct, mobile test cell building (TAN-624), four-rail track, dolly. Retaining wall between mobile test building and shielded control building (TAN-620) just beyond. North of control building are tank building (TAN-627) and fuel-transfer pump building (TAN-625). Guard house at upper right along exclusion fence. Construction vehicles and temporary warehouse in view near guard house. Date: June 6, 1955. INEEL negative no. 55-1462 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID
How do I interpret a confidence interval?
O'Brien, Sheila F; Yi, Qi Long
2016-07-01
A 95% confidence interval (CI) of the mean is a range with an upper and lower number calculated from a sample. Because the true population mean is unknown, this range describes possible values that the mean could be. If multiple samples were drawn from the same population and a 95% CI calculated for each sample, we would expect the population mean to be found within 95% of these CIs. CIs are sensitive to variability in the population (spread of values) and sample size. When used to compare the means of two or more treatment groups, a CI shows the magnitude of a difference between groups. This is helpful in understanding both the statistical significance and the clinical significance of a treatment. In this article we describe the basic principles of CIs and their interpretation.
ERIC Educational Resources Information Center
Hoekstra, Rink; Johnson, Addie; Kiers, Henk A. L.
2012-01-01
The use of confidence intervals (CIs) as an addition or as an alternative to null hypothesis significance testing (NHST) has been promoted as a means to make researchers more aware of the uncertainty that is inherent in statistical inference. Little is known, however, about whether presenting results via CIs affects how readers judge the…
A METHOD OF DETERMINING A CONFIDENCE INTERVAL FOR AVAILABILITY
This report presents a method of determining a confidence interval for availability when it is estimated from the mean time between equipment...for a confidence interval for availability. An example is included to demonstrate the procedure of placing a confidence interval about the estimated availability.
Confidence Interval Methodology for Ratio Means (CIM4RM)
2010-08-01
RDECOIW 40 Years of SAA Excellence in Analysis AMSAA TECHNICAL REPORT NO. TR-2010-35 CONFIDENCE INTERVAL METHODOLOGY FOR RATIO MEANS (CIM4RM...COVERED Technical Report 4 TITLE AND SUBTITLE Confidence Interval Methodology for Ratio Means (CIM4RM) 5 FUNDING NUMBERS 6 AUTHOR!SI John Nierwinski...LIST OF ACRONYMS CIM4RM - Confidence Interval Methodology for Ratio Means MH - Man-Hours MR - Maintenance Ratio PCM - Parts Cost per Mile CI
Contrasting Diversity Values: Statistical Inferences Based on Overlapping Confidence Intervals
MacGregor-Fors, Ian; Payton, Mark E.
2013-01-01
Ecologists often contrast diversity (species richness and abundances) using tests for comparing means or indices. However, many popular software applications do not support performing standard inferential statistics for estimates of species richness and/or density. In this study we simulated the behavior of asymmetric log-normal confidence intervals and determined an interval level that mimics statistical tests with P(α) = 0.05 when confidence intervals from two distributions do not overlap. Our results show that 84% confidence intervals robustly mimic 0.05 statistical tests for asymmetric confidence intervals, as has been demonstrated for symmetric ones in the past. Finally, we provide detailed user-guides for calculating 84% confidence intervals in two of the most robust and highly-used freeware related to diversity measurements for wildlife (i.e., EstimateS, Distance). PMID:23437239
Contrasting diversity values: statistical inferences based on overlapping confidence intervals.
MacGregor-Fors, Ian; Payton, Mark E
2013-01-01
Ecologists often contrast diversity (species richness and abundances) using tests for comparing means or indices. However, many popular software applications do not support performing standard inferential statistics for estimates of species richness and/or density. In this study we simulated the behavior of asymmetric log-normal confidence intervals and determined an interval level that mimics statistical tests with P(α) = 0.05 when confidence intervals from two distributions do not overlap. Our results show that 84% confidence intervals robustly mimic 0.05 statistical tests for asymmetric confidence intervals, as has been demonstrated for symmetric ones in the past. Finally, we provide detailed user-guides for calculating 84% confidence intervals in two of the most robust and highly-used freeware related to diversity measurements for wildlife (i.e., EstimateS, Distance).
A Note on Confidence Interval Estimation and Margin of Error
ERIC Educational Resources Information Center
Gilliland, Dennis; Melfi, Vince
2010-01-01
Confidence interval estimation is a fundamental technique in statistical inference. Margin of error is used to delimit the error in estimation. Dispelling misinterpretations that teachers and students give to these terms is important. In this note, we give examples of the confusion that can arise in regard to confidence interval estimation and…
Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling
ERIC Educational Resources Information Center
Banjanovic, Erin S.; Osborne, Jason W.
2016-01-01
Confidence intervals for effect sizes (CIES) provide readers with an estimate of the strength of a reported statistic as well as the relative precision of the point estimate. These statistics offer more information and context than null hypothesis statistic testing. Although confidence intervals have been recommended by scholars for many years,…
Lower Confidence Interval Bounds for Coherent Systems with Cyclic Components
1990-09-01
Three lower confidence interval estimation procedures for system reliability of coherent systems with cyclic components are developed and their...failure times and applied to yield a lower confidence interval procedures for the reliability of coherent systems with cyclic and continuously operating components.
New Confidence Interval Estimators Using Standardized Time Series.
1984-12-01
We develop new confidence interval estimators for the underlying mean of a stationary simulation process. These estimators can be viewed as...generalizations of Schruben’s so-called standardized time series area confidence interval estimators. Various properties of the new estimators are given.
Sample Size for the "Z" Test and Its Confidence Interval
ERIC Educational Resources Information Center
Liu, Xiaofeng Steven
2012-01-01
The statistical power of a significance test is closely related to the length of the confidence interval (i.e. estimate precision). In the case of a "Z" test, the length of the confidence interval can be expressed as a function of the statistical power. (Contains 1 figure and 1 table.)
Reporting Confidence Intervals and Effect Sizes: Collecting the Evidence
ERIC Educational Resources Information Center
Zientek, Linda Reichwein; Ozel, Z. Ebrar Yetkiner; Ozel, Serkan; Allen, Jeff
2012-01-01
Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…
Corrected profile likelihood confidence interval for binomial paired incomplete data.
Pradhan, Vivek; Menon, Sandeep; Das, Ujjwal
2013-01-01
Clinical trials often use paired binomial data as their clinical endpoint. The confidence interval is frequently used to estimate the treatment performance. Tang et al. (2009) have proposed exact and approximate unconditional methods for constructing a confidence interval in the presence of incomplete paired binary data. The approach proposed by Tang et al. can be overly conservative with large expected confidence interval width (ECIW) in some situations. We propose a profile likelihood-based method with a Jeffreys' prior correction to construct the confidence interval. This approach generates confidence interval with a much better coverage probability and shorter ECIWs. The performances of the method along with the corrections are demonstrated through extensive simulation. Finally, three real world data sets are analyzed by all the methods. Statistical Analysis System (SAS) codes to execute the profile likelihood-based methods are also presented.
Alternative Confidence Interval Methods Used in the Diagnostic Accuracy Studies.
Erdoğan, Semra; Gülhan, Orekıcı Temel
2016-01-01
Background/Aim. It is necessary to decide whether the newly improved methods are better than the standard or reference test or not. To decide whether the new diagnostics test is better than the gold standard test/imperfect standard test, the differences of estimated sensitivity/specificity are calculated with the help of information obtained from samples. However, to generalize this value to the population, it should be given with the confidence intervals. The aim of this study is to evaluate the confidence interval methods developed for the differences between the two dependent sensitivity/specificity values on a clinical application. Materials and Methods. In this study, confidence interval methods like Asymptotic Intervals, Conditional Intervals, Unconditional Interval, Score Intervals, and Nonparametric Methods Based on Relative Effects Intervals are used. Besides, as clinical application, data used in diagnostics study by Dickel et al. (2010) has been taken as a sample. Results. The results belonging to the alternative confidence interval methods for Nickel Sulfate, Potassium Dichromate, and Lanolin Alcohol are given as a table. Conclusion. While preferring the confidence interval methods, the researchers have to consider whether the case to be compared is single ratio or dependent binary ratio differences, the correlation coefficient between the rates in two dependent ratios and the sample sizes.
Small Sample Theory for Steady State Confidence Intervals
1989-06-01
confidence interval for the mean of a stationary sequence. As indicated in the literature, nonparametric confidence intervals in practice often have undesirable small-sample asymmetry and coverage characteristics. These phenomena are partially due to the fact that the third and fourth cumulants of the point estimator for the stationary mean, unlike those of the standard normal random variable, are not zero. We will apply Edgeworth and Cornish-Fisher expansions to obtain asymptotic expansions for the errors associated with confidence intervals. The analysis isolates various
Confidence Intervals for Error Rates Observed in Coded Communications Systems
NASA Astrophysics Data System (ADS)
Hamkins, J.
2015-05-01
We present methods to compute confidence intervals for the codeword error rate (CWER) and bit error rate (BER) of a coded communications link. We review several methods to compute exact and approximate confidence intervals for the CWER, and specifically consider the situation in which the true CWER is so low that only a handful, if any, codeword errors are able to be simulated. In doing so, we answer the question of how long an error-free simulation must be run in order to certify that a given CWER requirement is met with a given level of confidence, and discuss the bias introduced by aborting a simulation after observing the first codeword error. Next, we turn to the lesser studied problem of determining confidence intervals for the BER of coded systems. Since bit errors in systems that use coding or higher-order modulation do not occur independently, blind application of a method that assumes independence leads to inappropriately narrow confidence intervals. We present a new method to compute the confidence interval properly, using the first and second sample moments of the number of bit errors per codeword. This is the first method we know of to compute a confidence interval for the BER of a coded or higher-order modulation system.
Confidence Interval for Parameter n in a Binomial Distribution.
1987-01-01
procedure of estimating n in the form of a confidence interval . The last section consists of some concluding remark. A simulation procedure, an interactive computer program, and selected tables are included in the appendixes.
Prediction of the confidence interval of quantitative trait Loci location.
Visscher, Peter M; Goddard, Mike E
2004-07-01
In 1997, Darvasi and Soller presented empirical predictions of the confidence interval of quantitative trait loci (QTL) location for dense marker maps in experimental crosses. They showed from simulation results for backcross and F2 populations from inbred lines that the 95% confidence interval was a simple function of sample size and the effect of the QTL. In this study, we derive by theory simple equations that can be used to predict any confidence interval and show that for the 95% confidence interval, they are in good agreement with the empirical results given by Darvasi and Soller. A general form of the confidence interval is given that also applies to other population structures (e.g., collections of sib pairs). Furthermore, the expected shape of the likelihood-ratio-test around the true QTL location is derived, which is shown to be extremely leptokurtic. It is shown that this shape explains why confidence intervals from the Log of Odds (LOD) drop-off method and bootstrap results frequently differ for real data sets.
NASA Astrophysics Data System (ADS)
Lu, Dan; Ye, Ming; Hill, Mary C.
2012-09-01
Confidence intervals based on classical regression theories augmented to include prior information and credible intervals based on Bayesian theories are conceptually different ways to quantify parametric and predictive uncertainties. Because both confidence and credible intervals are used in environmental modeling, we seek to understand their differences and similarities. This is of interest in part because calculating confidence intervals typically requires tens to thousands of model runs, while Bayesian credible intervals typically require tens of thousands to millions of model runs. Given multi-Gaussian distributed observation errors, our theoretical analysis shows that, for linear or linearized-nonlinear models, confidence and credible intervals are always numerically identical when consistent prior information is used. For nonlinear models, nonlinear confidence and credible intervals can be numerically identical if parameter confidence regions defined using the approximate likelihood method and parameter credible regions estimated using Markov chain Monte Carlo realizations are numerically identical and predictions are a smooth, monotonic function of the parameters. Both occur if intrinsic model nonlinearity is small. While the conditions of Gaussian errors and small intrinsic model nonlinearity are violated by many environmental models, heuristic tests using analytical and numerical models suggest that linear and nonlinear confidence intervals can be useful approximations of uncertainty even under significantly nonideal conditions. In the context of epistemic model error for a complex synthetic nonlinear groundwater problem, the linear and nonlinear confidence and credible intervals for individual models performed similarly enough to indicate that the computationally frugal confidence intervals can be useful in many circumstances. Experiences with these groundwater models are expected to be broadly applicable to many environmental models. We suggest that for
Carkeet, Andrew; Goh, Yee Teng
2016-09-01
Bland and Altman described approximate methods in 1986 and 1999 for calculating confidence limits for their 95% limits of agreement, approximations which assume large subject numbers. In this paper, these approximations are compared with exact confidence intervals calculated using two-sided tolerance intervals for a normal distribution. The approximations are compared in terms of the tolerance factors themselves but also in terms of the exact confidence limits and the exact limits of agreement coverage corresponding to the approximate confidence interval methods. Using similar methods the 50th percentile of the tolerance interval are compared with the k values of 1.96 and 2, which Bland and Altman used to define limits of agreements (i.e. [Formula: see text]+/- 1.96Sd and [Formula: see text]+/- 2Sd). For limits of agreement outer confidence intervals, Bland and Altman's approximations are too permissive for sample sizes <40 (1999 approximation) and <76 (1986 approximation). For inner confidence limits the approximations are poorer, being permissive for sample sizes of <490 (1986 approximation) and all practical sample sizes (1999 approximation). Exact confidence intervals for 95% limits of agreements, based on two-sided tolerance factors, can be calculated easily based on tables and should be used in preference to the approximate methods, especially for small sample sizes.
Confidence intervals for effect parameters common in cancer epidemiology.
Sato, T
1990-01-01
This paper reviews approximate confidence intervals for some effect parameters common in cancer epidemiology. These methods have computational feasibility and give nearly nominal coverage rates. In the analysis of crude data, the simplest type of epidemiologic analysis, parameters of interest are the odds ratio in case-control studies and the rate ratio and difference in cohort studies. These parameters can estimate the instantaneous-incidence-rate ratio and difference that are the most meaningful effect measures in cancer epidemiology. Approximate confidence intervals for these parameters including the classical Cornfield's method are mainly based on efficient scores. When some confounding factors exist, stratified analysis and summary measures for effect parameters are needed. Since the Mantel-Haenszel estimators have been widely used by epidemiologists as summary measures, confidence intervals based on the Mantel-Haenszel estimators are described. The paper also discusses recent developments in these methods. PMID:2269246
Confidence intervals for low-level, paired counting
Potter, W.E.
1999-11-01
Fong and Alvarez (1997) make clear the lack of precision at MDA for paired counting. Confidence intervals provide a way of expressing a measurement process that lacks precision. Neyman-Pearson principles are briefly discussed and 95% confidence intervals of the form [0, {number_sign}{number_sign}.{number_sign}{number_sign}] are presented. Use is made of the fact that the probability of the difference of two random variables, each with a Poisson distribution, can be expressed in terms of modified Bessel functions of integral order and elementary functions. The validity of the values is discussed.
Mortality rate and confidence interval estimation in humanitarian emergencies.
Sullivan, Kevin; Hossain, S M Moazzem; Woodruff, Bradley A
2010-01-01
Surveys are conducted frequently in humanitarian emergencies to assess the health status of the population. Most often, they employ complex sample designs, such as cluster sampling. Mortality is an indicator commonly estimated in such surveys. Confidence limits provide information on the precision of the estimate and it is important to ensure that confidence limits for a mortality rate account for the survey design and utilise an acceptable methodology. This paper describes the calculation of confidence limits for mortality rates from surveys using complex sampling designs and a variety of software programmes and methods. It contains an example that makes use of the SAS, SPSS, and Epi Info software programmes. Of the three confidence interval methods examined--the ratio command approach, the modified rate approach, and the modified proportion approach--the paper recommends the ratio command approach to estimate mortality rates with confidence limits.
Likelihood-Based Confidence Intervals in Exploratory Factor Analysis
ERIC Educational Resources Information Center
Oort, Frans J.
2011-01-01
In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by…
Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Wagler, Amy E.
2014-01-01
Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…
Researchers Misunderstand Confidence Intervals and Standard Error Bars
ERIC Educational Resources Information Center
Belia, Sarah; Fidler, Fiona; Williams, Jennifer; Cumming, Geoff
2005-01-01
Little is known about researchers' understanding of confidence intervals (CIs) and standard error (SE) bars. Authors of journal articles in psychology, behavioral neuroscience, and medicine were invited to visit a Web site where they adjusted a figure until they judged 2 means, with error bars, to be just statistically significantly different (p…
Constructing Approximate Confidence Intervals for Parameters with Structural Equation Models
ERIC Educational Resources Information Center
Cheung, Mike W. -L.
2009-01-01
Confidence intervals (CIs) for parameters are usually constructed based on the estimated standard errors. These are known as Wald CIs. This article argues that likelihood-based CIs (CIs based on likelihood ratio statistics) are often preferred to Wald CIs. It shows how the likelihood-based CIs and the Wald CIs for many statistics and psychometric…
ERIC Educational Resources Information Center
Kelley, Ken
2005-01-01
The standardized group mean difference, Cohen's "d", is among the most commonly used and intuitively appealing effect sizes for group comparisons. However, reporting this point estimate alone does not reflect the extent to which sampling error may have led to an obtained value. A confidence interval expresses the uncertainty that exists between…
Some Improvements in Confidence Intervals for Standardized Regression Coefficients.
Dudgeon, Paul
2017-03-13
Yuan and Chan (Psychometrika 76:670-690, 2011. doi: 10.1007/S11336-011-9224-6 ) derived consistent confidence intervals for standardized regression coefficients under fixed and random score assumptions. Jones and Waller (Psychometrika 80:365-378, 2015. doi: 10.1007/S11336-013-9380-Y ) extended these developments to circumstances where data are non-normal by examining confidence intervals based on Browne's (Br J Math Stat Psychol 37:62-83, 1984. doi: 10.1111/j.2044-8317.1984.tb00789.x ) asymptotic distribution-free (ADF) theory. Seven different heteroscedastic-consistent (HC) estimators were investigated in the current study as potentially better solutions for constructing confidence intervals on standardized regression coefficients under non-normality. Normal theory, ADF, and HC estimators were evaluated in a Monte Carlo simulation. Findings confirmed the superiority of the HC3 (MacKinnon and White, J Econ 35:305-325, 1985. doi: 10.1016/0304-4076(85)90158-7 ) and HC5 (Cribari-Neto and Da Silva, Adv Stat Anal 95:129-146, 2011. doi: 10.1007/s10182-010-0141-2 ) interval estimators over Jones and Waller's ADF estimator under all conditions investigated, as well as over the normal theory method. The HC5 estimator was more robust in a restricted set of conditions over the HC3 estimator. Some possible extensions of HC estimators to other effect size measures are considered for future developments.
Quantifying uncertainty on sediment loads using bootstrap confidence intervals
NASA Astrophysics Data System (ADS)
Slaets, Johanna I. F.; Piepho, Hans-Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg
2017-01-01
Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While statistical models used to predict constituent concentrations have been developed considerably over the last few years, measures of uncertainty on constituent loads are rarely reported. Loads are the product of two predictions, constituent concentration and discharge, integrated over a time period, which does not make it straightforward to produce a standard error or a confidence interval. In this paper, a linear mixed model is used to estimate sediment concentrations. A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations are required. The method was tested for a small watershed in Northwest Vietnam for the period 2010-2011. The results showed that confidence intervals were asymmetric, with the highest uncertainty in the upper limit, and that a load of 6262 Mg year-1 had a 95 % confidence interval of (4331, 12 267) in 2010 and a load of 5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach demonstrated that direct estimates from the data were biased downwards compared to bootstrap median estimates. These results imply that constituent loads predicted from regression-type water quality models could frequently be underestimating sediment yields and their environmental impact.
Confidence intervals in Flow Forecasting by using artificial neural networks
NASA Astrophysics Data System (ADS)
Panagoulia, Dionysia; Tsekouras, George
2014-05-01
One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input
Recommended tests and confidence intervals for paired binomial proportions.
Fagerland, Morten W; Lydersen, Stian; Laake, Petter
2014-07-20
We describe, evaluate, and recommend statistical methods for the analysis of paired binomial proportions. A total of 24 methods are considered. The best tests for association include the asymptotic McNemar test and the McNemar mid- p test. For the difference between proportions, we recommend two simple confidence intervals with closed-form expressions and the asymptotic score interval. The asymptotic score interval is also recommended for the ratio of proportions, as is an interval with closed-form expression based on combining two Wilson score intervals for the single proportion. For the odds ratio, we recommend a transformation of the Wilson score interval and a transformation of the Clopper-Pearson mid- p interval. We illustrate the practical application of the methods using data from a recently published study of airway reactivity in children before and after stem cell transplantation and a matched case-control study of the association between floppy eyelid syndrome and obstructive sleep apnea-hypopnea syndrome.
Empirical Likelihood-Based Confidence Interval of ROC Curves.
Su, Haiyan; Qin, Yongsong; Liang, Hua
2009-11-01
In this article we propose an empirical likelihood-based confidence interval for receiver operating characteristic curves which are based on a continuous-scale test. The approach is easily understood, simply implemented, and computationally efficient. The results from our simulation studies indicate that the finite-sample numerical performance slightly outperforms the most promising methods published recently. Two real datasets are analyzed by using the proposed method and the existing bootstrap-based method.
Confidence intervals for expected moments algorithm flood quantile estimates
Cohn, T.A.; Lane, W.L.; Stedinger, J.R.
2001-01-01
Historical and paleoflood information can substantially improve flood frequency estimates if appropriate statistical procedures are properly applied. However, the Federal guidelines for flood frequency analysis, set forth in Bulletin 17B, rely on an inefficient "weighting" procedure that fails to take advantage of historical and paleoflood information. This has led researchers to propose several more efficient alternatives including the Expected Moments Algorithm (EMA), which is attractive because it retains Bulletin 17B's statistical structure (method of moments with the Log Pearson Type 3 distribution) and thus can be easily integrated into flood analyses employing the rest of the Bulletin 17B approach. The practical utility of EMA, however, has been limited because no closed-form method has been available for quantifying the uncertainty of EMA-based flood quantile estimates. This paper addresses that concern by providing analytical expressions for the asymptotic variance of EMA flood-quantile estimators and confidence intervals for flood quantile estimates. Monte Carlo simulations demonstrate the properties of such confidence intervals for sites where a 25- to 100-year streamgage record is augmented by 50 to 150 years of historical information. The experiments show that the confidence intervals, though not exact, should be acceptable for most purposes.
Technology Transfer Automated Retrieval System (TEKTRAN)
Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL (quantitative trait loci) realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs). Support interval (SI) meth...
On Some Confidence Intervals for Estimating the Mean of a Skewed Population
ERIC Educational Resources Information Center
Shi, W.; Kibria, B. M. Golam
2007-01-01
A number of methods are available in the literature to measure confidence intervals. Here, confidence intervals for estimating the population mean of a skewed distribution are considered. This note proposes two alternative confidence intervals, namely, Median t and Mad t, which are simple adjustments to the Student's t confidence interval. In…
Confidence interval construction for proportion ratio in paired studies based on hybrid method.
Tang, Man-Lai; Li, Hui-Qiong; Tang, Nian-Sheng
2012-08-01
In this article, we consider confidence interval construction for proportion ratio in paired samples. Previous studies usually reported that score-based confidence intervals consistently outperformed other asymptotic confidence intervals for correlated proportion difference and ratio. However, score-based confidence intervals may not possess closed-form solutions and iterative procedures are therefore required. This article investigates the problem of confidence interval construction for ratio of two correlated proportions based on a hybrid method. Briefly, the hybrid method simply combines two separate confidence intervals for two individual proportions to produce a hybrid confidence interval for the ratio of the two individual proportions in paired studies. Most importantly, confidence intervals based on this hybrid method possess explicit solutions. Our simulation studies indicate that hybrid Wilson score confidence intervals based on Fieller's theorem performs well. The proposed confidence intervals will be illustrated with three real examples.
Covariate-adjusted confidence interval for the intraclass correlation coefficient.
Shoukri, Mohamed M; Donner, Allan; El-Dali, Abdelmoneim
2013-09-01
A crucial step in designing a new study is to estimate the required sample size. For a design involving cluster sampling, the appropriate sample size depends on the so-called design effect, which is a function of the average cluster size and the intracluster correlation coefficient (ICC). It is well-known that under the framework of hierarchical and generalized linear models, a reduction in residual error may be achieved by including risk factors as covariates. In this paper we show that the covariate design, indicating whether the covariates are measured at the cluster level or at the within-cluster subject level affects the estimation of the ICC, and hence the design effect. Therefore, the distinction between these two types of covariates should be made at the design stage. In this paper we use the nested-bootstrap method to assess the accuracy of the estimated ICC for continuous and binary response variables under different covariate structures. The codes of two SAS macros are made available by the authors for interested readers to facilitate the construction of confidence intervals for the ICC. Moreover, using Monte Carlo simulations we evaluate the relative efficiency of the estimators and evaluate the accuracy of the coverage probabilities of a 95% confidence interval on the population ICC. The methodology is illustrated using a published data set of blood pressure measurements taken on family members.
Comparing Simultaneous and Pointwise Confidence Intervals for Hydrological Processes.
Francisco-Fernández, Mario; Quintela-del-Río, Alejandro
2016-01-01
Distribution function estimation of the random variable of river flow is an important problem in hydrology. This issue is directly related to quantile estimation, and consequently to return level prediction. The estimation process can be complemented with the construction of confidence intervals (CIs) to perform a probabilistic assessment of the different variables and/or estimated functions. In this work, several methods for constructing CIs using bootstrap techniques, and parametric and nonparametric procedures in the estimation process are studied and compared. In the case that the target is the joint estimation of a vector of values, some new corrections to obtain joint coverage probabilities closer to the corresponding nominal values are also presented. A comprehensive simulation study compares the different approaches, and the application of the different procedures to real data sets from four rivers in the United States and one in Spain complete the paper.
Probability distributions and confidence intervals for simulated power law noise.
Ashby, Neil
2015-01-01
A method for simulating power law noise in clocks and oscillators is presented based on modification of the spectrum of white phase noise, then Fourier transforming to the time domain. Symmetric real matrices are introduced whose traces-the sums of their eigenvalues-are equal to the Allan variances, in overlapping or non-overlapping forms, as well as for the corresponding forms of the modified Allan variance. We show that the standard expressions for spectral densities, and their relations to Allan variance, are obtained with this method. The matrix eigenvalues determine probability distributions for observing a variance at an arbitrary value of the sampling interval τ, and hence for estimating confidence in the measurements. Examples are presented for the common power-law noises. Extension to other variances such as the Hadamard variance, and variances with dead time, are discussed.
Comparing Simultaneous and Pointwise Confidence Intervals for Hydrological Processes
2016-01-01
Distribution function estimation of the random variable of river flow is an important problem in hydrology. This issue is directly related to quantile estimation, and consequently to return level prediction. The estimation process can be complemented with the construction of confidence intervals (CIs) to perform a probabilistic assessment of the different variables and/or estimated functions. In this work, several methods for constructing CIs using bootstrap techniques, and parametric and nonparametric procedures in the estimation process are studied and compared. In the case that the target is the joint estimation of a vector of values, some new corrections to obtain joint coverage probabilities closer to the corresponding nominal values are also presented. A comprehensive simulation study compares the different approaches, and the application of the different procedures to real data sets from four rivers in the United States and one in Spain complete the paper. PMID:26828651
A comparison of several methods for the confidence intervals of negative binomial proportions
NASA Astrophysics Data System (ADS)
Thong, Alfred Lim Sheng; Shan, Fam Pei
2015-12-01
This study focuses on the comparison of the performances of several approaches in constructing confidence interval of negative binomial proportions (single negative binomial proportion and the difference between two negative binomial proportions). After that, the strengths and weaknesses of the approaches in constructing confidence interval of negative binomial proportions are figured out. Performances of the approaches will be accessed by comparing their coverage probabilities and average lengths of confidence intervals. For the comparison of the performances of the approaches in single negative binomial proportion, Wald confidence interval (WCI-I), Agresti confidence interval (ACI-I), Wilson's Score confidence interval (WSCI-I) and Jeffrey confidence interval (JCI-I) are used. WSCI-I is the better approach for single negative binomial proportion in term of the average length of confidence intervals and average coverage probability. While for the comparison of the performances of the approaches in the difference between two negative binomial proportions, Wald confidence interval (WCI-II), Agresti confidence interval (ACI-II), Newcombe's Score confidence interval (NSCI-II), Jeffrey confidence interval (JCI-II) and Yule confidence interval (YCI-II) are used. Under different situations, a better approach has been discussed and recommended. There will be different approach that performs better for the coverage probability.
Love, Tanzy M.
2010-01-01
Background Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods. Results Based on a non-parametric confidence interval (NPCI) method designed for use with the Haley-Knott regression method for mapping QTL, a CIM-specific method (CIM-NPCI) was developed to appropriately account for the selection of background markers during analysis of bootstrap-resampled data sets. Coverage probabilities and interval widths resulting from use of the NPCI, SI, and CIM-NPCI methods were compared in a series of simulations analyzed via CIM, wherein four genetic effects were simulated in chromosomal regions with distinct marker densities while heritability was fixed at 0.6 for a population of 200 isolines. CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies. The effects of a known CIM bias toward locating QTL peaks at markers were also investigated for each marker density case. Evaluation of sub-simulations that varied according to the positions of simulated effects relative to the nearest markers showed that the CIM-NPCI method overcomes this bias, offering an explanation for the improved coverage probabilities when marker densities are high. Conclusions Extensive simulation studies herein demonstrate that the QTL confidence interval methods typically used to positionally evaluate CIM results can be dramatically improved
Error Modeling and Confidence Interval Estimation for Inductively Coupled Plasma Calibration Curves.
1987-02-01
confidence interval estimation for multiple use of the calibration curve is...calculate weights for the calibration curve fit. Multiple and single-use confidence interval estimates are obtained and results along the calibration curve are
ERIC Educational Resources Information Center
Algina, James; Moulder, Bradley C.
2001-01-01
Studied sample sizes for confidence intervals on the increase in the squared multiple correlation coefficient using simulation. Discusses predictors and actual coverage probability and provides sample-size guidelines for probability coverage to be near the nominal confidence interval. (SLD)
Strong Confidence Intervals: A Compromise between the Gaussian and the Slash.
1983-11-01
In this report we define strong confidence interval procedures and discuss their properties. Strong confidence means that the reported confidence level is achieved even conditioned on configurations. Furthermore this is true for both the Gaussian and the slash sampling situations. We will show how such a procedure can be obtained and compare its performance to some popular non-parametric confidence intervals. (Author)
Introduction to Sample Size Choice for Confidence Intervals Based on "t" Statistics
ERIC Educational Resources Information Center
Liu, Xiaofeng Steven; Loudermilk, Brandon; Simpson, Thomas
2014-01-01
Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis…
Point Estimation and Confidence Interval Estimation for Binomial and Multinomial Parameters
1975-12-31
AD-A021 208 POINT ESTIMATION AND CONFIDENCE INTERVAL ESTIMATION FOR BINOMIAL AND MULTINOMIAL PARAMETERS Ramesh Chandra Union College...I 00 064098 O < POINT ESTIMATION AND CONFIDENCE INTERVAL ESTIMATION FOR BINOMIAL AND MULTINOMIAL PARAMETERS AES-7514 ■ - 1976...AES-7514 2 COVT ACCESSION NO * TITLC fan« Submit) Point Estimation and Confidence Interval Estimation for Binomial and Multinomial Parameters
Behavior Detection using Confidence Intervals of Hidden Markov Models
Griffin, Christopher H
2009-01-01
Markov models are commonly used to analyze real-world problems. Their combination of discrete states and stochastic transitions is suited to applications with deterministic and stochastic components. Hidden Markov Models (HMMs) are a class of Markov model commonly used in pattern recognition. Currently, HMMs recognize patterns using a maximum likelihood approach. One major drawback with this approach is that data observations are mapped to HMMs without considering the number of data samples available. Another problem is that this approach is only useful for choosing between HMMs. It does not provide a criteria for determining whether or not a given HMM adequately matches the data stream. In this work, we recognize complex behaviors using HMMs and confidence intervals. The certainty of a data match increases with the number of data samples considered. Receiver Operating Characteristic curves are used to find the optimal threshold for either accepting or rejecting a HMM description. We present one example using a family of HMM's to show the utility of the proposed approach. A second example using models extracted from a database of consumer purchases provides additional evidence that this approach can perform better than existing techniques.
Simulations of the Hadamard Variance: Probability Distributions and Confidence Intervals.
Ashby, Neil; Patla, Bijunath
2016-04-01
Power-law noise in clocks and oscillators can be simulated by Fourier transforming a modified spectrum of white phase noise. This approach has been applied successfully to simulation of the Allan variance and the modified Allan variance in both overlapping and nonoverlapping forms. When significant frequency drift is present in an oscillator, at large sampling times the Allan variance overestimates the intrinsic noise, while the Hadamard variance is insensitive to frequency drift. The simulation method is extended in this paper to predict the Hadamard variance for the common types of power-law noise. Symmetric real matrices are introduced whose traces-the sums of their eigenvalues-are equal to the Hadamard variances, in overlapping or nonoverlapping forms, as well as for the corresponding forms of the modified Hadamard variance. We show that the standard relations between spectral densities and Hadamard variance are obtained with this method. The matrix eigenvalues determine probability distributions for observing a variance at an arbitrary value of the sampling interval τ, and hence for estimating confidence in the measurements.
An Introduction to Confidence Intervals for Both Statistical Estimates and Effect Sizes.
ERIC Educational Resources Information Center
Capraro, Mary Margaret
This paper summarizes methods of estimating confidence intervals, including classical intervals and intervals for effect sizes. The recent American Psychological Association (APA) Task Force on Statistical Inference report suggested that confidence intervals should always be reported, and the fifth edition of the APA "Publication Manual"…
Number of core samples: Mean concentrations and confidence intervals
Jensen, L.; Cromar, R.D.; Wilmarth, S.R.; Heasler, P.G.
1995-01-24
This document provides estimates of how well the mean concentration of analytes are known as a function of the number of core samples, composite samples, and replicate analyses. The estimates are based upon core composite data from nine recently sampled single-shell tanks. The results can be used when determining the number of core samples needed to ``characterize`` the waste from similar single-shell tanks. A standard way of expressing uncertainty in the estimate of a mean is with a 95% confidence interval (CI). The authors investigate how the width of a 95% CI on the mean concentration decreases as the number of observations increase. Specifically, the tables and figures show how the relative half-width (RHW) of a 95% CI decreases as the number of core samples increases. The RHW of a CI is a unit-less measure of uncertainty. The general conclusions are as follows: (1) the RHW decreases dramatically as the number of core samples is increased, the decrease is much smaller when the number of composited samples or the number of replicate analyses are increase; (2) if the mean concentration of an analyte needs to be estimated with a small RHW, then a large number of core samples is required. The estimated number of core samples given in the tables and figures were determined by specifying different sizes of the RHW. Four nominal sizes were examined: 10%, 25%, 50%, and 100% of the observed mean concentration. For a majority of analytes the number of core samples required to achieve an accuracy within 10% of the mean concentration is extremely large. In many cases, however, two or three core samples is sufficient to achieve a RHW of approximately 50 to 100%. Because many of the analytes in the data have small concentrations, this level of accuracy may be satisfactory for some applications.
Simultaneous confidence intervals for a steady-state leaky aquifer groundwater flow model
Christensen, S.; Cooley, R.L.
1996-01-01
Using the optimization method of Vecchia & Cooley (1987), nonlinear Scheffe??-type confidence intervals were calculated tor the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear widths was not correct for the head intervals. Results show that nonlinear effects can cause the nonlinear intervals to be offset from, and either larger or smaller than, the linear approximations. Prior information on some transmissivities helps reduce and stabilize the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.
Confidence Intervals for a Mean and a Proportion in the Bounded Case.
1986-11-01
This paper describes a 100x(1-alpha) confidence interval for the mean of a bounded random variable which is shorter than the interval that...Chebyshev’s inequality induces for small alpha and which avoids the error of approximation that assuming normality induces. The paper also presents an analogous development for deriving a 100x(1-alpha) confidence interval for a proportion.
ERIC Educational Resources Information Center
Aitkin, Murray A.
Fixed-width confidence intervals for a population regression line over a finite interval of x have recently been derived by Gafarian. The method is extended to provide fixed-width confidence intervals for the difference between two population regression lines, resulting in a simple procedure analogous to the Johnson-Neyman technique. (Author)
Krishnamoorthy, K; Lee, Meesook; Zhang, Dan
2017-02-01
Approximate closed-form confidence intervals (CIs) for estimating the difference, relative risk, odds ratio, and linear combination of proportions are proposed. These CIs are developed using the fiducial approach and the modified normal-based approximation to the percentiles of a linear combination of independent random variables. These confidence intervals are easy to calculate as the computation requires only the percentiles of beta distributions. The proposed confidence intervals are compared with the popular score confidence intervals with respect to coverage probabilities and expected widths. Comparison studies indicate that the proposed confidence intervals are comparable with the corresponding score confidence intervals, and better in some cases, for all the problems considered. The methods are illustrated using several examples.
On the Number of Bootstrap Simulations Required to Construct a Confidence Interval.
1985-03-01
bootstrap simulations needed to construct a percentile-t confidence interval based on an N-sample from a continuous distribution: "i) The bootstrap’s...March 1985 -pA ~ k..% ON THE NUMBER OF BOOTSTRAP SIMULATIONS REQUIRED TO CONSTRUCT A CONFIDENCE INTERVAL by Peter Hall 1 University of North Carolina...Chapel Hill 2 Summary. We make two points about the number, B, of bootstrap simulations needed to construct a percentile-t confidence interval based on
2011-09-01
were able to improve the detection of major hemorrhage in trauma patients. II. METHODS A. Respiratory Rate and Confidence Interval Estimation...nature. C. Confidence Interval Performance Evaluation Table II summarizes the ROC AUCs of HRR and RRR for the three CI ranges in the detection of...RR) Study Population is the subset of patients found to have regularized HRs (or RRs) from each of the three confidence interval ranges bFour
A novel nonparametric confidence interval for differences of proportions for correlated binary data.
Duan, Chongyang; Cao, Yingshu; Zhou, Lizhi; Tan, Ming T; Chen, Pingyan
2016-11-16
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance.
Confidence interval based parameter estimation--a new SOCR applet and activity.
Christou, Nicolas; Dinov, Ivo D
2011-01-01
Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates. Efficient and reliable parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. In this manuscript, we demonstrate simulation, construction, validation and interpretation of confidence intervals, under various assumptions, using the interactive web-based tools provided by the Statistics Online Computational Resource (http://www.SOCR.ucla.edu). Specifically, we present confidence interval examples for population means, with known or unknown population standard deviation; population variance; population proportion (exact and approximate), as well as confidence intervals based on bootstrapping or the asymptotic properties of the maximum likelihood estimates. Like all SOCR resources, these confidence interval resources may be openly accessed via an Internet-connected Java-enabled browser. The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval. Two applications of the new interval estimation computational library are presented. The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.
ERIC Educational Resources Information Center
Zhang, Guangjian; Preacher, Kristopher J.; Luo, Shanhong
2010-01-01
This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…
ERIC Educational Resources Information Center
Tryon, Warren W.; Lewis, Charles
2009-01-01
Tryon presented a graphic inferential confidence interval (ICI) approach to analyzing two independent and dependent means for statistical difference, equivalence, replication, indeterminacy, and trivial difference. Tryon and Lewis corrected the reduction factor used to adjust descriptive confidence intervals (DCIs) to create ICIs and introduced…
Comparing median lethal concentration values using confidence interval overlap or ratio tests.
Wheeler, Matthew W; Park, Robert M; Bailer, A John
2006-05-01
Experimenters in toxicology often compare the concentration-response relationship between two distinct populations using the median lethal concentration (LC50). This comparison is sometimes done by calculating the 95% confidence interval for the LC50 for each population, concluding that no significant difference exists if the two confidence intervals overlap. A more appropriate test compares the ratio of the LC50s to 1 or the log(LC50 ratio) to 0. In this ratio test, we conclude that no difference exists in LC50s if the confidence interval for the ratio of the LC50s contains 1 or the confidence interval for the log(LC50 ratio) contains 0. A Monte Carlo simulation study was conducted to compare the confidence interval overlap test to the ratio test. The confidence interval overlap test performs substantially below the nominal alpha = 0.05 level, closer to p = 0.005; therefore, it has considerably less power for detecting true differences compared to the ratio test. The ratio-based method exhibited better type I error rates and superior power properties in comparison to the confidence interval overlap test. Thus, a ratio-based statistical procedure is preferred to using simple overlap of two independently derived confidence intervals.
The paper presents an accuracy analysis of a suggested approximate confidence interval for system maintainability parameters. Technically, the...using the method of moments. The simulation has application to the classical confidence interval for mean time to repair of a series system, under the
A COMPARISON OF CONFIDENCE INTERVAL PROCEDURES IN CENSORED LIFE TESTING PROBLEMS.
Obtaining a confidence interval for a parameter lambda of an exponential distribution is a frequent occurrence in life testing problems. Oftentimes...the test plan used is one in which all the observations are censored at the same time point. Several approximate confidence interval procedures are
Confidence Intervals for the Mean: To Bootstrap or Not to Bootstrap
ERIC Educational Resources Information Center
Calzada, Maria E.; Gardner, Holly
2011-01-01
The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…
Number of Samples Needed to Obtain Desired Bayesion Confidence Intervals for a Proportion.
1988-03-01
This thesis analyzes a Bayesian method for determining the number of samples that are needed to produce a desired confidence interval size for a...relating sample size and confidence interval size when a Beta prior distribution is employed. Tables and graphs are developed to assist an experimenter
A Comparison of Methods for Estimating Confidence Intervals for Omega-Squared Effect Size
ERIC Educational Resources Information Center
Finch, W. Holmes; French, Brian F.
2012-01-01
Effect size use has been increasing in the past decade in many research areas. Confidence intervals associated with effect sizes are encouraged to be reported. Prior work has investigated the performance of confidence interval estimation with Cohen's d. This study extends this line of work to the analysis of variance case with more than two…
ERIC Educational Resources Information Center
Skidmore, Susan Troncoso
2009-01-01
Recommendations made by major educational and psychological organizations (American Educational Research Association, 2006; American Psychological Association, 2001) call for researchers to regularly report confidence intervals. The purpose of the present paper is to provide support for the use of confidence intervals. To contextualize this…
ERIC Educational Resources Information Center
Strazzeri, Kenneth Charles
2013-01-01
The purposes of this study were to investigate (a) undergraduate students' reasoning about the concepts of confidence intervals (b) undergraduate students' interactions with "well-designed" screencast videos on sampling distributions and confidence intervals, and (c) how screencast videos improve undergraduate students' reasoning ability…
Confidence Interval Estimation for Output of Discrete-Event Simulations Using the Kalman Filter
1992-03-01
output data, three new confidence interval construction techniques have been developed. One technique obtains an estimate of the mean value and its... interval using the final MMAE filter probabilities. The purpose of this research was twofold. The first objective was to explore these new confidence ...estimate of the simulation output’s mean value and its associated variance. The third technique also uses MMAE, but constructs a nonsymmetric confidence
Neutron multiplicity counting: Confidence intervals for reconstruction parameters
Verbeke, Jerome M.
2016-03-09
From nuclear materials accountability to homeland security, the need for improved nuclear material detection, assay, and authentication has grown over the past decades. Starting in the 1940s, neutron multiplicity counting techniques have enabled quantitative evaluation of masses and multiplications of fissile materials. In this paper, we propose a new method to compute uncertainties on these parameters using a model-based sequential Bayesian processor, resulting in credible regions in the fissile material mass and multiplication space. These uncertainties will enable us to evaluate quantitatively proposed improvements to the theoretical fission chain model. Additionally, because the processor can calculate uncertainties in real time,more » it is a useful tool in applications such as portal monitoring: monitoring can stop as soon as a preset confidence of non-threat is reached.« less
Neutron multiplicity counting: Confidence intervals for reconstruction parameters
Verbeke, Jerome M.
2016-03-09
From nuclear materials accountability to homeland security, the need for improved nuclear material detection, assay, and authentication has grown over the past decades. Starting in the 1940s, neutron multiplicity counting techniques have enabled quantitative evaluation of masses and multiplications of fissile materials. In this paper, we propose a new method to compute uncertainties on these parameters using a model-based sequential Bayesian processor, resulting in credible regions in the fissile material mass and multiplication space. These uncertainties will enable us to evaluate quantitatively proposed improvements to the theoretical fission chain model. Additionally, because the processor can calculate uncertainties in real time, it is a useful tool in applications such as portal monitoring: monitoring can stop as soon as a preset confidence of non-threat is reached.
Estimation and confidence intervals for empirical mixing distributions
Link, W.A.; Sauer, J.R.
1995-01-01
Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.
Confidence Intervals for Laboratory Sonic Boom Annoyance Tests
NASA Technical Reports Server (NTRS)
Rathsam, Jonathan; Christian, Andrew
2016-01-01
Commercial supersonic flight is currently forbidden over land because sonic booms have historically caused unacceptable annoyance levels in overflown communities. NASA is providing data and expertise to noise regulators as they consider relaxing the ban for future quiet supersonic aircraft. One deliverable NASA will provide is a predictive model for indoor annoyance to aid in setting an acceptable quiet sonic boom threshold. A laboratory study was conducted to determine how indoor vibrations caused by sonic booms affect annoyance judgments. The test method required finding the point of subjective equality (PSE) between sonic boom signals that cause vibrations and signals not causing vibrations played at various amplitudes. This presentation focuses on a few statistical techniques for estimating the interval around the PSE. The techniques examined are the Delta Method, Parametric and Nonparametric Bootstrapping, and Bayesian Posterior Estimation.
Confidence intervals for proportion difference from two independent partially validated series.
Qiu, Shi-Fang; Poon, Wai-Yin; Tang, Man-Lai
2016-10-01
Partially validated series are common when a gold-standard test is too expensive to be applied to all subjects, and hence a fallible device is used accordingly to measure the presence of a characteristic of interest. In this article, confidence interval construction for proportion difference between two independent partially validated series is studied. Ten confidence intervals based on the method of variance estimates recovery (MOVER) are proposed, with each using the confidence limits for the two independent binomial proportions obtained by the asymptotic, Logit-transformation, Agresti-Coull and Bayesian methods. The performances of the proposed confidence intervals and three likelihood-based intervals available in the literature are compared with respect to the empirical coverage probability, confidence width and ratio of mesial non-coverage to non-coverage probability. Our empirical results show that (1) all confidence intervals exhibit good performance in large samples; (2) confidence intervals based on MOVER combining the confidence limits for binomial proportions based on Wilson, Agresti-Coull, Logit-transformation, Bayesian (with three priors) methods perform satisfactorily from small to large samples, and hence can be recommended for practical applications. Two real data sets are analysed to illustrate the proposed methods.
Wang, So-Young; Kang, Seung-Ho
2013-03-11
This article deals with the dependency(ies) of noninferiority test(s) when the two confidence interval method is employed. There are two different definitions of the two confidence interval method. One of the objectives of this article is to sort out some of the confusion in these two different definitions. In the first definition the two confidence interval method is considered as the fixed margin method that treats a noninferiority margin as a fixed constant after it is determined based on historical data. In this article the method is called the two confidence interval method with fixed margin. The issue of the dependency(ies) of noninferiority test(s) does not occur in this case. In the second definition the two confidence interval method incorporates the uncertainty associated with the estimation for the noninferiority margin. In this article the method is called the two confidence interval method with random margin. The dependency(ies) occurs, because the two confidence interval method(s) with random margin shares the same historical data. In this article we investigate how the dependency(ies) affects the unconditional and conditional across-trial type I error rates.
Confidence-interval construction for rate ratio in matched-pair studies with incomplete data.
Li, Hui-Qiong; Chan, Ivan S F; Tang, Man-Lai; Tian, Guo-Liang; Tang, Nian-Sheng
2014-01-01
Matched-pair design is often used in clinical trials to increase the efficiency of establishing equivalence between two treatments with binary outcomes. In this article, we consider such a design based on rate ratio in the presence of incomplete data. The rate ratio is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this article, we propose 10 confidence-interval estimators for the rate ratio in incomplete matched-pair designs. A hybrid method that recovers variance estimates required for the rate ratio from the confidence limits for single proportions is proposed. It is noteworthy that confidence intervals based on this hybrid method have closed-form solution. The performance of the proposed confidence intervals is evaluated with respect to their exact coverage probability, expected confidence interval width, and distal and mesial noncoverage probability. The results show that the hybrid Agresti-Coull confidence interval based on Fieller's theorem performs satisfactorily for small to moderate sample sizes. Two real examples from clinical trials are used to illustrate the proposed confidence intervals.
Pirikahu, Sarah; Jones, Geoffrey; Hazelton, Martin L; Heuer, Cord
2016-08-15
Population attributable risk measures the public health impact of the removal of a risk factor. To apply this concept to epidemiological data, the calculation of a confidence interval to quantify the uncertainty in the estimate is desirable. However, because perhaps of the confusion surrounding the attributable risk measures, there is no standard confidence interval or variance formula given in the literature. In this paper, we implement a fully Bayesian approach to confidence interval construction of the population attributable risk for cross-sectional studies. We show that, in comparison with a number of standard Frequentist methods for constructing confidence intervals (i.e. delta, jackknife and bootstrap methods), the Bayesian approach is superior in terms of percent coverage in all except a few cases. This paper also explores the effect of the chosen prior on the coverage and provides alternatives for particular situations. Copyright © 2016 John Wiley & Sons, Ltd.
Sample size and the width of the confidence interval for mean difference.
Liu, Xiaofeng Steven
2009-05-01
The width of the confidence interval for mean difference can be viewed as a random variable. Overlooking its stochastic nature may lead to a serious underestimate of the sample size required to obtain an adequate probability of achieving the desired width for the confidence interval. The probability of achieving a certain width can either be an unconditional probability or a conditional probability given that the confidence interval includes the true parameter. We reconciled the difference between the unconditional and conditional probabilities by deriving the lower bound of the conditional probability. Additionally, we used the harmonic mean to determine unequal sample sizes for the confidence intervals for the two-mean comparison and multiple-mean comparisons.
Confidence intervals on fit parameters derived from optical reflectance spectroscopy measurements.
Amelink, Arjen; Robinson, Dominic J; Sterenborg, Henricus J C M
2008-01-01
We validate a simple method for determining the confidence intervals on fitted parameters derived from modeling optical reflectance spectroscopy measurements using synthetic datasets. The method estimates the parameter confidence intervals as the square roots of the diagonal elements of the covariance matrix, obtained by multiplying the inverse of the second derivative matrix of chi2 with respect to its free parameters by chi2/v, with v the number of degrees of freedom. We show that this method yields correct confidence intervals as long as the model used to describe the data is correct. Imperfections in the fitting model introduces a bias in the fitted parameters that greatly exceeds the estimated confidence intervals. We investigate the use of various methods to identify and subsequently minimize the bias in the fitted parameters associated with incorrect modeling.
Nonparametric confidence intervals for the one- and two-sample problems.
Zhou, Xiao Hua; Dinh, Phillip
2005-04-01
Confidence intervals for the mean of one sample and the difference in means of two independent samples based on the ordinary-t statistic suffer deficiencies when samples come from skewed families. In this article we evaluate several existing techniques and propose new methods to improve coverage accuracy. The methods examined include the ordinary-t, the bootstrap-t, the biased-corrected acceleration and three new intervals based on transformation of the t-statistic. Our study shows that our new transformation intervals and the bootstrap-t intervals give best coverage accuracy for a variety of skewed distributions, and that our new transformation intervals have shorter interval lengths.
Sample size determination for the confidence interval of linear contrast in analysis of covariance.
Liu, Xiaofeng Steven
2013-03-11
This article provides a way to determine sample size for the confidence interval of the linear contrast of treatment means in analysis of covariance (ANCOVA) without prior knowledge of the actual covariate means and covariate sum of squares, which are modeled as a t statistic. Using the t statistic, one can calculate the appropriate sample size to achieve the desired probability of obtaining a specified width in the confidence interval of the covariate-adjusted linear contrast.
Confidence interval construction for proportion difference in small-sample paired studies.
Tang, Man-Lai; Tang, Nian-Sheng; Chan, Ivan S F
2005-12-15
Paired dichotomous data may arise in clinical trials such as pre-/post-test comparison studies and equivalence trials. Reporting parameter estimates (e.g. odds ratio, rate difference and rate ratio) along with their associated confidence interval estimates becomes a necessity in many medical journals. Various asymptotic confidence interval estimators have long been developed for differences in correlated binary proportions. Nevertheless, the performance of these asymptotic methods may have poor coverage properties in small samples. In this article, we investigate several alternative confidence interval estimators for the difference between binomial proportions based on small-sample paired data. Specifically, we consider exact and approximate unconditional confidence intervals for rate difference via inverting a score test. The exact unconditional confidence interval guarantees the coverage probability, and it is recommended if strict control of coverage probability is required. However, the exact method tends to be overly conservative and computationally demanding. Our empirical results show that the approximate unconditional score confidence interval estimators based on inverting the score test demonstrate reasonably good coverage properties even in small-sample designs, and yet they are relatively easy to implement computationally. We illustrate the methods using real examples from a pain management study and a cancer study.
[Confidence interval calculation for small numbers of observations or no observations at all].
Harari, Gil; Herbst, Shimrit
2014-05-01
Confidence interval calculation is a common statistics measure, which is frequently used in the statistical analysis of studies in medicine and life sciences. A confidence interval specifies a range of values within which the unknown population parameter may lie. In most situations, especially those involving normally-distributed data or large samples of data from other distributions, the normal approximation may be used to calculate the confidence interval. But, if the number of observed cases is small or zero, we recommend that the confidence interval be calculated in more appropriate ways. In such cases, for example, in clinical trials where the number of observed adverse events is small, the criterion for approximate normality is calculated. Confidence intervals are calculated with the use of the approximated normal distribution if this criterion is met, and with the use of the exact binomial distribution if not. This article, accompanied by examples, describes the criteria in which the common and known method cannot be used as well as the stages and methods required to calculate confidence intervals in studies with a small number of observations.
NASA Astrophysics Data System (ADS)
Salamat, Mona; Zare, Mehdi; Holschneider, Matthias; Zöller, Gert
2017-03-01
The problem of estimating the maximum possible earthquake magnitude m_max has attracted growing attention in recent years. Due to sparse data, the role of uncertainties becomes crucial. In this work, we determine the uncertainties related to the maximum magnitude in terms of confidence intervals. Using an earthquake catalog of Iran, m_max is estimated for different predefined levels of confidence in six seismotectonic zones. Assuming the doubly truncated Gutenberg-Richter distribution as a statistical model for earthquake magnitudes, confidence intervals for the maximum possible magnitude of earthquakes are calculated in each zone. While the lower limit of the confidence interval is the magnitude of the maximum observed event,the upper limit is calculated from the catalog and the statistical model. For this aim, we use the original catalog which no declustering methods applied on as well as a declustered version of the catalog. Based on the study by Holschneider et al. (Bull Seismol Soc Am 101(4):1649-1659, 2011), the confidence interval for m_max is frequently unbounded, especially if high levels of confidence are required. In this case, no information is gained from the data. Therefore, we elaborate for which settings finite confidence levels are obtained. In this work, Iran is divided into six seismotectonic zones, namely Alborz, Azerbaijan, Zagros, Makran, Kopet Dagh, Central Iran. Although calculations of the confidence interval in Central Iran and Zagros seismotectonic zones are relatively acceptable for meaningful levels of confidence, results in Kopet Dagh, Alborz, Azerbaijan and Makran are not that much promising. The results indicate that estimating m_max from an earthquake catalog for reasonable levels of confidence alone is almost impossible.
On confidence intervals for the hazard ratio in randomized clinical trials.
Lin, Dan-Yu; Dai, Luyan; Cheng, Gang; Sailer, Martin Oliver
2016-12-01
The log-rank test is widely used to compare two survival distributions in a randomized clinical trial, while partial likelihood (Cox, 1975) is the method of choice for making inference about the hazard ratio under the Cox (1972) proportional hazards model. The Wald 95% confidence interval of the hazard ratio may include the null value of 1 when the p-value of the log-rank test is less than 0.05. Peto et al. (1977) provided an estimator for the hazard ratio based on the log-rank statistic; the corresponding 95% confidence interval excludes the null value of 1 if and only if the p-value of the log-rank test is less than 0.05. However, Peto's estimator is not consistent, and the corresponding confidence interval does not have correct coverage probability. In this article, we construct the confidence interval by inverting the score test under the (possibly stratified) Cox model, and we modify the variance estimator such that the resulting score test for the null hypothesis of no treatment difference is identical to the log-rank test in the possible presence of ties. Like Peto's method, the proposed confidence interval excludes the null value if and only if the log-rank test is significant. Unlike Peto's method, however, this interval has correct coverage probability. An added benefit of the proposed confidence interval is that it tends to be more accurate and narrower than the Wald confidence interval. We demonstrate the advantages of the proposed method through extensive simulation studies and a colon cancer study.
Confidence Intervals for True Scores Using the Skew-Normal Distribution
ERIC Educational Resources Information Center
Garcia-Perez, Miguel A.
2010-01-01
A recent comparative analysis of alternative interval estimation approaches and procedures has shown that confidence intervals (CIs) for true raw scores determined with the Score method--which uses the normal approximation to the binomial distribution--have actual coverage probabilities that are closest to their nominal level. It has also recently…
Exact Confidence Intervals for the Relative Risk and the Odds Ratio
Wang, Weizhen; Shan, Guogen
2015-01-01
Summary For comparison of proportions there are three commonly used measurements: the difference, the relative risk and the odds ratio. Significant effort has been spent on exact confidence intervals for the difference. In this paper, we focus on the relative risk and the odds ratio when data are collected from a matched-pairs design or a two-arm independent binomial experiment. Exact one-sided and two-sided confidence intervals are proposed for each configuration of two measurements and two types of data. The one-sided intervals are constructed using an inductive order, they are the smallest under the order, and are admissible under the set inclusion criterion. The two-sided intervals are the intersection of two one-sided intervals. R codes are developed to implement the intervals. Supplementary materials for this article are available online. PMID:26228945
A new and improved confidence interval for the Mantel-Haenszel risk difference.
Klingenberg, Bernhard
2014-07-30
Writing the variance of the Mantel-Haenszel estimator under the null of homogeneity and inverting the corresponding test, we arrive at an improved confidence interval for the common risk difference in stratified 2 × 2 tables. This interval outperforms a variety of other intervals currently recommended in the literature and implemented in software. We also discuss a score-type confidence interval that allows to incorporate strata/study weights. Both of these intervals work very well under many scenarios common in stratified trials or in a meta-analysis, including situations with a mixture of both small and large strata sample sizes, unbalanced treatment allocation, or rare events. The new interval has the advantage that it is available in closed form with a simple formula. In addition, it applies to matched pairs data. We illustrate the methodology with various stratified clinical trials and a meta-analysis. R code to reproduce all analysis is provided in the Appendix.
Bias-corrected confidence intervals for the concentration parameter in a dilution assay.
Wang, J; Basu, S
1999-03-01
Interval estimates of the concentration of target entities from a serial dilution assay are usually based on the maximum likelihood estimator. The distribution of the maximum likelihood estimator is skewed to the right and is positively biased. This bias results in interval estimates that either provide inadequate coverage relative to the nominal level or yield excessively long intervals. Confidence intervals based on both log transformation and bias reduction are proposed and are shown through simulations to provide appropriate coverage with shorter widths than the commonly used intervals in a variety of designs. An application to feline AIDS research, which motivated this work, is also presented.
Quantifying uncertainty in modelled estimates of annual maximum precipitation: confidence intervals
NASA Astrophysics Data System (ADS)
Panagoulia, Dionysia; Economou, Polychronis; Caroni, Chrys
2016-04-01
The possible nonstationarity of the GEV distribution fitted to annual maximum precipitation under climate change is a topic of active investigation. Of particular significance is how best to construct confidence intervals for items of interest arising from stationary/nonstationary GEV models.We are usually not only interested in parameter estimates but also in quantiles of the GEV distribution and it might be expected that estimates of extreme upper quantiles are far from being normally distributed even for moderate sample sizes.Therefore, we consider constructing confidence intervals for all quantities of interest by bootstrap methods based on resampling techniques. To this end, we examined three bootstrapping approaches to constructing confidence intervals for parameters and quantiles: random-t resampling, fixed-t resampling and the parametric bootstrap. Each approach was used in combination with the normal approximation method, percentile method, basic bootstrap method and bias-corrected method for constructing confidence intervals. We found that all the confidence intervals for the stationary model parameters have similar coverage and mean length. Confidence intervals for the more extreme quantiles tend to become very wide for all bootstrap methods. For nonstationary GEV models with linear time dependence of location or log-linear time dependence of scale, confidence interval coverage probabilities are reasonably accurate for the parameters. For the extreme percentiles, the bias-corrected and accelerated method is best overall, and the fixed-t method also has good average coverage probabilities. Reference: Panagoulia D., Economou P. and Caroni C., Stationary and non-stationary GEV modeling of extreme precipitation over a mountainous area under climate change, Environmetrics, 25 (1), 29-43, 2014.
The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution
NASA Astrophysics Data System (ADS)
Shin, H.; Heo, J.; Kim, T.; Jung, Y.
2007-12-01
The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.
A new method for choosing sample size for confidence interval-based inferences.
Jiroutek, Michael R; Muller, Keith E; Kupper, Lawrence L; Stewart, Paul W
2003-09-01
Scientists often need to test hypotheses and construct corresponding confidence intervals. In designing a study to test a particular null hypothesis, traditional methods lead to a sample size large enough to provide sufficient statistical power. In contrast, traditional methods based on constructing a confidence interval lead to a sample size likely to control the width of the interval. With either approach, a sample size so large as to waste resources or introduce ethical concerns is undesirable. This work was motivated by the concern that existing sample size methods often make it difficult for scientists to achieve their actual goals. We focus on situations which involve a fixed, unknown scalar parameter representing the true state of nature. The width of the confidence interval is defined as the difference between the (random) upper and lower bounds. An event width is said to occur if the observed confidence interval width is less than a fixed constant chosen a priori. An event validity is said to occur if the parameter of interest is contained between the observed upper and lower confidence interval bounds. An event rejection is said to occur if the confidence interval excludes the null value of the parameter. In our opinion, scientists often implicitly seek to have all three occur: width, validity, and rejection. New results illustrate that neglecting rejection or width (and less so validity) often provides a sample size with a low probability of the simultaneous occurrence of all three events. We recommend considering all three events simultaneously when choosing a criterion for determining a sample size. We provide new theoretical results for any scalar (mean) parameter in a general linear model with Gaussian errors and fixed predictors. Convenient computational forms are included, as well as numerical examples to illustrate our methods.
CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.
Cooley, Richard L.; Vecchia, Aldo V.
1987-01-01
A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.
A robust confidence interval for location for symmetric, long-tailed distributions.
Gross, A M
1973-07-01
A procedure called the wave-interval is presented for obtaining a 95% confidence interval for the center (mean, median) of a symmetric distribution that is not only highly efficient when the data have a Normal distribution but also performs well when some or all of the data come from a long-tailed distribution such as the Cauchy. Use of the wave-interval greatly reduces the risk of asserting much less than one's data will support. The only table required is the usual t-table. The wave-interval procedure is definitely recommended for samples of ten or more, and appears satisfactory for samples of nine or eight.
Ramasundarahettige, Chinthanie F; Donner, Allan; Zou, G Y
2009-03-30
Inferences for the difference between two dependent intraclass correlation coefficients (ICCs) may arise in studies in which a sample of subjects are each assessed several times with a new device and a standard. The ICC estimates for the two devices may then be compared using a test of significance. However, a confidence interval for a difference between two ICCs is more informative since it combines point estimation and hypothesis testing into a single inference statement. We propose a procedure that uses confidence limits for a single ICC to recover variance estimates needed to set confidence limits for the difference. An advantage of this approach is that it provides a confidence interval that reflects the underlying sampling distribution. Simulation results show that this method performs very well in terms of overall coverage percentage and tail errors. Two data sets are used to illustrate this procedure.
An Investigation of Quantile Function Estimators Relative to Quantile Confidence Interval Coverage.
Wei, Lai; Wang, Dongliang; Hutson, Alan D
In this article, we investigate the limitations of traditional quantile function estimators and introduce a new class of quantile function estimators, namely, the semi-parametric tail-extrapolated quantile estimators, which has excellent performance for estimating the extreme tails with finite sample sizes. The smoothed bootstrap and direct density estimation via the characteristic function methods are developed for the estimation of confidence intervals. Through a comprehensive simulation study to compare the confidence interval estimations of various quantile estimators, we discuss the preferred quantile estimator in conjunction with the confidence interval estimation method to use under different circumstances. Data examples are given to illustrate the superiority of the semi-parametric tail-extrapolated quantile estimators. The new class of quantile estimators is obtained by slight modification of traditional quantile estimators, and therefore, should be specifically appealing to researchers in estimating the extreme tails.
Tian, Lili
2013-05-01
In diagnostic studies, we often need to combine several biomarkers to increase the diagnostic accuracy. For continuous-scaled biomarkers or diagnostic tests, it is often of interest to estimate the confidence interval for sensitivity at a fixed level of specificity. Despite the fact that there exist many literature reports on confidence interval estimation of sensitivity at a fixed level of specificity for a single marker, the inference procedures for sensitivity at a fixed level of specificity for combined markers have rarely been addressed. This article fills this gap by investigating a generalized variable procedure for this purpose. The performance of the proposed generalized approach is numerically studied. For the optimal linear combination proposed by Su and Liu ( 1993 ), simulation study demonstrates that the proposed approach generally can provide confidence intervals with excellent coverage probabilities. The robustness of the proposed approach is investigated for categorical data. In the end, the proposed approach is applied to a real-life data set.
A self-normalized confidence interval for the mean of a class of nonstationary processes.
Zhao, Zhibiao
2011-01-01
We construct an asymptotic confidence interval for the mean of a class of nonstationary processes with constant mean and time-varying variances. Due to the large number of unknown parameters, traditional approaches based on consistent estimation of the limiting variance of sample mean through moving block or non-overlapping block methods are not applicable. Under a block-wise asymptotically equal cumulative variance assumption, we propose a self-normalized confidence interval that is robust against the nonstationarity and dependence structure of the data. We also apply the same idea to construct an asymptotic confidence interval for the mean difference of nonstationary processes with piecewise constant means. The proposed methods are illustrated through simulations and an application to global temperature series.
Zou, G Y
2013-12-01
The limits of agreement (LoA) method proposed by Bland and Altman has become a standard for assessing agreement between different methods measuring the same quantity. Virtually, all method comparison studies have reported only point estimates of LoA due largely to the lack of simple confidence interval procedures. In this article, we address confidence interval estimation for LoA when multiple measurements per individual are available. Separate procedures are proposed for situations when the underlying true value of the measured quantity is assumed changing and when it is perceived as stable. A fixed number of replicates per individual is not needed for the procedures to work. As shown by the worked examples, the construction of these confidence intervals requires only quantiles from the standard normal and chi-square distributions. Simulation results show the proposed procedures perform well. A SAS macro implementing the methods is available on the publisher's website.
NASA Astrophysics Data System (ADS)
Fang, Chih-Chiang; Yeh, Chun-Wu
2016-09-01
The quantitative evaluation of software reliability growth model is frequently accompanied by its confidence interval of fault detection. It provides helpful information to software developers and testers when undertaking software development and software quality control. However, the explanation of the variance estimation of software fault detection is not transparent in previous studies, and it influences the deduction of confidence interval about the mean value function that the current study addresses. Software engineers in such a case cannot evaluate the potential hazard based on the stochasticity of mean value function, and this might reduce the practicability of the estimation. Hence, stochastic differential equations are utilised for confidence interval estimation of the software fault-detection process. The proposed model is estimated and validated using real data-sets to show its flexibility.
Confidence intervals for a crop yield-loss function in nonlinear regression
Lee, E.H.; Tingey, D.T.; Hogsett, W.E.
1990-01-01
Quantifying the relationship between chronic pollutant exposure and the ensuring biological response requires consideration of nonlinear functions that are flexible enough to generate a wide range of response curves. The linear approximation interval estimates for ozone-induced relative crop yield loss are sensitive to parameter curvature effects in nonlinear regression. The adequacy of Wald's confidence interval for proportional response is studied using the nonlinearity measures proposed by Bates and Watts (1980), Cook and Goldberg (1986), and Clarke (1987a b) and the profile t plots of Bates and Watts (1988). Numerical examples comparing Wald's, likelihood ratio, the bootstrap, and Clarke's adjusted 95% confidence intervals for relative crop yield loss are presented for a number of ozone exposure studies conducted by the National Crop Loss Assessment Network (NCLAN) program. At ambient levels of ozone concentration, the effects of nonlinearity were significant and invalidated the adequacy of Wald's confidence interval. Depending upon the severity of the curvature effects, an alternative interval (i.e., Clarke's adjustment to Wald's interval or the likelihood ratio interval) for proportional yield loss should be considered.
Maruo, Kazushi; Kawai, Norisuke
2014-06-15
In this paper, we propose two new methods for computing confidence intervals for the difference of two independent binomial proportions in small sample cases. Several test-based exact confidence intervals have been developed to guarantee the nominal coverage probability in small sample cases. However, these methods are sometimes unnecessarily too conservative because they use the exact p-value for constructing confidence intervals by maximizing the tail probability to account for the worst configuration. In order to reduce conservatism, our new methods adopt the p-value weighted by two types of functions instead of the maximum p-value. Our proposed methods can be regarded as quasi-exact methods. The performance evaluation results showed that our methods are much less conservative than the exact method. Compared with other existing quasi-exact methods, generally, our methods possess coverage probabilities closer to the nominal confidence level and shorter expected confidence widths. In particular, the beta weighing method provides the most reasonable balance between accurate coverage probability and short interval width in small sample cases.
Two confidence interval approaches on the dependability coefficient in a two-factor crossed design.
Ting, Naitee; Cappelleri, Joseph C; Bushmakin, Andrew G
2009-07-01
For decisions based on the absolute level of performance among individuals, the dependability coefficient-a ratio of variance components-is used as a measure of reliability. Two methods developed for a two-factor random effects crossed (or one-facet) design-the Arteaga, Jeyaratnam, and Graybill (AJG) approach and the Cappelleri and Ting (CT) approach-are applied to construct confidence intervals on the dependability coefficient. A simulation study is conducted to investigate and compare the confidence interval coverage on the dependability coefficient based on AJG and CT. Both methods generally meet at least the nominal coverage. Both methods are illustrated with examples.
O'Gorman, Thomas W
2016-08-08
In the last decade, it has been shown that an adaptive testing method could be used, along with the Robbins-Monro search procedure, to obtain confidence intervals that are often narrower than traditional confidence intervals. However, these confidence interval limits require a great deal of computation and some familiarity with stochastic search methods. We propose a method for estimating the limits of confidence intervals that uses only a few tests of significance. We compare these limits to those obtained by a lengthy Robbins-Monro stochastic search and find that the proposed method is nearly as accurate as the Robbins-Monro search. Adaptive confidence intervals that are produced by the proposed method are often narrower than traditional confidence intervals when the distributions are long-tailed, skewed, or bimodal. Moreover, the proposed method of estimating confidence interval limits is easy to understand, because it is based solely on the p-values from a few tests of significance.
Bootstrap confidence intervals in a complex situation: A sequential paired clinical trial
Morton, S.C.
1988-06-01
This paper considers the problem of determining a confidence interval for the difference between two treatments in a simplified sequential paired clinical trial, which is analogous to setting an interval for the drift of a random walk subject to a parabolic stopping boundary. Three bootstrap methods of construction are applied: Efron's accelerated bias-covered, the DiCiccio-Romano, and the bootstrap-t. The results are compared with a theoretical approximate interval due to Siegmund. Difficulties inherent in the use of these bootstrap methods in a complex situations are illustrated. The DiCiccio-Romano method is shown to be the easiest to apply and to work well. 13 refs.
Note on a Confidence Interval for the Squared Semipartial Correlation Coefficient
ERIC Educational Resources Information Center
Algina, James; Keselman, Harvey J.; Penfield, Randall J.
2008-01-01
A squared semipartial correlation coefficient ([Delta]R[superscript 2]) is the increase in the squared multiple correlation coefficient that occurs when a predictor is added to a multiple regression model. Prior research has shown that coverage probability for a confidence interval constructed by using a modified percentile bootstrap method with…
NASA Astrophysics Data System (ADS)
Franco, Glaura C.; Reisen, Valderio A.
2007-03-01
This paper deals with different bootstrap approaches and bootstrap confidence intervals in the fractionally autoregressive moving average (ARFIMA(p,d,q)) process [J. Hosking, Fractional differencing, Biometrika 68(1) (1981) 165-175] using parametric and semi-parametric estimation techniques for the memory parameter d. The bootstrap procedures considered are: the classical bootstrap in the residuals of the fitted model [B. Efron, R. Tibshirani, An Introduction to the Bootstrap, Chapman and Hall, New York, 1993], the bootstrap in the spectral density function [E. Paparoditis, D.N Politis, The local bootstrap for periodogram statistics. J. Time Ser. Anal. 20(2) (1999) 193-222], the bootstrap in the residuals resulting from the regression equation of the semi-parametric estimators [G.C Franco, V.A Reisen, Bootstrap techniques in semiparametric estimation methods for ARFIMA models: a comparison study, Comput. Statist. 19 (2004) 243-259] and the Sieve bootstrap [P. Bühlmann, Sieve bootstrap for time series, Bernoulli 3 (1997) 123-148]. The performance of these procedures and confidence intervals for d in the stationary and non-stationary ranges are empirically obtained through Monte Carlo experiments. The bootstrap confidence intervals here proposed are alternative procedures with some accuracy to obtain confidence intervals for d.
Metrology and 1/f noise: linear regressions and confidence intervals in flicker noise context
NASA Astrophysics Data System (ADS)
Vernotte, F.; Lantz, E.
2015-04-01
1/f noise is very common but is difficult to handle in a metrological way. After having recalled the main characteristics of a strongly correlated noise, this paper will determine relationships giving confidence intervals over the arithmetic mean and the linear drift parameters. A complete example of processing of an actual measurement sequence affected by 1/f noise will be given.
Lesperance, M.; Reed, W. J.; Stephens, M. A.; Tsao, C.; Wilton, B.
2016-01-01
Benford’s Law is a probability distribution for the first significant digits of numbers, for example, the first significant digits of the numbers 871 and 0.22 are 8 and 2 respectively. The law is particularly remarkable because many types of data are considered to be consistent with Benford’s Law and scientists and investigators have applied it in diverse areas, for example, diagnostic tests for mathematical models in Biology, Genomics, Neuroscience, image analysis and fraud detection. In this article we present and compare statistically sound methods for assessing conformance of data with Benford’s Law, including discrete versions of Cramér-von Mises (CvM) statistical tests and simultaneous confidence intervals. We demonstrate that the common use of many binomial confidence intervals leads to rejection of Benford too often for truly Benford data. Based on our investigation, we recommend that the CvM statistic Ud2, Pearson’s chi-square statistic and 100(1 − α)% Goodman’s simultaneous confidence intervals be computed when assessing conformance with Benford’s Law. Visual inspection of the data with simultaneous confidence intervals is useful for understanding departures from Benford and the influence of sample size. PMID:27018999
ERIC Educational Resources Information Center
Zientek, Linda Reichwein; Yetkiner, Z. Ebrar; Thompson, Bruce
2010-01-01
The authors report the contextualization of effect sizes within mathematics anxiety research, and more specifically within research using the Mathematics Anxiety Rating Scale (MARS) and the MARS for Adolescents (MARS-A). The effect sizes from 45 studies were characterized by graphing confidence intervals (CIs) across studies involving (a) adults…
Lesperance, M; Reed, W J; Stephens, M A; Tsao, C; Wilton, B
2016-01-01
Benford's Law is a probability distribution for the first significant digits of numbers, for example, the first significant digits of the numbers 871 and 0.22 are 8 and 2 respectively. The law is particularly remarkable because many types of data are considered to be consistent with Benford's Law and scientists and investigators have applied it in diverse areas, for example, diagnostic tests for mathematical models in Biology, Genomics, Neuroscience, image analysis and fraud detection. In this article we present and compare statistically sound methods for assessing conformance of data with Benford's Law, including discrete versions of Cramér-von Mises (CvM) statistical tests and simultaneous confidence intervals. We demonstrate that the common use of many binomial confidence intervals leads to rejection of Benford too often for truly Benford data. Based on our investigation, we recommend that the CvM statistic Ud(2), Pearson's chi-square statistic and 100(1 - α)% Goodman's simultaneous confidence intervals be computed when assessing conformance with Benford's Law. Visual inspection of the data with simultaneous confidence intervals is useful for understanding departures from Benford and the influence of sample size.
The Distribution of the Product Explains Normal Theory Mediation Confidence Interval Estimation.
Kisbu-Sakarya, Yasemin; MacKinnon, David P; Miočević, Milica
2014-05-01
The distribution of the product has several useful applications. One of these applications is its use to form confidence intervals for the indirect effect as the product of 2 regression coefficients. The purpose of this article is to investigate how the moments of the distribution of the product explain normal theory mediation confidence interval coverage and imbalance. Values of the critical ratio for each random variable are used to demonstrate how the moments of the distribution of the product change across values of the critical ratio observed in research studies. Results of the simulation study showed that as skewness in absolute value increases, coverage decreases. And as skewness in absolute value and kurtosis increases, imbalance increases. The difference between testing the significance of the indirect effect using the normal theory versus the asymmetric distribution of the product is further illustrated with a real data example. This article is the first study to show the direct link between the distribution of the product and indirect effect confidence intervals and clarifies the results of previous simulation studies by showing why normal theory confidence intervals for indirect effects are often less accurate than those obtained from the asymmetric distribution of the product or from resampling methods.
Confidence Intervals for the Proportion of Mastery in Criterion-Referenced Measurement.
ERIC Educational Resources Information Center
Feldt, Leonard S.
1996-01-01
A relatively simple method is developed to obtain confidence intervals for a student's proportion of domain mastery in criterion-referenced or mastery measurement situations. The method uses the binomial distribution as a model for the student's scores under hypothetically repeated assessments, and it makes use of widely available "F"…
A Monte Carlo Study of Eight Confidence Interval Methods for Coefficient Alpha
ERIC Educational Resources Information Center
Romano, Jeanine L.; Kromrey, Jeffrey D.; Hibbard, Susan T.
2010-01-01
The purpose of this research is to examine eight of the different methods for computing confidence intervals around alpha that have been proposed to determine which of these, if any, is the most accurate and precise. Monte Carlo methods were used to simulate samples under known and controlled population conditions. In general, the differences in…
Likelihood based observability analysis and confidence intervals for predictions of dynamic models
2012-01-01
Background Predicting a system’s behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. Results In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. Conclusions The presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/∼ckreutz/PPL. PMID:22947028
Confidence interval estimation for number of patient-years needed to treat.
Zhu, Haiyuan; Wu, Xiao
2014-01-01
The number of patient-years needed to treat (NPY NT), also called the event-based number needed to treat, to avoid one additional exacerbation has been reported in recently published respiratory trials, but the confidence intervals are not routinely reported. The challenge of constructing confidence intervals for NPY NT is due to the fact that exacerbation data or count data in general are usually analyzed using Poisson-based models such as Poisson or negative binomial regression and the rate ratio is the natural metric for between-treatment comparison, while NPY NT is based on rate difference, which is not usually calculated for those models. Therefore, the variance estimates from these analysis models are directly related to the rate ratio rather than the rate difference. In this paper, we propose several methods to construct confidence intervals for the NPY NT, assuming that the event rates are estimated using Poisson or negative binomial regression models. The coverage property of the confidence intervals constructed with these methods is assessed by simulations.
Confidence Intervals for an Effect Size Measure in Multiple Linear Regression
ERIC Educational Resources Information Center
Algina, James; Keselman, H. J.; Penfield, Randall D.
2007-01-01
The increase in the squared multiple correlation coefficient ([Delta]R[squared]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The coverage probability that an asymptotic and percentile bootstrap confidence interval includes [Delta][rho][squared] was investigated. As expected,…
ERIC Educational Resources Information Center
Sapp, Marty
2012-01-01
Like many journals within education, for "Multicultural Learning and Teaching," this writer found little written on measurement, effect sizes, and confidence intervals; therefore, the purpose of this article is to address these factors. The effect of not addressing these issues is that a basic foundation of science cannot be established…
A Note on Confidence Intervals for Two-Group Latent Mean Effect Size Measures
ERIC Educational Resources Information Center
Choi, Jaehwa; Fan, Weihua; Hancock, Gregory R.
2009-01-01
This note suggests delta method implementations for deriving confidence intervals for a latent mean effect size measure for the case of 2 independent populations. A hypothetical kindergarten reading example using these implementations is provided, as is supporting LISREL syntax. (Contains 1 table.)
Charter, Richard A
2009-04-01
Over 50 years ago Payne and Jones (1957) developed what has been labeled the traditional reliable difference formula that continues to be useful as a significance test for the difference between two test scores. The traditional reliable difference is based on the standard error of measurement (SEM) and has been updated to a confidence interval approach. As an alternative to the traditional reliable difference, this article presents the regression-based reliable difference that is based on the standard error of estimate (SEE) and estimated true scores. This new approach should be attractive to clinicians preferring the idea of scores regressing toward the mean. The new approach is also presented in confidence interval form with an interpretation that can be viewed as a statement of all hypotheses that are tenable and consistent with the observed data and has the backing of several authorities. Two well-known conceptualizations for true score confidence intervals are the traditional and regression-based. Now clinicians favoring the regression-based conceptualization are not restricted to the use of traditional model when testing score differences using confidence intervals.
Spacecraft utility and the development of confidence intervals for criticality of anomalies
NASA Technical Reports Server (NTRS)
Williams, R. E.
1980-01-01
The concept of spacecraft utility, a measure of its performance in orbit, is discussed and its formulation is described. Performance is defined in terms of the malfunctions that occur and the criticality to the mission of these malfunctions. Different approaches to establishing average or expected values of criticality are discussed and confidence intervals are developed for parameters used in the computation of utility.
Applying a Score Confidence Interval to Aiken's Item Content-Relevance Index
ERIC Educational Resources Information Center
Penfield, Randall D.; Giacobbi, Peter R., Jr
2004-01-01
Item content-relevance is an important consideration for researchers when developing scales used to measure psychological constructs. Aiken (1980) proposed a statistic, "V," that can be used to summarize item content-relevance ratings obtained from a panel of expert judges. This article proposes the application of the Score confidence interval to…
Sample Size for Confidence Interval of Covariate-Adjusted Mean Difference
ERIC Educational Resources Information Center
Liu, Xiaofeng Steven
2010-01-01
This article provides a way to determine adequate sample size for the confidence interval of covariate-adjusted mean difference in randomized experiments. The standard error of adjusted mean difference depends on covariate variance and balance, which are two unknown quantities at the stage of planning sample size. If covariate observations are…
Empirical Size, Coverage, and Power of Confidence Intervals for Spearman's Rho.
ERIC Educational Resources Information Center
Caruso, John C.; Cliff, Norman
1997-01-01
Several methods of constructing confidence intervals for Spearman's rho (rank correlation coefficient) (C. Spearman, 1904) were tested in a Monte Carlo study using 2,000 samples of 3 different sizes. Results support the continued use of Spearman's rho in behavioral research. (SLD)
Confidence Intervals for the Overall Effect Size in Random-Effects Meta-Analysis
ERIC Educational Resources Information Center
Sanchez-Meca, Julio; Marin-Martinez, Fulgencio
2008-01-01
One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account…
Neyman-Pearson confidence intervals for extreme low-level, paired counting.
Potter, W E
1999-02-01
Neyman-Pearson principles are briefly discussed and 95% confidence intervals of the form [0, ##.##] are presented. Use is made of the fact that the probability of the difference of two random variables, each with a Poisson distribution, can be expressed in terms of modified Bessel functions of integral order and elementary functions. The validity of the values is discussed.
Confidence Interval Estimation of KR sub 20--Some Monte Carlo Results.
ERIC Educational Resources Information Center
Mandeville, Garrett K.
An investigation is conducted which presents extensive Monte Carlo results which indicate the conditions under which a procedure using the F distribution can be used to study the robustness of the confidence interval procedures for small samples. A review of the literature is presented. Procedure uses a binary data matrix. Results indicate that…
Optimal and Most Exact Confidence Intervals for Person Parameters in Item Response Theory Models
ERIC Educational Resources Information Center
Doebler, Anna; Doebler, Philipp; Holling, Heinz
2013-01-01
The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter [theta] is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given…
Confidence Intervals: Evaluating and Facilitating Their Use in Health Education Research
ERIC Educational Resources Information Center
Zhang, Jing; Hanik, Bruce W.; Chaney, Beth H.
2008-01-01
Health education researchers have called for research articles in health education to adhere to the recommendations of American Psychological Association and the American Medical Association regarding the reporting and use of effect sizes and confidence intervals (CIs). This article expands on the recommendations by (a) providing an overview of…
The Naive Intuitive Statistician: A Naive Sampling Model of Intuitive Confidence Intervals
ERIC Educational Resources Information Center
Juslin, Peter; Winman, Anders; Hansson, Patrik
2007-01-01
The perspective of the naive intuitive statistician is outlined and applied to explain overconfidence when people produce intuitive confidence intervals and why this format leads to more overconfidence than other formally equivalent formats. The naive sampling model implies that people accurately describe the sample information they have but are…
Multivariate Effect Size Estimation: Confidence Interval Construction via Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2010-01-01
A latent variable modeling method is outlined for constructing a confidence interval (CI) of a popular multivariate effect size measure. The procedure uses the conventional multivariate analysis of variance (MANOVA) setup and is applicable with large samples. The approach provides a population range of plausible values for the proportion of…
Shan, Guogen
2017-02-01
For a diagnostic test with continuous measurement, it is often important to construct confidence intervals for the sensitivity at a fixed level of specificity. Bootstrap-based confidence intervals were shown to have good performance as compared to others, and the one by Zhou and Qin (2005) was recommended as the best existing confidence interval, named the BTII interval. We propose two new confidence intervals based on the profile variance method and conduct extensive simulation studies to compare the proposed intervals and the BTII intervals under a wide range of conditions. An example from a medical study on severe head trauma is used to illustrate application of the new intervals. The new proposed intervals generally have better performance than the BTII interval.
Finite sample pointwise confidence intervals for a survival distribution with right-censored data.
Fay, Michael P; Brittain, Erica H
2016-07-20
We review and develop pointwise confidence intervals for a survival distribution with right-censored data for small samples, assuming only independence of censoring and survival. When there is no censoring, at each fixed time point, the problem reduces to making inferences about a binomial parameter. In this case, the recently developed beta product confidence procedure (BPCP) gives the standard exact central binomial confidence intervals of Clopper and Pearson. Additionally, the BPCP has been shown to be exact (gives guaranteed coverage at the nominal level) for progressive type II censoring and has been shown by simulation to be exact for general independent right censoring. In this paper, we modify the BPCP to create a 'mid-p' version, which reduces to the mid-p confidence interval for a binomial parameter when there is no censoring. We perform extensive simulations on both the standard and mid-p BPCP using a method of moments implementation that enforces monotonicity over time. All simulated scenarios suggest that the standard BPCP is exact. The mid-p BPCP, like other mid-p confidence intervals, has simulated coverage closer to the nominal level but may not be exact for all survival times, especially in very low censoring scenarios. In contrast, the two asymptotically-based approximations have lower than nominal coverage in many scenarios. This poor coverage is due to the extreme inflation of the lower error rates, although the upper limits are very conservative. Both the standard and the mid-p BPCP methods are available in our bpcp R package. Published 2016. This article is US Government work and is in the public domain in the USA.
Ponciano, José Miguel; Taper, Mark L; Dennis, Brian; Lele, Subhash R
2009-02-01
Hierarchical statistical models are increasingly being used to describe complex ecological processes. The data cloning (DC) method is a new general technique that uses Markov chain Monte Carlo (MCMC) algorithms to compute maximum likelihood (ML) estimates along with their asymptotic variance estimates for hierarchical models. Despite its generality, the method has two inferential limitations. First, it only provides Wald-type confidence intervals, known to be inaccurate in small samples. Second, it only yields ML parameter estimates, but not the maximized likelihood values used for profile likelihood intervals, likelihood ratio hypothesis tests, and information-theoretic model selection. Here we describe how to overcome these inferential limitations with a computationally efficient method for calculating likelihood ratios via data cloning. The ability to calculate likelihood ratios allows one to do hypothesis tests, construct accurate confidence intervals and undertake information-based model selection with hierarchical models in a frequentist context. To demonstrate the use of these tools with complex ecological models, we reanalyze part of Gause's classic Paramecium data with state-space population models containing both environmental noise and sampling error. The analysis results include improved confidence intervals for parameters, a hypothesis test of laboratory replication, and a comparison of the Beverton-Holt and the Ricker growth forms based on a model selection index.
Tsai, Tsung-Hsien; Tsai, Wei-Yann; Chi, Yunchan; Chang, Sheng-Mao
2016-03-01
The confidence intervals for the ratio of two median residual lifetimes are developed for left-truncated and right-censored data. The approach of Su and Wei (1993) is first extended by replacing the Kaplan-Meier survival estimator with the estimator of the conditional survival function (Lynden-Bell, 1971). This procedure does not involve a nonparametric estimation of the probability density function of the failure time. However, the Su and Wei type confidence intervals are very conservative even for larger sample size. Therefore, this article proposes an alternative confidence interval for the ratio of two median residual lifetimes, which is not only without nonparametric estimation of the density function of failure times but is also computationally simpler than the Su and Wei type confidence interval. A simulation study is conducted to examine the accuracy of these confidence intervals and the implementation of these confidence intervals to two real data sets is illustrated.
Tang, Nian-Sheng; Li, Hui-Qiong; Tang, Man-Lai; Li, Jie
2016-01-01
Under the assumption of missing at random, eight confidence intervals (CIs) for the difference between two correlated proportions in the presence of incomplete paired binary data are constructed on the basis of the likelihood ratio statistic, the score statistic, the Wald-type statistic, the hybrid method incorporated with the Wilson score and Agresti-Coull (AC) intervals, and the Bootstrap-resampling method. Extensive simulation studies are conducted to evaluate the performance of the presented CIs in terms of coverage probability and expected interval width. Our empirical results evidence that the Wilson-score-based hybrid CI and the Wald-type CI together with the constrained maximum likelihood estimates perform well for small-to-moderate sample sizes in the sense that (i) their empirical coverage probabilities are quite close to the prespecified confidence level, (ii) their expected interval widths are shorter, and (iii) their ratios of the mesial non-coverage to non-coverage probabilities lie in interval [0.4, 0.6]. An example from a neurological study is used to illustrate the proposed methodologies.
Confidence interval of the likelihood ratio associated with mixed stain DNA evidence.
Beecham, Gary W; Weir, Bruce S
2011-01-01
Likelihood ratios are necessary to properly interpret mixed stain DNA evidence. They can flexibly consider alternate hypotheses and can account for population substructure. The likelihood ratio should be seen as an estimate and not a fixed value, because the calculations are functions of allelic frequency estimates that were estimated from a small portion of the population. Current methods do not account for uncertainty in the likelihood ratio estimates and are therefore an incomplete picture of the strength of the evidence. We propose the use of a confidence interval to report the consequent variation of likelihood ratios. The confidence interval is calculated using the standard forensic likelihood ratio formulae and a variance estimate derived using the Taylor expansion. The formula is explained, and a computer program has been made available. Numeric work shows that the evidential strength of DNA profiles decreases as the variation among populations increases.
Alpha's standard error (ASE): an accurate and precise confidence interval estimate.
Duhachek, Adam; Lacobucci, Dawn
2004-10-01
This research presents the inferential statistics for Cronbach's coefficient alpha on the basis of the standard statistical assumption of multivariate normality. The estimation of alpha's standard error (ASE) and confidence intervals are described, and the authors analytically and empirically investigate the effects of the components of these equations. The authors then demonstrate the superiority of this estimate compared with previous derivations of ASE in a separate Monte Carlo simulation. The authors also present a sampling error and test statistic for a test of independent sample alphas. They conclude with a recommendation that all alpha coefficients be reported in conjunction with standard error or confidence interval estimates and offer SAS and SPSS programming codes for easy implementation.
MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis.
Campbell, Jamie I D; Thompson, Valerie A
2012-12-01
MorePower 6.0 is a flexible freeware statistical calculator that computes sample size, effect size, and power statistics for factorial ANOVA designs. It also calculates relational confidence intervals for ANOVA effects based on formulas from Jarmasz and Hollands (Canadian Journal of Experimental Psychology 63:124-138, 2009), as well as Bayesian posterior probabilities for the null and alternative hypotheses based on formulas in Masson (Behavior Research Methods 43:679-690, 2011). The program is unique in affording direct comparison of these three approaches to the interpretation of ANOVA tests. Its high numerical precision and ability to work with complex ANOVA designs could facilitate researchers' attention to issues of statistical power, Bayesian analysis, and the use of confidence intervals for data interpretation. MorePower 6.0 is available at https://wiki.usask.ca/pages/viewpageattachments.action?pageId=420413544 .
Tian, Lili; Xiong, Chengjie; Lai, Chin-Ying; Vexler, Albert
2011-01-01
In the cases with three ordinal diagnostic groups, the important measures of diagnostic accuracy are the volume under surface (VUS) and the partial volume under surface (PVUS) which are the extended forms of the area under curve (AUC) and the partial area under curve (PAUC). This article addresses confidence interval estimation of the difference in paired VUS s and the difference in paired PVUS s. To focus especially on studies with small to moderate sample sizes, we propose an approach based on the concepts of generalized inference. A Monte Carlo study demonstrates that the proposed approach generally can provide confidence intervals with reasonable coverage probabilities even at small sample sizes. The proposed approach is compared to a parametric bootstrap approach and a large sample approach through simulation. Finally, the proposed approach is illustrated via an application to a data set of blood test results of anemia patients.
Confidence interval estimation of the difference between paired AUCs based on combined biomarkers.
Tian, Lili; Vexler, Albert; Yan, Li; Schisterman, Enrique F
2009-01-01
In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.
Odds ratios and confidence intervals: a review for the pediatric oncology clinician.
Laing, Catherine M; Rankin, James A
2011-01-01
Professional registered nurses (RNs) are active participants in seeking and interpreting research evidence. To facilitate knowledge transfer for RNs at the bedside, it behooves researchers to present their findings in a format that facilitates understanding. There is also an expectation that clinicians are capable of interpreting results in a meaningful way. It is important to be able to understand and interpret research reports where statistical methods are used as part of providing the safest and best care for patients. The purpose of this article is to describe the basic concepts of odds ratios and confidence intervals used in research. These statistical measures are used frequently in quantitative research and are often the principle measure of association that is reported. The more comfortable pediatric oncology clinicians are with the interpretation of odds ratios and confidence intervals, the better equipped they will be to bring relevant research results from the "bench" to the bedside.
Efird, Jimmy Thomas; Nielsen, Susan Searles
2008-12-01
Epidemiological studies commonly test multiple null hypotheses. In some situations it may be appropriate to account for multiplicity using statistical methodology rather than simply interpreting results with greater caution as the number of comparisons increases. Given the one-to-one relationship that exists between confidence intervals and hypothesis tests, we derive a method based upon the Hochberg step-up procedure to obtain multiplicity corrected confidence intervals (CI) for odds ratios (OR) and by analogy for other relative effect estimates. In contrast to previously published methods that explicitly assume knowledge of P values, this method only requires that relative effect estimates and corresponding CI be known for each comparison to obtain multiplicity corrected CI.
The use of latin hypercube sampling for the efficient estimation of confidence intervals
Grabaskas, D.; Denning, R.; Aldemir, T.; Nakayama, M. K.
2012-07-01
Latin hypercube sampling (LHS) has long been used as a way of assuring adequate sampling of the tails of distributions in a Monte Carlo analysis and provided the framework for the uncertainty analysis performed in the NUREG-1150 risk assessment. However, this technique has not often been used in the performance of regulatory analyses due to the inability to establish confidence levels on the quantiles of the output distribution. Recent work has demonstrated a method that makes this possible. This method is compared to the procedure of crude Monte Carlo using order statistics, which is currently used to establish confidence levels. The results of several statistical examples demonstrate that the LHS confidence interval method can provide a more accurate and precise solution, but issues remain when applying the technique generally. (authors)
Pointwise confidence intervals for a survival distribution with small samples or heavy censoring.
Fay, Michael P; Brittain, Erica H; Proschan, Michael A
2013-09-01
We propose a beta product confidence procedure (BPCP) that is a non-parametric confidence procedure for the survival curve at a fixed time for right-censored data assuming independent censoring. In such situations, the Kaplan-Meier estimator is typically used with an asymptotic confidence interval (CI) that can have coverage problems when the number of observed failures is not large, and/or when testing the latter parts of the curve where there are few remaining subjects at risk. The BPCP guarantees central coverage (i.e. ensures that both one-sided error rates are no more than half of the total nominal rate) when there is no censoring (in which case it reduces to the Clopper-Pearson interval) or when there is progressive type II censoring (i.e. when censoring only occurs immediately after failures on fixed proportions of the remaining individuals). For general independent censoring, simulations show that the BPCP maintains central coverage in many situations where competing methods can have very substantial error rate inflation for the lower limit. The BPCP gives asymptotically correct coverage and is asymptotically equivalent to the CI on the Kaplan-Meier estimator using Greenwood's variance. The BPCP may be inverted to create confidence procedures for a quantile of the underlying survival distribution. Because the BPCP is easy to implement, offers protection in settings when other methods fail, and essentially matches other methods when they succeed, it should be the method of choice.
Non-parametric estimators of a monotonic dose-response curve and bootstrap confidence intervals.
Dilleen, Maria; Heimann, Günter; Hirsch, Ian
2003-03-30
In this paper we consider study designs which include a placebo and an active control group as well as several dose groups of a new drug. A monotonically increasing dose-response function is assumed, and the objective is to estimate a dose with equivalent response to the active control group, including a confidence interval for this dose. We present different non-parametric methods to estimate the monotonic dose-response curve. These are derived from the isotonic regression estimator, a non-negative least squares estimator, and a bias adjusted non-negative least squares estimator using linear interpolation. The different confidence intervals are based upon an approach described by Korn, and upon two different bootstrap approaches. One of these bootstrap approaches is standard, and the second ensures that resampling is done from empiric distributions which comply with the order restrictions imposed. In our simulations we did not find any differences between the two bootstrap methods, and both clearly outperform Korn's confidence intervals. The non-negative least squares estimator yields biased results for moderate sample sizes. The bias adjustment for this estimator works well, even for small and moderate sample sizes, and surprisingly outperforms the isotonic regression method in certain situations.
Bolboacă, Sorana; Jäntschi, Lorentz
2005-01-01
Likelihood Ratio medical key parameters calculated on categorical results from diagnostic tests are usually express accompanied with their confidence intervals, computed using the normal distribution approximation of binomial distribution. The approximation creates known anomalies,especially for limit cases. In order to improve the quality of estimation, four new methods (called here RPAC, RPAC0, RPAC1, and RPAC2) were developed and compared with the classical method (called here RPWald), using an exact probability calculation algorithm.Computer implementations of the methods use the PHP language. We defined and implemented the functions of the four new methods and the five criterions of confidence interval assessment. The experiments run for samples sizes which vary in 14 - 34 range, 90 - 100 range (0 < X < m, 0< Y < n), as well as for random numbers for samples sizes (4m, n confidence interval for positive and negative likelihood ratios.
Perez, Anne E; Haskell, Neal H; Wells, Jeffrey D
2014-08-01
Carrion insect succession patterns have long been used to estimate the postmortem interval (PMI) during a death investigation. However, no published carrion succession study included sufficient replication to calculate a confidence interval about a PMI estimate based on occurrence data. We exposed 53 pig carcasses (16±2.5 kg), near the likely minimum needed for such statistical analysis, at a site in north-central Indiana, USA, over three consecutive summer seasons. Insects and Collembola were sampled daily from each carcass for a total of 14 days, by this time each was skeletonized. The criteria for judging a life stage of a given species to be potentially useful for succession-based PMI estimation were (1) nonreoccurrence (observed during a single period of presence on a corpse), and (2) found in a sufficiently large proportion of carcasses to support a PMI confidence interval. For this data set that proportion threshold is 45/53. Of the 266 species collected and identified, none was nonreoccuring in that each showed at least a gap of one day on a single carcass. If the definition of nonreoccurrence is relaxed to include such a single one-day gap the larval forms of Necrophilaamericana, Fanniascalaris, Cochliomyia macellaria, Phormiaregina, and Luciliaillustris satisfied these two criteria. Adults of Creophilus maxillosus, Necrobiaruficollis, and Necrodessurinamensis were common and showed only a few, single-day gaps in occurrence. C.maxillosus, P.regina, and L.illustris displayed exceptional forensic utility in that they were observed on every carcass. Although these observations were made at a single site during one season of the year, the species we found to be useful have large geographic ranges. We suggest that future carrion insect succession research focus only on a limited set of species with high potential forensic utility so as to reduce sample effort per carcass and thereby enable increased experimental replication.
NASA Astrophysics Data System (ADS)
Witkovský, Viktor; Wimmer, Gejza; Ďuriš, Stanislav
2015-08-01
We consider a problem of constructing the exact and/or approximate coverage intervals for the common mean of several independent distributions. In a metrological context, this problem is closely related to evaluation of the interlaboratory comparison experiments, and in particular, to determination of the reference value (estimate) of a measurand and its uncertainty, or alternatively, to determination of the coverage interval for a measurand at a given level of confidence, based on such comparison data. We present a brief overview of some specific statistical models, methods, and algorithms useful for determination of the common mean and its uncertainty, or alternatively, the proper interval estimator. We illustrate their applicability by a simple simulation study and also by example of interlaboratory comparisons for temperature. In particular, we shall consider methods based on (i) the heteroscedastic common mean fixed effect model, assuming negligible laboratory biases, (ii) the heteroscedastic common mean random effects model with common (unknown) distribution of the laboratory biases, and (iii) the heteroscedastic common mean random effects model with possibly different (known) distributions of the laboratory biases. Finally, we consider a method, recently suggested by Singh et al., for determination of the interval estimator for a common mean based on combining information from independent sources through confidence distributions.
ERIC Educational Resources Information Center
Kelley, Ken; Lai, Keke
2011-01-01
The root mean square error of approximation (RMSEA) is one of the most widely reported measures of misfit/fit in applications of structural equation modeling. When the RMSEA is of interest, so too should be the accompanying confidence interval. A narrow confidence interval reveals that the plausible parameter values are confined to a relatively…
ERIC Educational Resources Information Center
Lai, Keke; Kelley, Ken
2011-01-01
In addition to evaluating a structural equation model (SEM) as a whole, often the model parameters are of interest and confidence intervals for those parameters are formed. Given a model with a good overall fit, it is entirely possible for the targeted effects of interest to have very wide confidence intervals, thus giving little information about…
ERIC Educational Resources Information Center
Grant, Timothy S.; Nathan, Mitchell J.
2008-01-01
Confidence intervals are beginning to play an increasing role in the reporting of research findings within the social and behavioral sciences and, consequently, are becoming more prevalent in beginning classes in statistics and research methods. Confidence intervals are an attractive means of conveying experimental results, as they contain a…
ERIC Educational Resources Information Center
Viechtbauer, Wolfgang
2007-01-01
Standardized effect sizes and confidence intervals thereof are extremely useful devices for comparing results across different studies using scales with incommensurable units. However, exact confidence intervals for standardized effect sizes can usually be obtained only via iterative estimation procedures. The present article summarizes several…
NASA Technical Reports Server (NTRS)
Grimes-Ledesma, Lorie; Murthy, Pappu L. N.; Phoenix, S. Leigh; Glaser, Ronald
2007-01-01
In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Overwrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter uncertainties are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.
Tang, Nian-Sheng; Qiu, Shi-Fang; Tang, Man-Lai; Pei, Yan-Bo
2011-06-01
Bilateral dichotomous data are very common in modern medical comparative studies (e.g. comparison of two treatments in ophthalmologic, orthopaedic and otolaryngologic studies) in which information involving paired organs (e.g. eyes, ears and hips) is available from each subject. In this article, we study various confidence interval estimators for proportion difference based on Wald-type statistics, Fieller theorem, likelihood ratio statistic, score statistics and bootstrap resampling method under the dependence or/and independence models for bilateral binary data. Performance is evaluated with respect to the coverage probability and expected width via simulation studies. Our empirical results show that (1) ignoring the dependence feature of bilateral data could lead to severely incorrect coverage probabilities; and (2) Wald-type, score-type and bootstrap confidence intervals based on the dependence model perform satisfactorily for small to large sample sizes in the sense that their empirical coverage probabilities are close to the pre-specified nominal confidence level and are hence recommended. A real data from an otolaryngologic study is used to illustrate the proposed methods.
Liu, Qunfeng; Chen, Wei-Neng; Deng, Jeremiah D; Gu, Tianlong; Zhang, Huaxiang; Yu, Zhengtao; Zhang, Jun
2017-02-07
The popular performance profiles and data profiles for benchmarking deterministic optimization algorithms are extended to benchmark stochastic algorithms for global optimization problems. A general confidence interval is employed to replace the significance test, which is popular in traditional benchmarking methods but suffering more and more criticisms. Through computing confidence bounds of the general confidence interval and visualizing them with performance profiles and (or) data profiles, our benchmarking method can be used to compare stochastic optimization algorithms by graphs. Compared with traditional benchmarking methods, our method is synthetic statistically and therefore is suitable for large sets of benchmark problems. Compared with some sample-mean-based benchmarking methods, e.g., the method adopted in black-box-optimization-benchmarking workshop/competition, our method considers not only sample means but also sample variances. The most important property of our method is that it is a distribution-free method, i.e., it does not depend on any distribution assumption of the population. This makes it a promising benchmarking method for stochastic optimization algorithms. Some examples are provided to illustrate how to use our method to compare stochastic optimization algorithms.
NASA Technical Reports Server (NTRS)
Grimes-Ledesma, Lorie; Murthy, Pappu, L. N.; Phoenix, S. Leigh; Glaser, Ronald
2006-01-01
In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Ovenvrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter error are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.
Bootstrap confidence intervals for the sensitivity of a quantitative diagnostic test.
Platt, R W; Hanley, J A; Yang, H
2000-02-15
We examine bootstrap approaches to the analysis of the sensitivity of quantitative diagnostic test data. Methods exist for inference concerning the sensitivity of one or more tests for fixed levels of specificity, taking into account the variability in the sensitivity due to variability in the test values for normal subjects. However, parametric methods do not adequately account for error, particularly when the data are non-normally distributed, and non-parametric methods have low power. We implement bootstrap methods for confidence limits for the sensitivity of a test for a fixed specificity and demonstrate that under certain circumstances the bootstrap method gives more accurate confidence intervals than do other methods, while it performs at least as well as other methods in many standard situations.
Barker, Lawrence; Cadwell, Betsy L
2008-09-10
We compared eight nominal 95 per cent confidence intervals for the ratio of two Poisson parameters, both assumed small, on their true coverage (the probability that the interval includes the ratio of Poisson parameters) and median width. The commonly used log-linear interval, justified by asymptotic considerations, provided coverage and relatively narrow intervals, despite small numbers of arrivals. However, the uniform and scores intervals, defined in the text, come very close to providing coverage while providing substantially narrower intervals. These intervals might have practical applications. In a sensitivity analysis, none of the intervals maintained coverage for negative binomial data, indicating that distributional assumptions should be checked before taking our recommendations.
Evolution of Heterogeneity (I2) Estimates and Their 95% Confidence Intervals in Large Meta-Analyses
Thorlund, Kristian; Imberger, Georgina; Johnston, Bradley C.; Walsh, Michael; Awad, Tahany; Thabane, Lehana; Gluud, Christian; Devereaux, P. J.; Wetterslev, Jørn
2012-01-01
Background Assessment of heterogeneity is essential in systematic reviews and meta-analyses of clinical trials. The most commonly used heterogeneity measure, I2, provides an estimate of the proportion of variability in a meta-analysis that is explained by differences between the included trials rather than by sampling error. Recent studies have raised concerns about the reliability of I2 estimates, due to their dependence on the precision of included trials and time-dependent biases. Authors have also advocated use of 95% confidence intervals (CIs) to express the uncertainty associated with I2 estimates. However, no previous studies have explored how many trials and events are required to ensure stable and reliable I2 estimates, or how 95% CIs perform as evidence accumulates. Methodology/Principal Findings To assess the stability and reliability of I2 estimates and their 95% CIs, in relation to the cumulative number of trials and events in meta-analysis, we looked at 16 large Cochrane meta-analyses - each including a sufficient number of trials and events to reliably estimate I2 - and monitored the I2 estimates and their 95% CIs for each year of publication. In 10 of the 16 meta-analyses, the I2 estimates fluctuated more than 40% over time. The median number of events and trials required before the cumulative I2 estimates stayed within +/−20% of the final I2 estimate was 467 and 11. No major fluctuations were observed after 500 events and 14 trials. The 95% confidence intervals provided good coverage over time. Conclusions/Significance I2 estimates need to be interpreted with caution when the meta-analysis only includes a limited number of events or trials. Confidence intervals for I2 estimates provide good coverage as evidence accumulates, and are thus valuable for reflecting the uncertainty associated with estimating I2. PMID:22848355
Amplitude estimation of a sine function based on confidence intervals and Bayes' theorem
NASA Astrophysics Data System (ADS)
Eversmann, D.; Pretz, J.; Rosenthal, M.
2016-05-01
This paper discusses the amplitude estimation using data originating from a sine-like function as probability density function. If a simple least squares fit is used, a significant bias is observed if the amplitude is small compared to its error. It is shown that a proper treatment using the Feldman-Cousins algorithm of likelihood ratios allows one to construct improved confidence intervals. Using Bayes' theorem a probability density function is derived for the amplitude. It is used in an application to show that it leads to better estimates compared to a simple least squares fit.
Yang, Zhao; Sun, Xuezheng; Hardin, James W
2013-04-15
For matched-pair binary data, a variety of approaches have been proposed for the construction of a confidence interval (CI) for the difference of marginal probabilities between two procedures. The score-based approximate CI has been shown to outperform other asymptotic CIs. Tango's method provides a score CI by inverting a score test statistic using an iterative procedure. In this paper, we propose an efficient non-iterative method with closed-form expression to calculate Tango's CIs. Examples illustrate the practical application of the new approach.
Fast time-series prediction using high-dimensional data: evaluating confidence interval credibility.
Hirata, Yoshito
2014-05-01
I propose an index for evaluating the credibility of confidence intervals for future observables predicted from high-dimensional time-series data. The index evaluates the distance from the current state to the data manifold. I demonstrate the index with artificial datasets generated from the Lorenz'96 II model [Lorenz, in Proceedings of the Seminar on Predictability, Vol. 1 (ECMWF, Reading, UK, 1996), p. 1], the Lorenz'96 I model [Hansen and Smith, 2859:TROOCI>2.0.CO;2">J. Atmos. Sci. 57, 2859 (2000).
Quiroz, Jorge; Burdick, Richard K
2009-01-01
Individual agreement between two measurement systems is determined using the total deviation index (TDI) or the coverage probability (CP) criteria as proposed by Lin (2000) and Lin et al. (2002). We used a variance component model as proposed by Choudhary (2007). Using the bootstrap approach, Choudhary (2007), and generalized confidence intervals, we construct bounds on TDI and CP. A simulation study was conducted to assess whether the bounds maintain the stated type I error probability of the test. We also present a computational example to demonstrate the statistical methods described in the paper.
Tarone, Aaron M; Foran, David R
2008-07-01
Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.
NASA Technical Reports Server (NTRS)
Murphy, Patrick Charles
1985-01-01
An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.
Empirical likelihood-based confidence intervals for length-biased data
Ning, J.; Qin, J.; Asgharian, M.; Shen, Y.
2013-01-01
Logistic or other constraints often preclude the possibility of conducting incident cohort studies. A feasible alternative in such cases is to conduct a cross-sectional prevalent cohort study for which we recruit prevalent cases, i.e. subjects who have already experienced the initiating event, say the onset of a disease. When the interest lies in estimating the lifespan between the initiating event and a terminating event, say death for instance, such subjects may be followed prospectively until the terminating event or loss to follow-up, whichever happens first. It is well known that prevalent cases have, on average, longer lifespans. As such they do not constitute a representative random sample from the target population; they comprise a biased sample. If the initiating events are generated from a stationary Poisson process, the so-called stationarity assumption, this bias is called length bias. The current literature on length-biased sampling lacks a simple method for estimating the margin of errors of commonly used summary statistics. We fill this gap using the empirical likelihood-based confidence intervals by adapting this method to right-censored length-biased survival data. Both large and small sample behaviors of these confidence intervals are studied. We illustrate our method using a set of data on survival with dementia, collected as part of the Canadian Study of Health and Aging. PMID:23027662
Confidence interval of intrinsic optimum temperature estimated using thermodynamic SSI model.
Ikemoto, Takaya; Kurahashi, Issei; Shi, Pei-Jian
2013-06-01
The intrinsic optimum temperature for the development of ectotherms is one of the most important factors not only for their physiological processes but also for ecological and evolutional processes. The Sharpe-Schoolfield-Ikemoto (SSI) model succeeded in defining the temperature that can thermodynamically meet the condition that at a particular temperature the probability of an active enzyme reaching its maximum activity is realized. Previously, an algorithm was developed by Ikemoto (Tropical malaria does not mean hot environments. Journal of Medical Entomology, 45, 963-969) to estimate model parameters, but that program was computationally very time consuming. Now, investigators can use the SSI model more easily because a full automatic computer program was designed by Shi et al. (A modified program for estimating the parameters of the SSI model. Environmental Entomology, 40, 462-469). However, the statistical significance of the point estimate of the intrinsic optimum temperature for each ectotherm has not yet been determined. Here, we provided a new method for calculating the confidence interval of the estimated intrinsic optimum temperature by modifying the approximate bootstrap confidence intervals method. For this purpose, it was necessary to develop a new program for a faster estimation of the parameters in the SSI model, which we have also done.
Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point.
Lai, Chin-Ying; Tian, Lili; Schisterman, Enrique F
2012-05-01
In diagnostic studies, the receiver operating characteristic (ROC) curve and the area under the ROC curve are important tools in assessing the utility of biomarkers in discriminating between non-diseased and diseased populations. For classifying a patient into the non-diseased or diseased group, an optimal cut-point of a continuous biomarker is desirable. Youden's index (J), defined as the maximum vertical distance between the ROC curve and the diagonal line, serves as another global measure of overall diagnostic accuracy and can be used in choosing an optimal cut-point. The proposed approach is to make use of a generalized approach to estimate the confidence intervals of the Youden index and its corresponding optimal cut-point. Simulation results are provided for comparing the coverage probabilities of the confidence intervals based on the proposed method with those based on the large sample method and the parametric bootstrap method. Finally, the proposed method is illustrated via an application to a data set from a study on Duchenne muscular dystrophy (DMD).
Mishra, D K; Dolan, K D; Yang, L
2008-01-01
Degradation of nutraceuticals in low- and intermediate-moisture foods heated at high temperature (>100 degrees C) is difficult to model because of the nonisothermal condition. Isothermal experiments above 100 degrees C are difficult to design because they require high pressure and small sample size in sealed containers. Therefore, a nonisothermal method was developed to estimate the thermal degradation kinetic parameter of nutraceuticals and determine the confidence intervals for the parameters and the predicted Y (concentration). Grape pomace at 42% moisture content (wb) was heated in sealed 202 x 214 steel cans in a steam retort at 126.7 degrees C for > 30 min. Can center temperature was measured by thermocouple and predicted using Comsol software. Thermal conductivity (k) and specific heat (C(p)) were estimated as quadratic functions of temperature using Comsol and nonlinear regression. The k and C(p) functions were then used to predict temperature inside the grape pomace during retorting. Similar heating experiments were run at different time-temperature treatments from 8 to 25 min for kinetic parameter estimation. Anthocyanin concentration in the grape pomace was measured using HPLC. Degradation rate constant (k(110 degrees C)) and activation energy (E(a)) were estimated using nonlinear regression. The thermophysical properties estimates at 100 degrees C were k = 0.501 W/m degrees C, Cp= 3600 J/kg and the kinetic parameters were k(110 degrees C)= 0.0607/min and E(a)= 65.32 kJ/mol. The 95% confidence intervals for the parameters and the confidence bands and prediction bands for anthocyanin retention were plotted. These methods are useful for thermal processing design for nutraceutical products.
Krishnamoorthy, K; Oral, Evrim
2015-11-26
Standardized likelihood ratio test (SLRT) for testing the equality of means of several log-normal distributions is proposed. The properties of the SLRT and an available modified likelihood ratio test (MLRT) and a generalized variable (GV) test are evaluated by Monte Carlo simulation and compared. Evaluation studies indicate that the SLRT is accurate even for small samples, whereas the MLRT could be quite liberal for some parameter values, and the GV test is in general conservative and less powerful than the SLRT. Furthermore, a closed-form approximate confidence interval for the common mean of several log-normal distributions is developed using the method of variance estimate recovery, and compared with the generalized confidence interval with respect to coverage probabilities and precision. Simulation studies indicate that the proposed confidence interval is accurate and better than the generalized confidence interval in terms of coverage probabilities. The methods are illustrated using two examples.
Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel
2011-10-01
Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before retraining. In this direction, we propose an adaptive classifier based on the intersection of confidence intervals rule for detecting a possible change in the process generating the data as well as identifying the new data to be used to configure the classifier. A key point of the research is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process is subject to abrupt changes.
BootES: an R package for bootstrap confidence intervals on effect sizes.
Kirby, Kris N; Gerlanc, Daniel
2013-12-01
Bootstrap Effect Sizes (bootES; Gerlanc & Kirby, 2012) is a free, open-source software package for R (R Development Core Team, 2012), which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen's d, Hedges's g, and Pearson's r) and makes easily available for the first time the computation of their bootstrap confidence intervals (CIs). In this article, we illustrate how to use bootES to find effect sizes for contrasts in between-subjects, within-subjects, and mixed factorial designs and to find bootstrap CIs for correlations and differences between correlations. An appendix gives a brief introduction to R that will allow readers to use bootES without having prior knowledge of R.
Dong, Tuochuan; Tian, Lili
2015-01-01
Many disease processes can be divided into three stages: the non-diseased stage: the early diseased stage, and the fully diseased stage. To assess the accuracy of diagnostic tests for such diseases, various summary indexes have been proposed, such as volume under the surface (VUS), partial volume under the surface (PVUS), and the sensitivity to the early diseased stage given specificity and the sensitivity to the fully diseased stage (P2). This paper focuses on confidence interval estimation for P2 based on empirical likelihood. Simulation studies are carried out to assess the performance of the new methods compared to the existing parametric and nonparametric ones. A real dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) is analyzed.
Confidence interval of difference of proportions in logistic regression in presence of covariates.
Reeve, Russell
2016-03-16
Comparison of treatment differences in incidence rates is an important objective of many clinical trials. However, often the proportion is affected by covariates, and the adjustment of the predicted proportion is made using logistic regression. It is desirable to estimate the treatment differences in proportions adjusting for the covariates, similarly to the comparison of adjusted means in analysis of variance. Because of the correlation between the point estimates in the different treatment groups, the standard methods for constructing confidence intervals are inadequate. The problem is more difficult in the binary case, as the comparison is not uniquely defined, and the sampling distribution more difficult to analyze. Four procedures for analyzing the data are presented, which expand upon existing methods and generalize the link function. It is shown that, among the four methods studied, the resampling method based on the exact distribution function yields a coverage rate closest to the nominal.
Lee, Soojeong; Jeon, Gwanggil; Kang, Seokhoon
2015-01-01
Blood pressure (BP) is an important vital sign to determine the health of an individual. Although the estimation of average arterial blood pressure using oscillometric methods is possible, there are no established methods for obtaining confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we propose a two-step pseudomaximum amplitude (TSPMA) as a novel approach to obtain improved CIs of SBP and DBP using a double bootstrap approach. The weighted median (WM) filter is employed to reduce impulsive and Gaussian noises in the step of preprocessing. Application of the proposed method provides tighter CIs and smaller standard deviation of CIs than the pseudomaximum amplitude-envelope and maximum amplitude algorithms with Student's t-method.
Properties of Weir and Cockerham's Fst estimators and associated bootstrap confidence intervals.
Leviyang, Sivan; Hamilton, Matthew B
2011-01-01
Weir and Cockerham introduced single locus and multiloci F(st) estimators for the parameter θ. These estimators are commonly used, but little beyond their bias and variance is known. In this work, we develop formulas that allow us to describe how the underlying value of θ and the genetic diversity of sampled loci affect the distributions of these estimators. We show that in certain settings, these estimators are close to normal, while in others they are far from normal. We use these results to analyze confidence interval construction for θ, showing that the percentile-t bootstrap works well while the BCa bootstrap works poorly. Our results are derived using a novel coalescent based method.
Zhang, Fanghong; Miyaoka, Etsuo; Huang, Fuping; Tanaka, Yutaka
2015-01-01
The problem for establishing noninferiority is discussed between a new treatment and a standard (control) treatment with ordinal categorical data. A measure of treatment effect is used and a method of specifying noninferiority margin for the measure is provided. Two Z-type test statistics are proposed where the estimation of variance is constructed under the shifted null hypothesis using U-statistics. Furthermore, the confidence interval and the sample size formula are given based on the proposed test statistics. The proposed procedure is applied to a dataset from a clinical trial. A simulation study is conducted to compare the performance of the proposed test statistics with that of the existing ones, and the results show that the proposed test statistics are better in terms of the deviation from nominal level and the power.
Statistical variability and confidence intervals for planar dose QA pass rates
Bailey, Daniel W.; Nelms, Benjamin E.; Attwood, Kristopher; Kumaraswamy, Lalith; Podgorsak, Matthew B.
2011-11-15
Purpose: The most common metric for comparing measured to calculated dose, such as for pretreatment quality assurance of intensity-modulated photon fields, is a pass rate (%) generated using percent difference (%Diff), distance-to-agreement (DTA), or some combination of the two (e.g., gamma evaluation). For many dosimeters, the grid of analyzed points corresponds to an array with a low areal density of point detectors. In these cases, the pass rates for any given comparison criteria are not absolute but exhibit statistical variability that is a function, in part, on the detector sampling geometry. In this work, the authors analyze the statistics of various methods commonly used to calculate pass rates and propose methods for establishing confidence intervals for pass rates obtained with low-density arrays. Methods: Dose planes were acquired for 25 prostate and 79 head and neck intensity-modulated fields via diode array and electronic portal imaging device (EPID), and matching calculated dose planes were created via a commercial treatment planning system. Pass rates for each dose plane pair (both centered to the beam central axis) were calculated with several common comparison methods: %Diff/DTA composite analysis and gamma evaluation, using absolute dose comparison with both local and global normalization. Specialized software was designed to selectively sample the measured EPID response (very high data density) down to discrete points to simulate low-density measurements. The software was used to realign the simulated detector grid at many simulated positions with respect to the beam central axis, thereby altering the low-density sampled grid. Simulations were repeated with 100 positional iterations using a 1 detector/cm{sup 2} uniform grid, a 2 detector/cm{sup 2} uniform grid, and similar random detector grids. For each simulation, %/DTA composite pass rates were calculated with various %Diff/DTA criteria and for both local and global %Diff normalization
Desharnais, Brigitte; Camirand-Lemyre, Félix; Mireault, Pascal; Skinner, Cameron D
2015-03-01
Calculating the confidence interval is a common procedure in data analysis and is readily obtained from normally distributed populations with the familiar [Formula: see text] formula. However, when working with non-normally distributed data, determining the confidence interval is not as obvious. For this type of data, there are fewer references in the literature, and they are much less accessible. We describe, in simple language, the percentile and bias-corrected and accelerated variations of the bootstrap method to calculate confidence intervals. This method can be applied to a wide variety of parameters (mean, median, slope of a calibration curve, etc.) and is appropriate for normal and non-normal data sets. As a worked example, the confidence interval around the median concentration of cocaine in femoral blood is calculated using bootstrap techniques. The median of the non-toxic concentrations was 46.7 ng/mL with a 95% confidence interval of 23.9-85.8 ng/mL in the non-normally distributed set of 45 postmortem cases. This method should be used to lead to more statistically sound and accurate confidence intervals for non-normally distributed populations, such as reference values of therapeutic and toxic drug concentration, as well as situations of truncated concentration values near the limit of quantification or cutoff of a method.
NASA Technical Reports Server (NTRS)
Murphy, P. C.; Klein, V.
1984-01-01
Improved techniques for estimating airplane stability and control derivatives and their standard errors are presented. A maximum likelihood estimation algorithm is developed which relies on an optimization scheme referred to as a modified Newton-Raphson scheme with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort compared to integrating the analytically-determined sensitivity equations or using a finite difference scheme. An aircraft estimation problem is solved using real flight data to compare MNRES with the commonly used modified Newton-Raphson technique; MNRES is found to be faster and more generally applicable. Parameter standard errors are determined using a random search technique. The confidence intervals obtained are compared with Cramer-Rao lower bounds at the same confidence level. It is observed that the nonlinearity of the cost function is an important factor in the relationship between Cramer-Rao bounds and the error bounds determined by the search technique.
CI2 for creating and comparing confidence-intervals for time-series bivariate plots.
Mullineaux, David R
2017-02-01
Currently no method exists for calculating and comparing the confidence-intervals (CI) for the time-series of a bivariate plot. The study's aim was to develop 'CI2' as a method to calculate the CI on time-series bivariate plots, and to identify if the CI between two bivariate time-series overlap. The test data were the knee and ankle angles from 10 healthy participants running on a motorised standard-treadmill and non-motorised curved-treadmill. For a recommended 10+ trials, CI2 involved calculating 95% confidence-ellipses at each time-point, then taking as the CI the points on the ellipses that were perpendicular to the direction vector between the means of two adjacent time-points. Consecutive pairs of CI created convex quadrilaterals, and any overlap of these quadrilaterals at the same time or ±1 frame as a time-lag calculated using cross-correlations, indicated where the two time-series differed. CI2 showed no group differences between left and right legs on both treadmills, but the same legs between treadmills for all participants showed differences of less knee extension on the curved-treadmill before heel-strike. To improve and standardise the use of CI2 it is recommended to remove outlier time-series, use 95% confidence-ellipses, and scale the ellipse by the fixed Chi-square value as opposed to the sample-size dependent F-value. For practical use, and to aid in standardisation or future development of CI2, Matlab code is provided. CI2 provides an effective method to quantify the CI of bivariate plots, and to explore the differences in CI between two bivariate time-series.
Chen, Yong-Gang; Ding, Li-Xia; Ge, Hong-Li; Zhang, Mao-Zhen; Hu, Yun
2011-09-01
In the present study, based on the leaf-level hyperspectral data of BaiMu, LeiZhu and WuHuanZi, the authors come up with two solutions through the theory of statistics; the first one is that optimal discriminating band between tree species is extracted by mean interval confidence, the other one is that tree species is discriminated by the Manhattan distance and the Min Max interval similarity. The research results showed that (1) the optimal discriminating bands between BaiMu and LeiZhu are around 350-446, 497-527, 553-1 330, 1 355-2 400 and 2 436-2 500 nm; the optimal discriminating bands between BaiMu and WuHuanZi are around 434-555, 580-1 903, 1 914-2 089, 2 172-2 457 and 2 475-2 500 nm; the optimal discriminating bands between LeiZhu and WuHuanZi are around 434-555, 580-1 903, 1 914-2 089, 2 172-2 457 and 2 475-2 500 nm; and this result is helpful for us to find maximum difference to identifying tree species respectively. (2) In these optimal discriminating bands, we find that the Manhattan distance between the same species is far less than the different species; but the Min-Max interval similarity between the same species is far more than the different species, so this result could help us to discriminate and identify different types of tree species effectively.
Hill, M.C.
1989-01-01
Inaccuracies in parameter values, parameterization, stresses, and boundary conditions of analytical solutions and numerical models of groundwater flow produce errors in simulated hydraulic heads. These errors can be quantified in terms of approximate, simultaneous, nonlinear confidence intervals presented in the literature. Approximate confidence intervals can be applied in both error and sensitivity analysis and can be used prior to calibration or when calibration was accomplished by trial and error. The method is expanded for use in numerical problems, and the accuracy of the approximate intervals is evaluated using Monte Carlo runs. Four test cases are reported. -from Author
Confidence interval in estimating solute loads from a small forested catchment
NASA Astrophysics Data System (ADS)
Tada, A.; Tanakamaru, H.
2007-12-01
The evaluation of uncertainty in estimating mass flux (load) from catchments plays the important role in the evaluation of chemical weathering, TMDLs implementation, and so on. Loads from catchments are estimated with many methods such as weighted average, rating curve, regression model, ratio estimator, and composite method, considering the appropriate sampling strategy. Total solute loads for 10 months from a small forested catchment were calculated based on the high-temporal resolution data and used in evaluating the validity of 95% confidence intervals (CIs) of estimated loads. The effect of employing random and flow-stratified sampling methods on 95% CIs was also evaluated. Water quality data of the small forested catchment (12.8 ha) in Japan was collected every 15 minutes during 10 months in 2004 to acquire the 'true values' of solute loads. Those data were measured by the monitoring equipment using FIP (flow injection potentiometry) method with ion-selective electrodes. Measured indices were sodium, potassium, and chloride ion in the stream water. Water quantity (discharge rate) data were measured continuously by the V-notch weir at the catchment outlet. The Beale ratio estimator was employed as the estimation method of solute loads because it was known as unbiased estimator. The bootstrap method was also used for calculating the 95% confidence intervals of solute loads with 2,000 bootstrap replications. Both flow-stratified and random sampling was adopted as sampling strategy which extracted sample data sets from the entire observations. Discharge rate seemed to be a dominant factor of solute concentration because the catchment was almost undisturbed. The validity of 95% CIs were evaluated using the number of inclusion of 'true value' inside CIs out of 1,000 estimations derived from independently and iteratively extracted sample data sets. The number of samples in each data set was set to 5,500, 950, 470, 230, 40, and 20, equivalent to hourly, 6-hourly, 12
Tang, Nian-Sheng; Li, Hui-Qiong; Tang, Man-Lai
2010-01-15
A stratified matched-pair study is often designed for adjusting a confounding effect or effect of different trails/centers/ groups in modern medical studies. The relative risk is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this paper, we propose seven confidence interval estimators for the common relative risk and three simultaneous confidence interval estimators for the relative risks in stratified matched-pair designs. The performance of the proposed methods is evaluated with respect to their type I error rates, powers, coverage probabilities, and expected widths. Our empirical results show that the percentile bootstrap confidence interval and bootstrap-resampling-based Bonferroni simultaneous confidence interval behave satisfactorily for small to large sample sizes in the sense that (i) their empirical coverage probabilities can be well controlled around the pre-specified nominal confidence level with reasonably shorter confidence widths; and (ii) the empirical type I error rates of their associated test statistics are generally closer to the pre-specified nominal level with larger powers. They are hence recommended. Two real examples from clinical laboratory studies are used to illustrate the proposed methodologies.
Cooley, Richard L.
1993-01-01
A new method is developed to efficiently compute exact Scheffé-type confidence intervals for output (or other function of parameters) g(β) derived from a groundwater flow model. The method is general in that parameter uncertainty can be specified by any statistical distribution having a log probability density function (log pdf) that can be expanded in a Taylor series. However, for this study parameter uncertainty is specified by a statistical multivariate beta distribution that incorporates hydrogeologic information in the form of the investigator's best estimates of parameters and a grouping of random variables representing possible parameter values so that each group is defined by maximum and minimum bounds and an ordering according to increasing value. The new method forms the confidence intervals from maximum and minimum limits of g(β) on a contour of a linear combination of (1) the quadratic form for the parameters used by Cooley and Vecchia (1987) and (2) the log pdf for the multivariate beta distribution. Three example problems are used to compare characteristics of the confidence intervals for hydraulic head obtained using different weights for the linear combination. Different weights generally produced similar confidence intervals, whereas the method of Cooley and Vecchia (1987) often produced much larger confidence intervals.
Saha, Krishna K
2012-12-20
The intraclass correlation in binary outcome data sampled from clusters is an important and versatile measure in many biological and biomedical investigations. Properties of the different estimators of the intraclass correlation based on the parametric, semi-parametric, and nonparametric approaches have been studied extensively, mainly in terms of bias and efficiency [see, for example, Ridout et al., Biometrics 1999, 55:137-148; Paul et al., Journal of Statistical Computation and Simulation 2003, 73:507-523; and Lee, Statistical Modelling 2004, 4: 113-126], but little attention has been paid to extending these results to the problem of the confidence intervals. In this article, we generalize the results of the four point estimators by constructing asymptotic confidence intervals obtaining closed-form asymptotic and sandwich variance expressions of those four point estimators. It appears from simulation results that the asymptotic confidence intervals based on these four estimators have serious under-coverage. To remedy this, we introduce the Fisher's z-transformation approach on the intraclass correlation coefficient, the profile likelihood approach based on the beta-binomial model, and the hybrid profile variance approach based on the quadratic estimating equation for constructing the confidence intervals of the intraclass correlation for binary outcome data. As assessed by Monte Carlo simulations, these confidence interval approaches show significant improvement in the coverage probabilities. Moreover, the profile likelihood approach performs quite well by providing coverage levels close to nominal over a wide range of parameter combinations. We provide applications to biological data to illustrate the methods.
Lai, Keke; Kelley, Ken
2011-06-01
In addition to evaluating a structural equation model (SEM) as a whole, often the model parameters are of interest and confidence intervals for those parameters are formed. Given a model with a good overall fit, it is entirely possible for the targeted effects of interest to have very wide confidence intervals, thus giving little information about the magnitude of the population targeted effects. With the goal of obtaining sufficiently narrow confidence intervals for the model parameters of interest, sample size planning methods for SEM are developed from the accuracy in parameter estimation approach. One method plans for the sample size so that the expected confidence interval width is sufficiently narrow. An extended procedure ensures that the obtained confidence interval will be no wider than desired, with some specified degree of assurance. A Monte Carlo simulation study was conducted that verified the effectiveness of the procedures in realistic situations. The methods developed have been implemented in the MBESS package in R so that they can be easily applied by researchers.
Shieh, Gwowen
2013-03-01
Effect size reporting and interpreting practices have been extensively recommended in academic journals when primary outcomes of all empirical studies have been analyzed. This article presents an alternative approach to constructing confidence intervals of the weighted eta-squared effect size within the context of one-way heteroscedastic ANOVA models. It is shown that the proposed interval procedure has advantages over an existing method in its theoretical justification, computational simplicity, and numerical performance. For design planning, the corresponding sample size procedures for precise interval estimation of the weighted eta-squared association measure are also delineated. Specifically, the developed formulas compute the necessary sample sizes with respect to the considerations of expected confidence interval width and tolerance probability of interval width within a designated value. Supplementary computer programs are provided to aid the implementation of the suggested techniques in practical applications of ANOVA designs when the assumption of homogeneous variances is not tenable.
Feingold, Alan
2014-01-01
Objective Multilevel and latent growth models are frequently used interchangeably to examine differences between groups in trajectories of outcomes from controlled clinical trials. The unstandardized coefficient for the effect from group to slope (the treatment effect) from such models can be converted to a standardized mean difference (Cohen's d) between the treatment and control groups at end of study. This article addresses the confidence interval (CI) for this effect size. Method Two sets of equations for estimating the CI for the treatment effect size in multilevel models were derived and their usage was illustrated with data from the National Youth Study. Validity of the CIs was examined with a Monte Carlo simulation study that manipulated effect potency and sample size. Results The equivalence of the two new CI estimation methods was demonstrated and the Monte Carlo study found that bias in the CI for the effect size were not appreciably larger than bias in the CI for the widely used unstandardized coefficient. Conclusions Investigators reporting this increasingly popular effect size can estimate its CI with equations presented in this article. PMID:25181028
Confidence interval procedures for system reliability and applications to competing risks models.
Hong, Yili; Meeker, William Q
2014-04-01
System reliability depends on the reliability of the system's components and the structure of the system. For example, in a competing risks model, the system fails when the weakest component fails. The reliability function and the quantile function of a complicated system are two important metrics for characterizing the system's reliability. When there are data available at the component level, the system reliability can be estimated by using the component level information. Confidence intervals (CIs) are needed to quantify the statistical uncertainty in the estimation. Obtaining system reliability CI procedures with good properties is not straightforward, especially when the system structure is complicated. In this paper, we develop a general procedure for constructing a CI for the system failure-time quantile function by using the implicit delta method. We also develop general procedures for constructing a CI for the cumulative distribution function (cdf) of the system. We show that the recommended procedures are asymptotically valid and have good statistical properties. We conduct simulations to study the finite-sample coverage properties of the proposed procedures and compare them with existing procedures. We apply the proposed procedures to three applications; two applications in competing risks models and an application with a k-out-of-s system. The paper concludes with some discussion and an outline of areas for future research.
Pinto, Francisco R.; Melo-Cristino, José; Ramirez, Mário
2008-01-01
Very diverse research fields frequently deal with the analysis of multiple clustering results, which should imply an objective detection of overlaps and divergences between the formed groupings. The congruence between these multiple results can be quantified by clustering comparison measures such as the Wallace coefficient (W). Since the measured congruence is dependent on the particular sample taken from the population, there is variability in the estimated values relatively to those of the true population. In the present work we propose the use of a confidence interval (CI) to account for this variability when W is used. The CI analytical formula is derived assuming a Gaussian sampling distribution and recurring to the algebraic relationship between W and the Simpson's index of diversity. This relationship also allows the estimation of the expected Wallace value under the assumption of independence of classifications. We evaluated the CI performance using simulated and published microbial typing data sets. The simulations showed that the CI has the desired 95% coverage when the W is greater than 0.5. This behaviour is robust to changes in cluster number, cluster size distributions and sample size. The analysis of the published data sets demonstrated the usefulness of the new CI by objectively validating some of the previous interpretations, while showing that other conclusions lacked statistical support. PMID:19002246
Coulson, Melissa; Healey, Michelle; Fidler, Fiona; Cumming, Geoff
2010-01-01
A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioral neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST.
Coulson, Melissa; Healey, Michelle; Fidler, Fiona; Cumming, Geoff
2010-01-01
A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioral neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST. PMID:21607077
Standard Errors and Confidence Intervals of Norm Statistics for Educational and Psychological Tests.
Oosterhuis, Hannah E M; van der Ark, L Andries; Sijtsma, Klaas
2016-11-14
Norm statistics allow for the interpretation of scores on psychological and educational tests, by relating the test score of an individual test taker to the test scores of individuals belonging to the same gender, age, or education groups, et cetera. Given the uncertainty due to sampling error, one would expect researchers to report standard errors for norm statistics. In practice, standard errors are seldom reported; they are either unavailable or derived under strong distributional assumptions that may not be realistic for test scores. We derived standard errors for four norm statistics (standard deviation, percentile ranks, stanine boundaries and Z-scores) under the mild assumption that the test scores are multinomially distributed. A simulation study showed that the standard errors were unbiased and that corresponding Wald-based confidence intervals had good coverage. Finally, we discuss the possibilities for applying the standard errors in practical test use in education and psychology. The procedure is provided via the R function check.norms, which is available in the mokken package.
Pan, Jin-ren; Chen, Kun
2010-07-01
Interaction assessment is an important step in epidemiological analysis. When etiological study is carried out, the logarithmic models such as logistic model or Cox proportional hazard model are commonly used to estimate the independent effects of the risk factors. However, estimating interaction between risk factors by the regression coefficient of the product term is on multiplicative scale, and for public-health purposes, it is supposed to be on additive scale or departure from additivity. This paper illustrates with a example of cohort study by fitting Cox proportional hazard model to estimate three measures for additive interaction which presented by Rothman. Adopting the S-Plus application with a built-in Bootstrap function, it is convenient to estimate the confidence interval for additive interaction. Furthermore, this method can avoid the exaggerated estimation by using ORs in a cohort study to gain better precision. When using the complex combination models between additive interaction and multiplicative interaction, it is reasonable to choose the former one when the result is inconsistent.
Dong, Tuochuan; Kang, Le; Hutson, Alan; Xiong, Chengjie; Tian, Lili
2014-03-01
Although most of the statistical methods for diagnostic studies focus on disease processes with binary disease status, many diseases can be naturally classified into three ordinal diagnostic categories, that is normal, early stage, and fully diseased. For such diseases, the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. Because the early disease stage is most likely the optimal time window for therapeutic intervention, the sensitivity to the early diseased stage has been suggested as another diagnostic measure. For the purpose of comparing the diagnostic abilities on early disease detection between two markers, it is of interest to estimate the confidence interval of the difference between sensitivities to the early diseased stage. In this paper, we present both parametric and non-parametric methods for this purpose. An extensive simulation study is carried out for a variety of settings for the purpose of evaluating and comparing the performance of the proposed methods. A real example of Alzheimer's disease (AD) is analyzed using the proposed approaches.
Confidence intervals for return levels for the peaks-over-threshold approach
NASA Astrophysics Data System (ADS)
Schendel, Thomas; Thongwichian, Rossukon
2017-01-01
The peaks-over-threshold (POT) approach is an important alternative to the annual block maxima (ABM) method in flood frequency analysis. POT requires the mathematical description of both, the number of exceedances over the threshold as well as the values of those exceedances. Regardless the method, estimates of extreme flood events are typically associated with a large range of uncertainty, which is usually showcased by appropriate confidence intervals (CIs). However, existing methods to estimate CIs for return levels for the POT approach have mostly neglected its dual-domain character and focused on the distribution of the magnitudes only. We present here a customization of two methods, the Profile Likelihood (PL) and test inversion bootstrap (TIB), which account for the dual-domain structure of POT. Both, PL and TIB, are in the framework of ABM already successfully employed for estimating CIs of extreme flood events. A comparison of the performance of the estimated CIs (in terms of coverage error) of the PL, TIB, and percentile bootstrap is done. As result, it is seen that both the lower and upper boundary of the CIs are strongly underestimated for the percentile bootstrap approach. A similar effect (although in a much less pronounced way) can be observed for PL. The performance of the TIB is usually superior to the percentile bootstrap and PL and yielded reasonable estimates for the CIs for large return periods.
Polychronopoulou, Argy; Pandis, Nikolaos; Eliades, Theodore
2011-02-01
The purpose of this study was to search the orthodontic literature and determine the frequency of reporting of confidence intervals (CIs) in orthodontic journals with an impact factor. The six latest issues of the American Journal of Orthodontics and Dentofacial Orthopedics, the European Journal of Orthodontics, and the Angle Orthodontist were hand searched and the reporting of CIs, P values, and implementation of univariate or multivariate statistical analyses were recorded. Additionally, studies were classified according to the type/design as cross-sectional, case-control, cohort, and clinical trials, and according to the subject of the study as growth/genetics, behaviour/psychology, diagnosis/treatment, and biomaterials/biomechanics. The data were analyzed using descriptive statistics followed by univariate examination of statistical associations, logistic regression, and multivariate modelling. CI reporting was very limited and was recorded in only 6 per cent of the included published studies. CI reporting was independent of journal, study area, and design. Studies that used multivariate statistical analyses had a higher probability of reporting CIs compared with those using univariate statistical analyses. Misunderstanding of the use of P values and CIs may have important implications in implementation of research findings in clinical practice.
Garg, Harish
2013-03-01
The main objective of the present paper is to propose a methodology for analyzing the behavior of the complex repairable industrial systems. In real-life situations, it is difficult to find the most optimal design policies for MTBF (mean time between failures), MTTR (mean time to repair) and related costs by utilizing available resources and uncertain data. For this, the availability-cost optimization model has been constructed for determining the optimal design parameters for improving the system design efficiency. The uncertainties in the data related to each component of the system are estimated with the help of fuzzy and statistical methodology in the form of the triangular fuzzy numbers. Using these data, the various reliability parameters, which affects the system performance, are obtained in the form of the fuzzy membership function by the proposed confidence interval based fuzzy Lambda-Tau (CIBFLT) methodology. The computed results by CIBFLT are compared with the existing fuzzy Lambda-Tau methodology. Sensitivity analysis on the system MTBF has also been addressed. The methodology has been illustrated through a case study of washing unit, the main part of the paper industry.
Reliability and Confidence Interval Analysis of a CMC Turbine Stator Vane
NASA Technical Reports Server (NTRS)
Murthy, Pappu L. N.; Gyekenyesi, John P.; Mital, Subodh K.
2008-01-01
an economical manner. The methods to accurately determine the service life of an engine component with associated variability have become increasingly difficult. This results, in part, from the complex missions which are now routinely considered during the design process. These missions include large variations of multi-axial stresses and temperatures experienced by critical engine parts. There is a need for a convenient design tool that can accommodate various loading conditions induced by engine operating environments, and material data with their associated uncertainties to estimate the minimum predicted life of a structural component. A probabilistic composite micromechanics technique in combination with woven composite micromechanics, structural analysis and Fast Probability Integration (FPI) techniques has been used to evaluate the maximum stress and its probabilistic distribution in a CMC turbine stator vane. Furthermore, input variables causing scatter are identified and ranked based upon their sensitivity magnitude. Since the measured data for the ceramic matrix composite properties is very limited, obtaining a probabilistic distribution with their corresponding parameters is difficult. In case of limited data, confidence bounds are essential to quantify the uncertainty associated with the distribution. Usually 90 and 95% confidence intervals are computed for material properties. Failure properties are then computed with the confidence bounds. Best estimates and the confidence bounds on the best estimate of the cumulative probability function for R-S (strength - stress) are plotted. The methodologies and the results from these analyses will be discussed in the presentation.
Kelley, Ken
2008-01-01
Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size so that the expected width of the confidence interval will be sufficiently narrow. Modifications of these methods are then developed so that necessary sample size will lead to sufficiently narrow confidence intervals with no less than some desired degree of assurance. Computer routines have been developed and are included within the MBESS R package so that the methods discussed in the article can be implemented. The methods and computer routines are demonstrated using an empirical example linking innovation in the health services industry with previous innovation, personality factors, and group climate characteristics.
Computationally efficient permutation-based confidence interval estimation for tail-area FDR.
Millstein, Joshua; Volfson, Dmitri
2013-01-01
Challenges of satisfying parametric assumptions in genomic settings with thousands or millions of tests have led investigators to combine powerful False Discovery Rate (FDR) approaches with computationally expensive but exact permutation testing. We describe a computationally efficient permutation-based approach that includes a tractable estimator of the proportion of true null hypotheses, the variance of the log of tail-area FDR, and a confidence interval (CI) estimator, which accounts for the number of permutations conducted and dependencies between tests. The CI estimator applies a binomial distribution and an overdispersion parameter to counts of positive tests. The approach is general with regards to the distribution of the test statistic, it performs favorably in comparison to other approaches, and reliable FDR estimates are demonstrated with as few as 10 permutations. An application of this approach to relate sleep patterns to gene expression patterns in mouse hypothalamus yielded a set of 11 transcripts associated with 24 h REM sleep [FDR = 0.15 (0.08, 0.26)]. Two of the corresponding genes, Sfrp1 and Sfrp4, are involved in wnt signaling and several others, Irf7, Ifit1, Iigp2, and Ifih1, have links to interferon signaling. These genes would have been overlooked had a typical a priori FDR threshold such as 0.05 or 0.1 been applied. The CI provides the flexibility for choosing a significance threshold based on tolerance for false discoveries and precision of the FDR estimate. That is, it frees the investigator to use a more data-driven approach to define significance, such as the minimum estimated FDR, an option that is especially useful for weak effects, often observed in studies of complex diseases.
NASA Technical Reports Server (NTRS)
Rutledge, Charles K.
1988-01-01
The validity of applying chi-square based confidence intervals to far-field acoustic flyover spectral estimates was investigated. Simulated data, using a Kendall series and experimental acoustic data from the NASA/McDonnell Douglas 500E acoustics test, were analyzed. Statistical significance tests to determine the equality of distributions of the simulated and experimental data relative to theoretical chi-square distributions were performed. Bias and uncertainty errors associated with the spectral estimates were easily identified from the data sets. A model relating the uncertainty and bias errors to the estimates resulted, which aided in determining the appropriateness of the chi-square distribution based confidence intervals. Such confidence intervals were appropriate for nontonally associated frequencies of the experimental data but were inappropriate for tonally associated estimate distributions. The appropriateness at the tonally associated frequencies was indicated by the presence of bias error and noncomformity of the distributions to the theoretical chi-square distribution. A technique for determining appropriate confidence intervals at the tonally associated frequencies was suggested.
Results from a NIST-EPA Interagency Agreement on Understanding Systematic Measurement Error in Thermal-Optical Analysis for PM Black Carbon Using Response Surfaces and Surface Confidence Intervals will be presented at the American Association for Aerosol Research (AAAR) 24th Annu...
ERIC Educational Resources Information Center
Ruscio, John; Mullen, Tara
2012-01-01
It is good scientific practice to the report an appropriate estimate of effect size and a confidence interval (CI) to indicate the precision with which a population effect was estimated. For comparisons of 2 independent groups, a probability-based effect size estimator (A) that is equal to the area under a receiver operating characteristic curve…
ERIC Educational Resources Information Center
Odgaard, Eric C.; Fowler, Robert L.
2010-01-01
Objective: In 2005, the "Journal of Consulting and Clinical Psychology" ("JCCP") became the first American Psychological Association (APA) journal to require statistical measures of clinical significance, plus effect sizes (ESs) and associated confidence intervals (CIs), for primary outcomes (La Greca, 2005). As this represents the single largest…
ERIC Educational Resources Information Center
Kelley, Ken
2008-01-01
Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size…
Saeki, Hiroyuki; Tango, Toshiro
2011-12-10
The efficacy of diagnostic procedures is generally evaluated on the basis of the results from multiple raters. However, there are few adequate methods of performing non-inferiority tests with confidence intervals to compare the accuracies (sensitivities or specificities) when multiple raters are considered. We propose new statistical methods for comparing the accuracies of two diagnostic procedures in a non-inferiority trial, on the basis of the results from multiple independent raters who are also independent of the study centers. We consider a study design in which each patient is subjected to two diagnostic procedures and all images are read by all raters. By assuming a multinomial distribution for matched-pair categorical data arising from the study design, we derive a score-based full menu, that is, a non-inferiority test, confidence interval and sample size formula, for inference of the difference in correlated proportions between the two diagnostic procedures. We conduct Monte Carlo simulation studies to examine the validity of the proposed methods, which showed that the proposed test has a size closer to the nominal significance level than a Wald-type test and that the proposed confidence interval has better empirical coverage probability than a Wald-type confidence interval. We illustrate the proposed methods with data from a study of diagnostic procedures for the diagnosis of oesophageal carcinoma infiltrating the tracheobronchial tree.
NASA Astrophysics Data System (ADS)
Kyselý, Jan
2010-08-01
Bootstrap, a technique for determining the accuracy of statistics, is a tool widely used in climatological and hydrological applications. The paper compares coverage probabilities of confidence intervals of high quantiles (5- to 200-year return values) constructed by the nonparametric and parametric bootstrap in frequency analysis of heavy-tailed data, typical for maxima of precipitation amounts. The simulation experiments are based on a wide range of models used for precipitation extremes (generalized extreme value, generalized Pareto, generalized logistic, and mixed distributions). The coverage probability of the confidence intervals is quantified for several sample sizes ( n = 20, 40, 60, and 100) and tail behaviors. We show that both bootstrap methods underestimate the width of the confidence intervals but that the parametric bootstrap is clearly superior to the nonparametric one. Even a misspecification of the parametric model—often unavoidable in practice—does not prevent the parametric bootstrap from performing better in most cases. A tendency to narrower confidence intervals from the nonparametric than parametric bootstrap is demonstrated in the application to high quantiles of distributions of observed maxima of 1- and 5-day precipitation amounts; the differences increase with the return level. The results show that estimation of uncertainty based on nonparametric bootstrap is highly unreliable, especially for small and moderate sample sizes and for very heavy-tailed data.
ERIC Educational Resources Information Center
Algina, James; Keselman, H. J.
2008-01-01
Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection. (Contains 2 tables.)
Palmer, Matthew A; Brewer, Neil; Weber, Nathan; Nagesh, Ambika
2013-03-01
Prior research points to a meaningful confidence-accuracy (CA) relationship for positive identification decisions. However, there are theoretical grounds for expecting that different aspects of the CA relationship (calibration, resolution, and over/underconfidence) might be undermined in some circumstances. This research investigated whether the CA relationship for eyewitness identification decisions is affected by three, forensically relevant variables: exposure duration, retention interval, and divided attention at encoding. In Study 1 (N = 986), a field experiment, we examined the effects of exposure duration (5 s vs. 90 s) and retention interval (immediate testing vs. a 1-week delay) on the CA relationship. In Study 2 (N = 502), we examined the effects of attention during encoding on the CA relationship by reanalyzing data from a laboratory experiment in which participants viewed a stimulus video under full or divided attention conditions and then attempted to identify two targets from separate lineups. Across both studies, all three manipulations affected identification accuracy. The central analyses concerned the CA relation for positive identification decisions. For the manipulations of exposure duration and retention interval, overconfidence was greater in the more difficult conditions (shorter exposure; delayed testing) than the easier conditions. Only the exposure duration manipulation influenced resolution (which was better for 5 s than 90 s), and only the retention interval manipulation affected calibration (which was better for immediate testing than delayed testing). In all experimental conditions, accuracy and diagnosticity increased with confidence, particularly at the upper end of the confidence scale. Implications for theory and forensic settings are discussed.
Jackson, Dan; Bowden, Jack; Baker, Rose
2015-12-01
Moment-based estimators of the between-study variance are very popular when performing random effects meta-analyses. This type of estimation has many advantages including computational and conceptual simplicity. Furthermore, by using these estimators in large samples, valid meta-analyses can be performed without the assumption that the treatment effects follow a normal distribution. Recently proposed moment-based confidence intervals for the between-study variance are exact under the random effects model but are quite elaborate. Here, we present a much simpler method for calculating approximate confidence intervals of this type. This method uses variance-stabilising transformations as its basis and can be used for a very wide variety of moment-based estimators in both the random effects meta-analysis and meta-regression models.
Harari, Gil
2014-01-01
Statistic significance, also known as p-value, and CI (Confidence Interval) are common statistics measures and are essential for the statistical analysis of studies in medicine and life sciences. These measures provide complementary information about the statistical probability and conclusions regarding the clinical significance of study findings. This article is intended to describe the methodologies, compare between the methods, assert their suitability for the different needs of study results analysis and to explain situations in which each method should be used.
Van Norman, Ethan R
2016-09-01
Curriculum-based measurement of oral reading (CBM-R) progress monitoring data is used to measure student response to instruction. Federal legislation permits educators to use CBM-R progress monitoring data as a basis for determining the presence of specific learning disabilities. However, decision making frameworks originally developed for CBM-R progress monitoring data were not intended for such high stakes assessments. Numerous documented issues with trend line estimation undermine the validity of using slope estimates to infer progress. One proposed recommendation is to use confidence interval overlap as a means of judging reliable growth. This project explored the degree to which confidence interval overlap was related to true growth magnitude using simulation methodology. True and observed CBM-R scores were generated across 7 durations of data collection (range 6-18 weeks), 3 levels of dataset quality or residual variance (5, 10, and 15 words read correct per minute) and 2 types of data collection schedules. Descriptive and inferential analyses were conducted to explore interactions between overlap status, progress monitoring scenarios, and true growth magnitude. A small but statistically significant interaction was observed between overlap status, duration, and dataset quality, b = -0.004, t(20992) =-7.96, p < .001. In general, confidence interval overlap does not appear to meaningfully account for variance in true growth across many progress monitoring conditions. Implications for research and practice are discussed. Limitations and directions for future research are addressed. (PsycINFO Database Record
NASA Astrophysics Data System (ADS)
Cameron, Ewan
2011-06-01
I present a critical review of techniques for estimating confidence intervals on binomial population proportions inferred from success counts in small to intermediate samples. Population proportions arise frequently as quantities of interest in astronomical research; for instance, in studies aiming to constrain the bar fraction, active galactic nucleus fraction, supermassive black hole fraction, merger fraction, or red sequence fraction from counts of galaxies exhibiting distinct morphological features or stellar populations. However, two of the most widely-used techniques for estimating binomial confidence intervals - the `normal approximation' and the Clopper & Pearson approach - are liable to misrepresent the degree of statistical uncertainty present under sampling conditions routinely encountered in astronomical surveys, leading to an ineffective use of the experimental data (and, worse, an inefficient use of the resources expended in obtaining that data). Hence, I provide here an overview of the fundamentals of binomial statistics with two principal aims: (i) to reveal the ease with which (Bayesian) binomial confidence intervals with more satisfactory behaviour may be estimated from the quantiles of the beta distribution using modern mathematical software packages (e.g. r, matlab, mathematica, idl, python); and (ii) to demonstrate convincingly the major flaws of both the `normal approximation' and the Clopper & Pearson approach for error estimation.
Lambert, Ronald J W; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M
2012-08-01
This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve.
Confidence Intervals for Squared Semipartial Correlation Coefficients: The Effect of Nonnormality
ERIC Educational Resources Information Center
Algina, James; Keselman, H. J.; Penfield, Randall D.
2010-01-01
The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…
ERIC Educational Resources Information Center
Cumming, Geoff
2010-01-01
This comment offers three descriptions of "p[subscript rep]" that start with a frequentist account of confidence intervals, draw on R. A. Fisher's fiducial argument, and do not make Bayesian assumptions. Links are described among "p[subscript rep]," "p" values, and the probability a confidence interval will capture…
Su, Ya-Hui; Sheu, Ching-Fan; Wang, Wen-Chung
2007-01-01
The item infit and outfit mean square errors (MSE) and their t-transformed statistics are widely used to screen poorly fitting items. The t-transformed statistics, however, do not follow the standard normal distribution so that hypothesis testing of item fit based on the conventional critical values is likely to be inaccurate (Wang and Chen, 2005). The MSE statistics are effect-size measures of misfit and have an expected value of unity when the data fit the model's expectation. Unfortunately, most computer programs for item response analysis do not report confidence intervals of the item infit and outfit MSE, mainly because their sampling distributions are analytically intractable. Hence, the user is left without interval estimates of the magnitudes of misfit. In this study, we developed a FORTRAN 90 computer program in conjunction with the commercial program WINSTEPS (Linacre, 2001) that yields confidence intervals of the item infit and outfit MSE using the bootstrap method. The utility of the program is demonstrated through three illustrations of simulated data sets.
Laud, Peter J; Dane, Aaron
2014-01-01
This paper uses graphical methods to illustrate and compare the coverage properties of a number of methods for calculating confidence intervals for the difference between two independent binomial proportions. We investigate both small-sample and large-sample properties of both two-sided and one-sided coverage, with an emphasis on asymptotic methods. In terms of aligning the smoothed coverage probability surface with the nominal confidence level, we find that the score-based methods on the whole have the best two-sided coverage, although they have slight deficiencies for confidence levels of 90% or lower. For an easily taught, hand-calculated method, the Brown-Li 'Jeffreys' method appears to perform reasonably well, and in most situations, it has better one-sided coverage than the widely recommended alternatives. In general, we find that the one-sided properties of many of the available methods are surprisingly poor. In fact, almost none of the existing asymptotic methods achieve equal coverage on both sides of the interval, even with large sample sizes, and consequently if used as a non-inferiority test, the type I error rate (which is equal to the one-sided non-coverage probability) can be inflated. The only exception is the Gart-Nam 'skewness-corrected' method, which we express using modified notation in order to include a bias correction for improved small-sample performance, and an optional continuity correction for those seeking more conservative coverage. Using a weighted average of two complementary methods, we also define a new hybrid method that almost matches the performance of the Gart-Nam interval.
Paula, T O M; Marinho, C D; Amaral Júnior, A T; Peternelli, L A; Gonçalves, L S A
2013-06-27
The objective of this study was to determine the optimal number of repetitions to be used in competition trials of popcorn traits related to production and quality, including grain yield and expansion capacity. The experiments were conducted in 3 environments representative of the north and northwest regions of the State of Rio de Janeiro with 10 Brazilian genotypes of popcorn, consisting by 4 commercial hybrids (IAC 112, IAC 125, Zélia, and Jade), 4 improved varieties (BRS Ângela, UFVM-2 Barão de Viçosa, Beija-flor, and Viçosa) and 2 experimental populations (UNB2U-C3 and UNB2U-C4). The experimental design utilized was a randomized complete block design with 7 repetitions. The Bootstrap method was employed to obtain samples of all of the possible combinations within the 7 blocks. Subsequently, the confidence intervals of the parameters of interest were calculated for all simulated data sets. The optimal number of repetition for all of the traits was considered when all of the estimates of the parameters in question were encountered within the confidence interval. The estimates of the number of repetitions varied according to the parameter estimated, variable evaluated, and environment cultivated, ranging from 2 to 7. It is believed that only the expansion capacity traits in the Colégio Agrícola environment (for residual variance and coefficient of variation), and number of ears per plot, in the Itaocara environment (for coefficient of variation) needed 7 repetitions to fall within the confidence interval. Thus, for the 3 studies conducted, we can conclude that 6 repetitions are optimal for obtaining high experimental precision.
McLoughlin, Kevin
2016-01-11
This report describes the design and implementation of an algorithm for estimating relative microbial abundances, together with confidence limits, using data from metagenomic DNA sequencing. For the background behind this project and a detailed discussion of our modeling approach for metagenomic data, we refer the reader to our earlier technical report, dated March 4, 2014. Briefly, we described a fully Bayesian generative model for paired-end sequence read data, incorporating the effects of the relative abundances, the distribution of sequence fragment lengths, fragment position bias, sequencing errors and variations between the sampled genomes and the nearest reference genomes. A distinctive feature of our modeling approach is the use of a Chinese restaurant process (CRP) to describe the selection of genomes to be sampled, and thus the relative abundances. The CRP component is desirable for fitting abundances to reads that may map ambiguously to multiple targets, because it naturally leads to sparse solutions that select the best representative from each set of nearly equivalent genomes.
Landes, Reid D; Lensing, Shelly Y; Kodell, Ralph L; Hauer-Jensen, Martin
2013-12-01
The dose of a substance that causes death in P% of a population is called an LDP, where LD stands for lethal dose. In radiation research, a common LDP of interest is the radiation dose that kills 50% of the population by a specified time, i.e., lethal dose 50 or LD50. When comparing LD50 between two populations, relative potency is the parameter of interest. In radiation research, this is commonly known as the dose reduction factor (DRF). Unfortunately, statistical inference on dose reduction factor is seldom reported. We illustrate how to calculate confidence intervals for dose reduction factor, which may then be used for statistical inference. Further, most dose reduction factor experiments use hundreds, rather than tens of animals. Through better dosing strategies and the use of a recently available sample size formula, we also show how animal numbers may be reduced while maintaining high statistical power. The illustrations center on realistic examples comparing LD50 values between a radiation countermeasure group and a radiation-only control. We also provide easy-to-use spreadsheets for sample size calculations and confidence interval calculations, as well as SAS® and R code for the latter.
Landes, Reid D.; Lensing, Shelly Y.; Kodell, Ralph L.; Hauer-Jensen, Martin
2014-01-01
The dose of a substance that causes death in P% of a population is called an LDP, where LD stands for lethal dose. In radiation research, a common LDP of interest is the radiation dose that kills 50% of the population by a specified time, i.e., lethal dose 50 or LD50. When comparing LD50 between two populations, relative potency is the parameter of interest. In radiation research, this is commonly known as the dose reduction factor (DRF). Unfortunately, statistical inference on dose reduction factor is seldom reported. We illustrate how to calculate confidence intervals for dose reduction factor, which may then be used for statistical inference. Further, most dose reduction factor experiments use hundreds, rather than tens of animals. Through better dosing strategies and the use of a recently available sample size formula, we also show how animal numbers may be reduced while maintaining high statistical power. The illustrations center on realistic examples comparing LD50 values between a radiation countermeasure group and a radiation-only control. We also provide easy-to-use spreadsheets for sample size calculations and confidence interval calculations, as well as SAS® and R code for the latter. PMID:24164553
Matzke, Melissa M; Allan, Sarah E; Anderson, Kim A; Waters, Katrina M
2012-12-01
The use of passive sampling devices (PSDs) for monitoring hydrophobic organic contaminants in aquatic environments can entail logistical constraints that often limit a comprehensive statistical sampling plan, thus resulting in a restricted number of samples. The present study demonstrates an approach for using the results of a pilot study designed to estimate sampling variability, which in turn can be used as variance estimates for confidence intervals for future n = 1 PSD samples of the same aquatic system. Sets of three to five PSDs were deployed in the Portland Harbor Superfund site for three sampling periods over the course of two years. The PSD filters were extracted and, as a composite sample, analyzed for 33 polycyclic aromatic hydrocarbon compounds. The between-sample and within-sample variances were calculated to characterize sources of variability in the environment and sampling methodology. A method for calculating a statistically reliable and defensible confidence interval for the mean of a single aquatic passive sampler observation (i.e., n = 1) using an estimate of sample variance derived from a pilot study is presented. Coverage probabilities are explored over a range of variance values using a Monte Carlo simulation.
Laubender, Ruediger P; Bender, Ralf
2014-02-28
Recently, Laubender and Bender (Stat. Med. 2010; 29: 851-859) applied the average risk difference (RD) approach to estimate adjusted RD and corresponding number needed to treat measures in the Cox proportional hazards model. We calculated standard errors and confidence intervals by using bootstrap techniques. In this paper, we develop asymptotic variance estimates of the adjusted RD measures and corresponding asymptotic confidence intervals within the counting process theory and evaluated them in a simulation study. We illustrate the use of the asymptotic confidence intervals by means of data of the Düsseldorf Obesity Mortality Study.
Pradhan, Vivek; Saha, Krishna K; Banerjee, Tathagata; Evans, John C
2014-07-30
Inference on the difference between two binomial proportions in the paired binomial setting is often an important problem in many biomedical investigations. Tang et al. (2010, Statistics in Medicine) discussed six methods to construct confidence intervals (henceforth, we abbreviate it as CI) for the difference between two proportions in paired binomial setting using method of variance estimates recovery. In this article, we propose weighted profile likelihood-based CIs for the difference between proportions of a paired binomial distribution. However, instead of the usual likelihood, we use weighted likelihood that is essentially making adjustments to the cell frequencies of a 2 × 2 table in the spirit of Agresti and Min (2005, Statistics in Medicine). We then conduct numerical studies to compare the performances of the proposed CIs with that of Tang et al. and Agresti and Min in terms of coverage probabilities and expected lengths. Our numerical study clearly indicates that the weighted profile likelihood-based intervals and Jeffreys interval (cf. Tang et al.) are superior in terms of achieving the nominal level, and in terms of expected lengths, they are competitive. Finally, we illustrate the use of the proposed CIs with real-life examples.
NASA Astrophysics Data System (ADS)
Christensen, Steen
2017-02-01
This paper derives and tests methods to correct regression-based confidence and prediction intervals for groundwater models that neglect sub-parameterization heterogeneity within the hydraulic property fields of the groundwater system. Several levels of knowledge and uncertainty about the system are considered. It is shown by a two-dimensional groundwater flow example that when reliable probabilistic models are available for the property fields, the corrected confidence and prediction intervals are nearly accurate; when the probabilistic models must be suggested from subjective judgment, the corrected confidence intervals are likely to be much more accurate than their uncorrected counterparts; when no probabilistic information is available then conservative bound values can be used to correct the intervals but they are likely to be very wide. The paper also shows how confidence and prediction intervals can be computed and corrected when the weights applied to the data are estimated as part of the regression. It is demonstrated that in this case it cannot be guaranteed that applying the conservative bound values will lead to conservative confidence and prediction intervals. Finally, it is demonstrated by the two-dimensional flow example that the accuracy of the corrected confidence and prediction intervals deteriorates for very large covariance of the log-transmissivity field, and particularly when the weight matrix differs from the inverse total error covariance matrix. It is argued that such deterioration is less likely to happen for three-dimensional groundwater flow systems.
variable Y having mean m and variance S sub y. It is desired to find a fixed-width confidence interval of width 2d for m having probability of...coverage g. Assuming that the cost for taking observations in the same for both measuring instruments, the problem becomes one of finding the most accurate measuring instrument and using it to construct the confidence interval .
Abd-Elfattah, Ehab F
2012-04-01
The randomization design used to collect the data provides basis for the exact distributions of the permutation tests. The truncated binomial design is one of the commonly used designs for forcing balance in clinical trials to eliminate experimental bias. In this article, we consider the exact distribution of the weighted log-rank class of tests for censored data under the truncated binomial design. A double saddlepoint approximation for p-values of this class is derived under the truncated binomial design. The speed and accuracy of the saddlepoint approximation over the normal asymptotic facilitate the inversion of the weighted log-rank tests to determine nominal 95% confidence intervals for treatment effect with right censored data.
Liu, Xiaofeng Steven
2011-05-01
The use of covariates is commonly believed to reduce the unexplained error variance and the standard error for the comparison of treatment means, but the reduction in the standard error is neither guaranteed nor uniform over different sample sizes. The covariate mean differences between the treatment conditions can inflate the standard error of the covariate-adjusted mean difference and can actually produce a larger standard error for the adjusted mean difference than that for the unadjusted mean difference. When the covariate observations are conceived of as randomly varying from one study to another, the covariate mean differences can be related to a Hotelling's T(2) . Using this Hotelling's T(2) statistic, one can always find a minimum sample size to achieve a high probability of reducing the standard error and confidence interval width for the adjusted mean difference.
Peng, Jianan; Liu, Wei; Bretz, Frank; Shkedy, Ziv
2016-12-26
Benjamini and Yekutieli () introduced the concept of the false coverage-statement rate (FCR) to account for selection when the confidence intervals (CIs) are constructed only for the selected parameters. Dose-response analysis in dose-response microarray experiments is conducted only for genes having monotone dose-response relationship, which is a selection problem. In this paper, we consider multiple CIs for the mean gene expression difference between the highest dose and control in monotone dose-response microarray experiments for selected parameters adjusted for the FCR. A simulation study is conducted to study the performance of the method proposed. The method is applied to a real dose-response microarray experiment with 16, 998 genes for illustration.
Preston, Dale L.; Sokolnikov, Mikhail; Napier, Bruce A.; Degteva, Marina; Moroz, Brian; Vostrotin, Vadim; Shiskina, Elena; Birchall, Alan; Stram, Daniel O.
2017-01-01
In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures. While the effects of independent measurement errors on parameter estimation and methods to correct these effects have been studied comprehensively in the epidemiological literature, the literature on the effects of correlated errors, and associated correction methods is much more sparse. In this paper, we implement a novel method that calculates corrected confidence intervals based on the approximate asymptotic distribution of parameter estimates in linear excess relative risk (ERR) models. These models are widely used in survival analysis, particularly in radiation epidemiology. Specifically, for the dose effect estimate of interest (increase in relative risk per unit dose), a mixture distribution consisting of a normal and a lognormal component is applied. This choice of asymptotic approximation guarantees that corrected confidence intervals will always be bounded, a result which does not hold under a normal approximation. A simulation study was conducted to evaluate the proposed method in survival analysis using a realistic ERR model. We used both simulated Monte Carlo dosimetry systems (MCDS) and actual dose histories from the Mayak Worker Dosimetry System 2013, a MCDS for plutonium exposures in the Mayak Worker Cohort. Results show our proposed methods provide much improved coverage probabilities for the dose effect parameter, and noticeable improvements for other model parameters. PMID:28369141
Tang, Man-Lai; Tang, Nian-Sheng; Carey, Vincent J
2004-06-01
In this article, we consider problems with correlated data that can be summarized in a 2 x 2 table with structural zero in one of the off-diagonal cells. Data of this kind sometimes appear in infectious disease studies and two-step procedure studies. Lui (1998, Biometrics54, 706-711) considered confidence interval estimation of rate ratio based on Fieller-type, Wald-type, and logarithmic transformation statistics. We reexamine the same problem under the context of confidence interval construction on false-negative rate ratio in diagnostic performance when combining two diagnostic tests. We propose a score statistic for testing the null hypothesis of nonunity false-negative rate ratio. Score test-based confidence interval construction for false-negative rate ratio will also be discussed. Simulation studies are conducted to compare the performance of the new derived score test statistic and existing statistics for small to moderate sample sizes. In terms of confidence interval construction, our asymptotic score test-based confidence interval estimator possesses significantly shorter expected width with coverage probability being close to the anticipated confidence level. In terms of hypothesis testing, our asymptotic score test procedure has actual type I error rate close to the pre-assigned nominal level. We illustrate our methodologies with real examples from a clinical laboratory study and a cancer study.
NASA Astrophysics Data System (ADS)
AlHakeem, Donna Ibrahim
This thesis focuses on short-term photovoltaic forecasting (STPVF) for the power generation of a solar PV system using probabilistic forecasts and deterministic forecasts. Uncertainty estimation, in the form of a probabilistic forecast, is emphasized in this thesis to quantify the uncertainties of the deterministic forecasts. Two hybrid intelligent models are proposed in two separate chapters to perform the STPVF. In Chapter 4, the framework of the deterministic proposed hybrid intelligent model is presented, which is a combination of wavelet transform (WT) that is a data filtering technique and a soft computing model (SCM) that is generalized regression neural network (GRNN). Additionally, this chapter proposes a model that is combined as WT+GRNN and is utilized to conduct the forecast of two random days in each season for 1-hour-ahead to find the power generation. The forecasts are analyzed utilizing accuracy measures equations to determine the model performance and compared with another SCM. In Chapter 5, the framework of the proposed model is presented, which is a combination of WT, a SCM based on radial basis function neural network (RBFNN), and a population-based stochastic particle swarm optimization (PSO). Chapter 5 proposes a model combined as a deterministic approach that is represented as WT+RBFNN+PSO, and then a probabilistic forecast is conducted utilizing bootstrap confidence intervals to quantify uncertainty from the output of WT+RBFNN+PSO. In Chapter 5, the forecasts are conducted by furthering the tests done in Chapter 4. Chapter 5 forecasts the power generation of two random days in each season for 1-hour-ahead, 3-hour-ahead, and 6-hour-ahead. Additionally, different types of days were also forecasted in each season such as a sunny day (SD), cloudy day (CD), and a rainy day (RD). These forecasts were further analyzed using accuracy measures equations, variance and uncertainty estimation. The literature that is provided supports that the proposed
Feng, Dai; Svetnik, Vladimir; Coimbra, Alexandre; Baumgartner, Richard
2014-01-01
The intraclass correlation coefficient (ICC) with fixed raters or, equivalently, the concordance correlation coefficient (CCC) for continuous outcomes is a widely accepted aggregate index of agreement in settings with small number of raters. Quantifying the precision of the CCC by constructing its confidence interval (CI) is important in early drug development applications, in particular in qualification of biomarker platforms. In recent years, there have been several new methods proposed for construction of CIs for the CCC, but their comprehensive comparison has not been attempted. The methods consisted of the delta method and jackknifing with and without Fisher's Z-transformation, respectively, and Bayesian methods with vague priors. In this study, we carried out a simulation study, with data simulated from multivariate normal as well as heavier tailed distribution (t-distribution with 5 degrees of freedom), to compare the state-of-the-art methods for assigning CI to the CCC. When the data are normally distributed, the jackknifing with Fisher's Z-transformation (JZ) tended to provide superior coverage and the difference between it and the closest competitor, the Bayesian method with the Jeffreys prior was in general minimal. For the nonnormal data, the jackknife methods, especially the JZ method, provided the coverage probabilities closest to the nominal in contrast to the others which yielded overly liberal coverage. Approaches based upon the delta method and Bayesian method with conjugate prior generally provided slightly narrower intervals and larger lower bounds than others, though this was offset by their poor coverage. Finally, we illustrated the utility of the CIs for the CCC in an example of a wake after sleep onset (WASO) biomarker, which is frequently used in clinical sleep studies of drugs for treatment of insomnia.
García-Pérez, Miguel A.; Alcalá-Quintana, Rocío
2016-01-01
Hoekstra et al. (Psychonomic Bulletin & Review, 2014, 21:1157–1164) surveyed the interpretation of confidence intervals (CIs) by first-year students, master students, and researchers with six items expressing misinterpretations of CIs. They asked respondents to answer all items, computed the number of items endorsed, and concluded that misinterpretation of CIs is robust across groups. Their design may have produced this outcome artifactually for reasons that we describe. This paper discusses first the two interpretations of CIs and, hence, why misinterpretation cannot be inferred from endorsement of some of the items. Next, a re-analysis of Hoekstra et al.'s data reveals some puzzling differences between first-year and master students that demand further investigation. For that purpose, we designed a replication study with an extended questionnaire including two additional items that express correct interpretations of CIs (to compare endorsement of correct vs. nominally incorrect interpretations) and we asked master students to indicate which items they would have omitted had they had the option (to distinguish deliberate from uninformed endorsement caused by the forced-response format). Results showed that incognizant first-year students endorsed correct and nominally incorrect items identically, revealing that the two item types are not differentially attractive superficially; in contrast, master students were distinctively more prone to endorsing correct items when their uninformed responses were removed, although they admitted to nescience more often that might have been expected. Implications for teaching practices are discussed. PMID:27458424
Lumme, Sonja; Sund, Reijo; Leyland, Alastair H; Keskimäki, Ilmo
In this paper, we introduce several statistical methods to evaluate the uncertainty in the concentration index (C) for measuring socioeconomic equality in health and health care using aggregated total population register data. The C is a widely used index when measuring socioeconomic inequality, but previous studies have mainly focused on developing statistical inference for sampled data from population surveys. While data from large population-based or national registers provide complete coverage, registration comprises several sources of error. We simulate confidence intervals for the C with different Monte Carlo approaches, which take into account the nature of the population data. As an empirical example, we have an extensive dataset from the Finnish cause-of-death register on mortality amenable to health care interventions between 1996 and 2008. Amenable mortality has been often used as a tool to capture the effectiveness of health care. Thus, inequality in amenable mortality provides evidence on weaknesses in health care performance between socioeconomic groups. Our study shows using several approaches with different parametric assumptions that previously introduced methods to estimate the uncertainty of the C for sampled data are too conservative for aggregated population register data. Consequently, we recommend that inequality indices based on the register data should be presented together with an approximation of the uncertainty and suggest using a simulation approach we propose. The approach can also be adapted to other measures of equality in health.
Kim, Seonjin; Zhao, Zhibiao; Shao, Xiaofeng
2015-01-01
This paper is concerned with the inference of nonparametric mean function in a time series context. The commonly used kernel smoothing estimate is asymptotically normal and the traditional inference procedure then consistently estimates the asymptotic variance function and relies upon normal approximation. Consistent estimation of the asymptotic variance function involves another level of nonparametric smoothing. In practice, the choice of the extra bandwidth parameter can be difficult, the inference results can be sensitive to bandwidth selection and the normal approximation can be quite unsatisfactory in small samples leading to poor coverage. To alleviate the problem, we propose to extend the recently developed self-normalized approach, which is a bandwidth free inference procedure developed for parametric inference, to construct point-wise confidence interval for nonparametric mean function. To justify asymptotic validity of the self-normalized approach, we establish a functional central limit theorem for recursive nonparametric mean regression function estimates under primitive conditions and show that the limiting process is a Gaussian process with non-stationary and dependent increments. The superior finite sample performance of the new approach is demonstrated through simulation studies.
NASA Astrophysics Data System (ADS)
Tada, A.; Tanakamaru, H.
2008-12-01
Total mass flux (load) from a catchment is a basic factor in evaluating chemical weathering or in TMDLs implementation. So far, many combinations of load estimation methods with sampling strategies were tested to obtain an unbiased flux estimate. To utilize such flux estimates in the political or scientific application, the information of uncertainty of flux estimates should also be provided. Giving the interval estimate of total flux may be a desirable solution to this situation. Total solute flux from a small, undisturbed forested catchment (12.8ha) during 10 months were calculated based on high-temporal resolution data and used in validation of 95% confidence intervals (CIs) of flux estimates. Water quality data (sodium, potassium, and chloride concentration) were collected and measured every 15 minutes during 10 months in 2004 by the on-site monitoring system using FIP (flow injection potentiometry) method with ion-selective electrodes. Water quantity data (the flow rate data) were measured continuously by V-notch weir at the catchment outlet. Flux estimates and 95% CIs were calculated for three indices with 41 methods; sample average, flow- weighted average, the Beale ratio estimator, rating curve method with simple linear regression between flux and the flow rate, and nine regression models in the USGS Load Estimator (Loadest). Smearing estimates, MVUE estimates, and estimates by composite method were also evaluated concerning nine regression models in Load Estimator. Two sampling strategies were tested; periodical sampling (daily and weekly) and flow stratified sampling. After data were sorted in ascending order of the flow rate, five strata were configured so that each stratum contained same number of data in flow stratified sampling. The performance of these 95% CIs was evaluated by the rate of inclusion of true flux value within these CIs, which should be expected as 0.95. A simple bootstrap method was adopted to construct the CIs with 2,000 bootstrap
Parks, Nathan A.; Gannon, Matthew A.; Long, Stephanie M.; Young, Madeleine E.
2016-01-01
Analysis of event-related potential (ERP) data includes several steps to ensure that ERPs meet an appropriate level of signal quality. One such step, subject exclusion, rejects subject data if ERP waveforms fail to meet an appropriate level of signal quality. Subject exclusion is an important quality control step in the ERP analysis pipeline as it ensures that statistical inference is based only upon those subjects exhibiting clear evoked brain responses. This critical quality control step is most often performed simply through visual inspection of subject-level ERPs by investigators. Such an approach is qualitative, subjective, and susceptible to investigator bias, as there are no standards as to what constitutes an ERP of sufficient signal quality. Here, we describe a standardized and objective method for quantifying waveform quality in individual subjects and establishing criteria for subject exclusion. The approach uses bootstrap resampling of ERP waveforms (from a pool of all available trials) to compute a signal-to-noise ratio confidence interval (SNR-CI) for individual subject waveforms. The lower bound of this SNR-CI (SNRLB) yields an effective and objective measure of signal quality as it ensures that ERP waveforms statistically exceed a desired signal-to-noise criterion. SNRLB provides a quantifiable metric of individual subject ERP quality and eliminates the need for subjective evaluation of waveform quality by the investigator. We detail the SNR-CI methodology, establish the efficacy of employing this approach with Monte Carlo simulations, and demonstrate its utility in practice when applied to ERP datasets. PMID:26903849
NASA Astrophysics Data System (ADS)
Sugiura, T.; Hirata, H.; Hand, J. W.; van Leeuwen, J. M. J.; Mizushina, S.
2011-10-01
Clinical trials of hypothermic brain treatment for newborn babies are currently hindered by the difficulty in measuring deep brain temperatures. As one of the possible methods for noninvasive and continuous temperature monitoring that is completely passive and inherently safe is passive microwave radiometry (MWR). We have developed a five-band microwave radiometer system with a single dual-polarized, rectangular waveguide antenna operating within the 1-4 GHz range and a method for retrieving the temperature profile from five radiometric brightness temperatures. This paper addresses (1) the temperature calibration for five microwave receivers, (2) the measurement experiment using a phantom model that mimics the temperature profile in a newborn baby, and (3) the feasibility for noninvasive monitoring of deep brain temperatures. Temperature resolutions were 0.103, 0.129, 0.138, 0.105 and 0.111 K for 1.2, 1.65, 2.3, 3.0 and 3.6 GHz receivers, respectively. The precision of temperature estimation (2σ confidence interval) was about 0.7°C at a 5-cm depth from the phantom surface. Accuracy, which is the difference between the estimated temperature using this system and the measured temperature by a thermocouple at a depth of 5 cm, was about 2°C. The current result is not satisfactory for clinical application because the clinical requirement for accuracy must be better than 1°C for both precision and accuracy at a depth of 5 cm. Since a couple of possible causes for this inaccuracy have been identified, we believe that the system can take a step closer to the clinical application of MWR for hypothermic rescue treatment.
Raykov, Tenko; Zinbarg, Richard E
2011-05-01
A confidence interval construction procedure for the proportion of explained variance by a hierarchical, general factor in a multi-component measuring instrument is outlined. The method provides point and interval estimates for the proportion of total scale score variance that is accounted for by the general factor, which could be viewed as common to all components. The approach may also be used for testing composite (one-tailed) or simple hypotheses about this proportion, and is illustrated with a pair of examples.
ERIC Educational Resources Information Center
Simpson, Mary Ann; Gong, Brian; Marion, Scott
2006-01-01
This study addresses three questions: First, considering the full group of students and the special education subgroup, what is the likely effect of minimum cell size and confidence interval size on school-level Adequate Yearly Progress (AYP) determinations? Second, what effects do the changing minimum cell sizes have on inclusion of special…
ERIC Educational Resources Information Center
Romano, Jeanine L.; Kromrey, Jeffrey D.; Owens, Corina M.; Scott, Heather M.
2011-01-01
In this study, the authors aimed to examine 8 of the different methods for computing confidence intervals around alpha that have been proposed to determine which of these, if any, is the most accurate and precise. Monte Carlo methods were used to simulate samples under known and controlled population conditions wherein the underlying item…
Technology Transfer Automated Retrieval System (TEKTRAN)
In this work, we address uncertainty analysis for a model, presented in a companion paper, quantifying the effect of soil moisture and plant development on soybean (Glycine max (L.) Merr.) leaf conductance. To achieve this we present several methods for confidence interval estimation. Estimation ...
Hodgkins, Glenn A.; Stewart, Gregory J.; Cohn, Timothy A.; Dudley, Robert W.
2007-01-01
Large amounts of rain fell on southern Maine from the afternoon of April 15, 2007, to the afternoon of April 16, 2007, causing substantial damage to houses, roads, and culverts. This report provides an estimate of the peak flows on two rivers in southern Maine--the Mousam River and the Little Ossipee River--because of their severe flooding. The April 2007 estimated peak flow of 9,230 ft3/s at the Mousam River near West Kennebunk had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 25 years to greater than 500 years. The April 2007 estimated peak flow of 8,220 ft3/s at the Little Ossipee River near South Limington had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 50 years to greater than 500 years.
ERIC Educational Resources Information Center
Palmer, Matthew A.; Brewer, Neil; Weber, Nathan; Nagesh, Ambika
2013-01-01
Prior research points to a meaningful confidence-accuracy (CA) relationship for positive identification decisions. However, there are theoretical grounds for expecting that different aspects of the CA relationship (calibration, resolution, and over/underconfidence) might be undermined in some circumstances. This research investigated whether the…
ERIC Educational Resources Information Center
Paek, Insu
2016-01-01
The effect of guessing on the point estimate of coefficient alpha has been studied in the literature, but the impact of guessing and its interactions with other test characteristics on the interval estimators for coefficient alpha has not been fully investigated. This study examined the impact of guessing and its interactions with other test…
NASA Astrophysics Data System (ADS)
Yan, W. M.; Yuen, Ka-Veng
2015-01-01
Blast-induced ground vibration has received much engineering and public attention. The vibration is often represented by the peak particle velocity (PPV) and the empirical approach is employed to describe the relationship between the PPV and the scaled distance. Different statistical methods are often used to obtain the confidence level of the prediction. With a known scaled distance, the amount of explosives in a planned blast can then be determined by a blast engineer when the PPV limit and the confidence level of the vibration magnitude are specified. This paper shows that these current approaches do not incorporate the posterior uncertainty of the fitting coefficients. In order to resolve this problem, a Bayesian method is proposed to derive the site-specific fitting coefficients based on a small amount of data collected at an early stage of a blasting project. More importantly, uncertainty of both the fitting coefficients and the design formula can be quantified. Data collected from a site formation project in Hong Kong is used to illustrate the performance of the proposed method. It is shown that the proposed method resolves the underestimation problem in one of the conventional approaches. The proposed approach can be easily conducted using spreadsheet calculation without the need for any additional tools, so it will be particularly welcome by practicing engineers.
NASA Technical Reports Server (NTRS)
Murphy, P. C.
1986-01-01
An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates.
NASA Astrophysics Data System (ADS)
Ogren, Paul; Davis, Brian; Guy, Nick
2001-06-01
A spreadsheet approach is used to fit multilinear functions with three adjustable parameters: ƒ = a1X1(x) + a2X2(x) + a3X3(x). Results are illustrated for three familiar examples: IR analysis of gaseous DCl, the electronic/vibrational spectrum of gaseous I2, and van Deemter plots of chromatographic data. These cases are simple enough for students in upper-level physical or advanced analytical courses to write and modify their own spreadsheets. In addition to the original x, y, and sy values, 12 columns are required: three for Xn(xi) values, six for Xn(xi)Xk(xi) product sums for the curvature matrix [a], and three for yi Xn(xi) sums for (b) in the vector equation (b) = [a](a). The Excel spreadsheet MINVERSE function provides the [e] error matrix from [a]. The [e] elements are then used to determine best-fit parameter values contained in (a). These spreadsheets also use a "dimensionless" or "reduced parameter" approach in calculating parameter weights, uncertainties, and correlations. Students can later enter data sets and fit parameters into a larger spreadsheet that uses Monte Carlo techniques to produce two-dimensional scatter plots. These correspond to Dc2 ellipsoidal cross-sections or projections and provide visual depictions of parameter uncertainties and correlations. The Monte Carlo results can also be used to estimate confidence envelopes for fitting plots.
Franz, Volker H; Loftus, Geoffrey R
2012-06-01
Repeated measures designs are common in experimental psychology. Because of the correlational structure in these designs, the calculation and interpretation of confidence intervals is nontrivial. One solution was provided by Loftus and Masson (Psychonomic Bulletin & Review 1:476-490, 1994). This solution, although widely adopted, has the limitation of implying same-size confidence intervals for all factor levels, and therefore does not allow for the assessment of variance homogeneity assumptions (i.e., the circularity assumption, which is crucial for the repeated measures ANOVA). This limitation and the method's perceived complexity have sometimes led scientists to use a simplified variant, based on a per-subject normalization of the data (Bakeman & McArthur, Behavior Research Methods, Instruments, & Computers 28:584-589, 1996; Cousineau, Tutorials in Quantitative Methods for Psychology 1:42-45, 2005; Morey, Tutorials in Quantitative Methods for Psychology 4:61-64, 2008; Morrison & Weaver, Behavior Research Methods, Instruments, & Computers 27:52-56, 1995). We show that this normalization method leads to biased results and is uninformative with regard to circularity. Instead, we provide a simple, intuitive generalization of the Loftus and Masson method that allows for assessment of the circularity assumption.
Christensen, Steen; Cooley, Richard L.
2006-01-01
This report introduces and documents the Uncertainty (UNC) Process, a new Process in MODFLOW-2000 that calculates uncertainty measures for model parameters and for predictions produced by the model. Uncertainty measures can be computed by various methods, but when regression is applied to calibrate a model (for example when using the Parameter-Estimation Process of MODFLOW-2000) it is advantageous to also use regression-based methods to quantify uncertainty. For this reason the UNC Process computes (1) confidence intervals for parameters of the Parameter-Estimation Process and (2) confidence and prediction intervals for most types of functions that can be computed by a MODFLOW-2000 model calibrated by the Parameter-Estimation Process. The types of functions for which the Process works include hydraulic heads, hydraulic head differences, head-dependent flows computed by the head-dependent flow packages for drains (DRN6), rivers (RIV6), general-head boundaries (GHB6), streams (STR6), drain-return cells (DRT1), and constant-head boundaries (CHD), and for differences between flows computed by any of the mentioned flow packages. The UNC Process does not allow computation of intervals for the difference between flows computed by two different flow packages. The report also documents three programs, RESAN2-2k, BEALE2-2k, and CORFAC-2k, which are valuable for the evaluation of results from the Parameter-Estimation Process and for the preparation of input values for the UNC Process. RESAN2-2k and BEALE2-2k are significant updates of the residual analysis and modified Beale's measure programs first published by Cooley and Naff (1990) and later modified for use with MODFLOWP (Hill, 1994) and MODFLOW-2000 (Hill and others, 2000). CORFAC-2k is a new program that computes correction factors to be used by UNC.
Conny, J M; Norris, G A; Gould, T R
2009-03-09
Thermal-optical transmission (TOT) analysis measures black carbon (BC) in atmospheric aerosol on a fibrous filter. The method pyrolyzes organic carbon (OC) and employs laser light absorption to distinguish BC from the pyrolyzed OC; however, the instrument does not necessarily separate the two physically. In addition, a comprehensive temperature protocol for the analysis based on the Beer-Lambert Law remains elusive. Here, empirical response-surface modeling was used to show how the temperature protocol in TOT analysis can be modified to distinguish pyrolyzed OC from BC based on the Beer-Lambert Law. We determined the apparent specific absorption cross sections for pyrolyzed OC (sigma(Char)) and BC (sigma(BC)), which accounted for individual absorption enhancement effects within the filter. Response-surface models of these cross sections were derived from a three-factor central-composite factorial experimental design: temperature and duration of the high-temperature step in the helium phase, and the heating increase in the helium-oxygen phase. The response surface for sigma(BC), which varied with instrument conditions, revealed a ridge indicating the correct conditions for OC pyrolysis in helium. The intersection of the sigma(BC) and sigma(Char) surfaces indicated the conditions where the cross sections were equivalent, satisfying an important assumption upon which the method relies. 95% confidence interval surfaces defined a confidence region for a range of pyrolysis conditions. Analyses of wintertime samples from Seattle, WA revealed a temperature between 830 degrees C and 850 degrees C as most suitable for the helium high-temperature step lasting 150s. However, a temperature as low as 750 degrees C could not be rejected statistically.
ROMERO,VICENTE J.
2000-05-04
In order to devise an algorithm for autonomously terminating Monte Carlo sampling when sufficiently small and reliable confidence intervals (CI) are achieved on calculated probabilities, the behavior of CI estimators must be characterized. This knowledge is also required in comparing the accuracy of other probability estimation techniques to Monte Carlo results. Based on 100 trials in a hypothesis test, estimated 95% CI from classical approximate CI theory are empirically examined to determine if they behave as true 95% CI over spectrums of probabilities (population proportions) ranging from 0.001 to 0.99 in a test problem. Tests are conducted for population sizes of 500 and 10,000 samples where applicable. Significant differences between true and estimated 95% CI are found to occur at probabilities between 0.1 and 0.9, such that estimated 95% CI can be rejected as not being true 95% CI at less than a 40% chance of incorrect rejection. With regard to Latin Hypercube sampling (LHS), though no general theory has been verified for accurately estimating LHS CI, recent numerical experiments on the test problem have found LHS to be conservatively over an order of magnitude more efficient than SRS for similar sized CI on probabilities ranging between 0.25 and 0.75. The efficiency advantage of LHS vanishes, however, as the probability extremes of 0 and 1 are approached.
Usuda, K; Kono, K; Dote, T; Miyata, K; Nishiura, H; Shimahara, M; Sugimoto, K
1998-09-04
A simple and rapid method for the determination of urine boron by inductively-coupled plasma argon emission spectrometry (ICPAES) has been developed to establish boron exposure guidelines. After 11-fold dilution in 18.25 M omega cm ultra-pure water and vigorous shaking, urine may be directly injected into the spectrometer, providing accurate and reproducible results. We report the results obtained with urine samples obtained from a group of male Japanese electronic workers (n = 102) who had not been exposed to boron; boron concentrations were corrected with use of a specific gravity of 1.024 g/ml. The frequency distribution resulted in a log-normal distribution diagram for anatomical spread. The geometric mean values for urine boron in the non-exposed workers was 798.0 micrograms/l, while the confidence interval (C.I.) was between 398.1 and 1599.6 micrograms/l. Taking into consideration the short biological half-life of boron and its major excretion route via urine, urine was considered to be a suitable means for monitoring of exposure to this element. We conclude that the guidelines established by determining boron reference values are useful for the protection of individuals exposed to boron in their working environments.
Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A
2015-05-01
Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories.
2010-06-01
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Approximating Confidence Intervals for Factor Loadings.
ERIC Educational Resources Information Center
Lambert, Zarrel V.; And Others
1991-01-01
A method is presented that eliminates some interpretational limitations arising from assumptions implicit in the use of arbitrary rules of thumb to interpret exploratory factor analytic results. The bootstrap method is presented as a way of approximating sampling distributions of estimated factor loadings. Simulated datasets illustrate the…
NASA Astrophysics Data System (ADS)
Roederer, Juan G.
Many conferences are being held on confidence building in many countries. Usually they are organized and attended by political scientists and science policy specialists. A remarkable exception, in which the main brainstorming was done by “grass roots” geophysicists, nuclear physicists, engineers and ecologists, was a meeting in July at St. John's College in Santa Fe, N. Mex.The aim of the conference Technology-Based Confidence Building: Energy and Environment was to survey programs of international cooperation in pertinent areas of mutual concern to all nations and to identify new initiatives that could contribute to enhanced international stability, with emphasis on cooperation between the U.S. and U.S.S.R.
Roland, Mark A.; Stuckey, Marla H.
2008-01-01
Regression equations were developed for estimating flood flows at selected recurrence intervals for ungaged streams in Pennsylvania with drainage areas less than 2,000 square miles. These equations were developed utilizing peak-flow data from 322 streamflow-gaging stations within Pennsylvania and surrounding states. All stations used in the development of the equations had 10 or more years of record and included active and discontinued continuous-record as well as crest-stage partial-record stations. The state was divided into four regions, and regional regression equations were developed to estimate the 2-, 5-, 10-, 50-, 100-, and 500-year recurrence-interval flood flows. The equations were developed by means of a regression analysis that utilized basin characteristics and flow data associated with the stations. Significant explanatory variables at the 95-percent confidence level for one or more regression equations included the following basin characteristics: drainage area; mean basin elevation; and the percentages of carbonate bedrock, urban area, and storage within a basin. The regression equations can be used to predict the magnitude of flood flows for specified recurrence intervals for most streams in the state; however, they are not valid for streams with drainage areas generally greater than 2,000 square miles or with substantial regulation, diversion, or mining activity within the basin. Estimates of flood-flow magnitude and frequency for streamflow-gaging stations substantially affected by upstream regulation are also presented.
Confidence bounds on structural reliability
NASA Technical Reports Server (NTRS)
Mehta, S. R.; Cruse, T. A.; Mahadevan, S.
1993-01-01
Different approaches for quantifying physical, statistical, and model uncertainties associated with the distribution parameters which are aimed at determining structural reliability are described. Confidence intervals on the distribution parameters of the input random variables are estimated using four algorithms to evaluate uncertainty of the response. Design intervals are evaluated using either Monte Carlo simulation or an iterative approach. A first order approach can be used to compute a first approximation of the design interval, but its accuracy is not satisfactory. The regression approach which combines the iterative approach with Monte Carlo simulation is capable of providing good results if the performance function can be accurately represented using regression analysis. It is concluded that the design interval-based approach seems to be quite general and takes into account distribution and model uncertainties.
NASA Technical Reports Server (NTRS)
Biggs, Robert E.
1991-01-01
Confidence Limits Program (CLP) calculates upper and lower confidence limits associated with observed outcome of N independent trials with M occurrences of event of interest. Calculates probability of event of interest for confidence levels of 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, and 99 percent. Provides graphical presentation of all limits and how they relate to maximum-likelihood value. Written in IBM PC BASIC.
Understanding Academic Confidence
ERIC Educational Resources Information Center
Sander, Paul; Sanders, Lalage
2006-01-01
This paper draws on the psychological theories of self-efficacy and the self-concept to understand students' self-confidence in academic study in higher education as measured by the Academic Behavioural Confidence scale (ABC). In doing this, expectancy-value theory and self-efficacy theory are considered and contrasted with self-concept and…
Strengthening Public Confidence.
ERIC Educational Resources Information Center
Herlihy, John J.
Board members and administrators can build public confidence in their schools by taking every opportunity to communicate positive attitudes about the people in the schools. As leaders, they have the responsibility to use people power to promote the schools. If school employees feel good about their jobs, they will build confidence within the…
ERIC Educational Resources Information Center
Hess, Melinda R.; Hogarty, Kristine Y.; Ferron, John M.; Kromrey, Jeffrey D.
2007-01-01
Monte Carlo methods were used to examine techniques for constructing confidence intervals around multivariate effect sizes. Using interval inversion and bootstrapping methods, confidence intervals were constructed around the standard estimate of Mahalanobis distance (D[superscript 2]), two bias-adjusted estimates of D[superscript 2], and Huberty's…
Application of Sequential Interval Estimation to Adaptive Mastery Testing
ERIC Educational Resources Information Center
Chang, Yuan-chin Ivan
2005-01-01
In this paper, we apply sequential one-sided confidence interval estimation procedures with beta-protection to adaptive mastery testing. The procedures of fixed-width and fixed proportional accuracy confidence interval estimation can be viewed as extensions of one-sided confidence interval procedures. It can be shown that the adaptive mastery…
Comparison of asymptotic confidence sets for regression in small samples.
Kolobkov, Dmitry; Demin, Oleg; Metelkin, Evgeny
2016-01-01
In case of small samples, asymptotic confidence sets may be inaccurate, with their actual coverage probability far from a nominal confidence level. In a single framework, we consider four popular asymptotic methods of confidence estimation. These methods are based on model linearization, F-test, likelihood ratio test, and nonparametric bootstrapping procedure. Next, we apply each of these methods to derive three types of confidence sets: confidence intervals, confidence regions, and pointwise confidence bands. Finally, to estimate the actual coverage of these confidence sets, we conduct a simulation study on three regression problems. A linear model and nonlinear Hill and Gompertz models are tested in conditions of different sample size and experimental noise. The simulation study comprises calculation of the actual coverage of confidence sets over pseudo-experimental datasets for each model. For confidence intervals, such metrics as width and simultaneous coverage are also considered. Our comparison shows that the F-test and linearization methods are the most suitable for the construction of confidence intervals, the F-test - for confidence regions and the linearization - for pointwise confidence bands.
Confidence sets for optimal factor levels of a response surface.
Wan, Fang; Liu, Wei; Bretz, Frank; Han, Yang
2016-12-01
Construction of confidence sets for the optimal factor levels is an important topic in response surfaces methodology. In Wan et al. (2015), an exact (1-α) confidence set has been provided for a maximum or minimum point (i.e., an optimal factor level) of a univariate polynomial function in a given interval. In this article, the method has been extended to construct an exact (1-α) confidence set for the optimal factor levels of response surfaces. The construction method is readily applied to many parametric and semiparametric regression models involving a quadratic function. A conservative confidence set has been provided as an intermediate step in the construction of the exact confidence set. Two examples are given to illustrate the application of the confidence sets. The comparison between confidence sets indicates that our exact confidence set is better than the only other confidence set available in the statistical literature that guarantees the (1-α) confidence level.
Combining one-sample confidence procedures for inference in the two-sample case.
Fay, Michael P; Proschan, Michael A; Brittain, Erica
2015-03-01
We present a simple general method for combining two one-sample confidence procedures to obtain inferences in the two-sample problem. Some applications give striking connections to established methods; for example, combining exact binomial confidence procedures gives new confidence intervals on the difference or ratio of proportions that match inferences using Fisher's exact test, and numeric studies show the associated confidence intervals bound the type I error rate. Combining exact one-sample Poisson confidence procedures recreates standard confidence intervals on the ratio, and introduces new ones for the difference. Combining confidence procedures associated with one-sample t-tests recreates the Behrens-Fisher intervals. Other applications provide new confidence intervals with fewer assumptions than previously needed. For example, the method creates new confidence intervals on the difference in medians that do not require shift and continuity assumptions. We create a new confidence interval for the difference between two survival distributions at a fixed time point when there is independent censoring by combining the recently developed beta product confidence procedure for each single sample. The resulting interval is designed to guarantee coverage regardless of sample size or censoring distribution, and produces equivalent inferences to Fisher's exact test when there is no censoring. We show theoretically that when combining intervals asymptotically equivalent to normal intervals, our method has asymptotically accurate coverage. Importantly, all situations studied suggest guaranteed nominal coverage for our new interval whenever the original confidence procedures themselves guarantee coverage.
Alternatives to P value: confidence interval and effect size
2016-01-01
The previous articles of the Statistical Round in the Korean Journal of Anesthesiology posed a strong enquiry on the issue of null hypothesis significance testing (NHST). P values lie at the core of NHST and are used to classify all treatments into two groups: "has a significant effect" or "does not have a significant effect." NHST is frequently criticized for its misinterpretation of relationships and limitations in assessing practical importance. It has now provoked criticism for its limited use in merely separating treatments that "have a significant effect" from others that do not. Effect sizes and CIs expand the approach to statistical thinking. These attractive estimates facilitate authors and readers to discriminate between a multitude of treatment effects. Through this article, I have illustrated the concept and estimating principles of effect sizes and CIs. PMID:27924194
Modified Confidence Intervals for the Mean of an Autoregressive Process.
1985-08-01
functions of weakly dependent random variables. The original paper of Cornish and Fisher appeared in 1927. See HILL AND DAVIS (1968) for a more recent...STUART (1977) or HILL AND DAVIS (1968) for the statement and derivation of the Cornish Fisher expansion). T2 differs from a standard normal random...o Vya +i, which has been computed in the first order correction. Let S2,11il stand for the sum F’.>o S 0S j+g..-- Compute: 12. 5-,11-ij = SUMo 2(SaSP
Confidence Intervals for Standardized Linear Contrasts of Means
ERIC Educational Resources Information Center
Bonnett, Douglas G.
2008-01-01
Most psychology journals now require authors to report a sample value of effect size along with hypothesis testing results. The sample effect size value can be misleading because it contains sampling error. Authors often incorrectly interpret the sample effect size as if it were the population effect size. A simple solution to this problem is to…
Alternatives to P value: confidence interval and effect size.
Lee, Dong Kyu
2016-12-01
The previous articles of the Statistical Round in the Korean Journal of Anesthesiology posed a strong enquiry on the issue of null hypothesis significance testing (NHST). P values lie at the core of NHST and are used to classify all treatments into two groups: "has a significant effect" or "does not have a significant effect." NHST is frequently criticized for its misinterpretation of relationships and limitations in assessing practical importance. It has now provoked criticism for its limited use in merely separating treatments that "have a significant effect" from others that do not. Effect sizes and CIs expand the approach to statistical thinking. These attractive estimates facilitate authors and readers to discriminate between a multitude of treatment effects. Through this article, I have illustrated the concept and estimating principles of effect sizes and CIs.
Technological Pedagogical Content Knowledge (TPACK) Literature Using Confidence Intervals
ERIC Educational Resources Information Center
Young, Jamaal R.; Young, Jemimah L.; Shaker, Ziad
2012-01-01
The validity and reliability of Technological Pedagogical Content Knowledge (TPACK) as a framework to measure the extent to which teachers can teach with technology hinges on the ability to aggregate results across empirical studies. The results of data collected using the survey of pre-service teacher knowledge of teaching with technology (TKTT)…
Estimation of Confidence Intervals for Multiplication and Efficiency
Verbeke, J
2009-07-17
Helium-3 tubes are used to detect thermal neutrons by charge collection using the {sup 3}He(n,p) reaction. By analyzing the time sequence of neutrons detected by these tubes, one can determine important features about the constitution of a measured object: Some materials such as Cf-252 emit several neutrons simultaneously, while others such as uranium and plutonium isotopes multiply the number of neutrons to form bursts. This translates into unmistakable signatures. To determine the type of materials measured, one compares the measured count distribution with the one generated by a theoretical fission chain model. When the neutron background is negligible, the theoretical count distributions can be completely characterized by a pair of parameters, the multiplication M and the detection efficiency {var_epsilon}. While the optimal pair of M and {var_epsilon} can be determined by existing codes such as BigFit, the uncertainty on these parameters has not yet been fully studied. The purpose of this work is to precisely compute the uncertainties on the parameters M and {var_epsilon}, given the uncertainties in the count distribution. By considering different lengths of time tagged data, we will determine how the uncertainties on M and {var_epsilon} vary with the different count distributions.
... before trying any type of interval training. Recent studies suggest, however, that interval training can be used safely for short periods even in individuals with heart disease. Also keep the risk of overuse injury in mind. If you rush into a strenuous workout before ...
1984-09-11
COpY SEP 1 1 1984 Final Report PFTR-1092-83-1 Contract Number:, N00014-80-C-0140 Work Unit Number: -N197-064 DTIO0 ELECTEl odD THE PSYCHOLOGY OF...the final report of research done under the contract THE PSYCHOLOGY OF CONFIDENCE (Contract N00014-80-C-0150) awarded by the Office of Naval Research...section is a narrative summary of the research completed under this contract. It describes the general background to our work on confidence and
Adding Confidence to Knowledge
ERIC Educational Resources Information Center
Goodson, Ludwika Aniela; Slater, Don; Zubovic, Yvonne
2015-01-01
A "knowledge survey" and a formative evaluation process led to major changes in an instructor's course and teaching methods over a 5-year period. Design of the survey incorporated several innovations, including: a) using "confidence survey" rather than "knowledge survey" as the title; b) completing an instructional…
Predicting Systemic Confidence
ERIC Educational Resources Information Center
Falke, Stephanie Inez
2009-01-01
Using a mixed method approach, this study explored which educational factors predicted systemic confidence in master's level marital and family therapy (MFT) students, and whether or not the impact of these factors was influenced by student beliefs and their perception of their supervisor's beliefs about the value of systemic practice. One hundred…
Confidence Course Instructor's Guide.
ERIC Educational Resources Information Center
Montgomery County Public Schools, Rockville, MD.
The Confidence Course is a program of physical activities which seeks to improve individual self-image and to develop initiative and resourcefulness while promoting feelings of trust and good will within a group. General guidelines and procedures include safety considerations as well as common sense in activity selection and a perspective of…
Computing Graphical Confidence Bounds
NASA Technical Reports Server (NTRS)
Mezzacappa, M. A.
1983-01-01
Approximation for graphical confidence bounds is simple enough to run on programmable calculator. Approximation is used in lieu of numerical tables not always available, and exact calculations, which often require rather sizable computer resources. Approximation verified for collection of up to 50 data points. Method used to analyze tile-strength data on Space Shuttle thermal-protection system.
2016-06-07
both cognitive and affective factors can be implicated in trust judgements. Moreover, unlike confidence judgements (which can occur in many...occurs in situations without risks. A trust judgement, on the other hand, uses a variety of information beyond the merely cognitive , occurs only when...defined. Although there are many different definitions of trust, our definition (Adams and Webb, 2003) is as follows: Trust is a psychological state
Reclaim your creative confidence.
Kelley, Tom; Kelley, David
2012-12-01
Most people are born creative. But over time, a lot of us learn to stifle those impulses. We become warier of judgment, more cautious more analytical. The world seems to divide into "creatives" and "noncreatives," and too many people resign themselves to the latter category. And yet we know that creativity is essential to success in any discipline or industry. The good news, according to authors Tom Kelley and David Kelley of IDEO, is that we all can rediscover our creative confidence. The trick is to overcome the four big fears that hold most of us back: fear of the messy unknown, fear of judgment, fear of the first step, and fear of losing control. The authors use an approach based on the work of psychologist Albert Bandura in helping patients get over their snake phobias: You break challenges down into small steps and then build confidence by succeeding on one after another. Creativity is something you practice, say the authors, not just a talent you are born with.
Optimally combined confidence limits
NASA Astrophysics Data System (ADS)
Janot, P.; Le Diberder, F.
1998-02-01
An analytical and optimal procedure to combine statistically independent sets of confidence levels on a quantity is presented. This procedure does not impose any constraint on the methods followed by each analysis to derive its own limit. It incorporates the a priori statistical power of each of the analyses to be combined, in order to optimize the overall sensitivity. It can, in particular, be used to combine the mass limits obtained by several analyses searching for the Higgs boson in different decay channels, with different selection efficiencies, mass resolution and expected background. It can also be used to combine the mass limits obtained by several experiments (e.g. ALEPH, DELPHI, L3 and OPAL, at LEP 2) independently of the method followed by each of these experiments to derive their own limit. A method to derive the limit set by one analysis is also presented, along with an unbiased prescription to optimize the expected mass limit in the no-signal-hypothesis.
Simulation integration with confidence
NASA Astrophysics Data System (ADS)
Strelich, Tom; Stalcup, Bruce W.
1999-07-01
Current financial, schedule and risk constraints mandate reuse of software components when building large-scale simulations. While integration of simulation components into larger systems is a well-understood process, it is extremely difficult to do while ensuring that the results are correct. Illgen Simulation Technologies Incorporated and Litton PRC have joined forces to provide tools to integrate simulations with confidence. Illgen Simulation Technologies has developed an extensible and scaleable, n-tier, client- server, distributed software framework for integrating legacy simulations, models, tools, utilities, and databases. By utilizing the Internet, Java, and the Common Object Request Brokering Architecture as the core implementation technologies, the framework provides built-in scalability and extensibility.
Confidence bounds for nonlinear dose-response relationships.
Baayen, C; Hougaard, P
2015-11-30
An important aim of drug trials is to characterize the dose-response relationship of a new compound. Such a relationship can often be described by a parametric (nonlinear) function that is monotone in dose. If such a model is fitted, it is useful to know the uncertainty of the fitted curve. It is well known that Wald confidence intervals are based on linear approximations and are often unsatisfactory in nonlinear models. Apart from incorrect coverage rates, they can be unreasonable in the sense that the lower confidence limit of the difference to placebo can be negative, even when an overall test shows a significant positive effect. Bootstrap confidence intervals solve many of the problems of the Wald confidence intervals but are computationally intensive and prone to undercoverage for small sample sizes. In this work, we propose a profile likelihood approach to compute confidence intervals for the dose-response curve. These confidence bounds have better coverage than Wald intervals and are more precise and generally faster than bootstrap methods. Moreover, if monotonicity is assumed, the profile likelihood approach takes this automatically into account. The approach is illustrated using a public dataset and simulations based on the Emax and sigmoid Emax models.
Confidence in Numerical Simulations
Hemez, Francois M.
2015-02-23
This PowerPoint presentation offers a high-level discussion of uncertainty, confidence and credibility in scientific Modeling and Simulation (M&S). It begins by briefly evoking M&S trends in computational physics and engineering. The first thrust of the discussion is to emphasize that the role of M&S in decision-making is either to support reasoning by similarity or to “forecast,” that is, make predictions about the future or extrapolate to settings or environments that cannot be tested experimentally. The second thrust is to explain that M&S-aided decision-making is an exercise in uncertainty management. The three broad classes of uncertainty in computational physics and engineering are variability and randomness, numerical uncertainty and model-form uncertainty. The last part of the discussion addresses how scientists “think.” This thought process parallels the scientific method where by a hypothesis is formulated, often accompanied by simplifying assumptions, then, physical experiments and numerical simulations are performed to confirm or reject the hypothesis. “Confidence” derives, not just from the levels of training and experience of analysts, but also from the rigor with which these assessments are performed, documented and peer-reviewed.
Confidence and Cognitive Test Performance
ERIC Educational Resources Information Center
Stankov, Lazar; Lee, Jihyun
2008-01-01
This article examines the nature of confidence in relation to abilities, personality, and metacognition. Confidence scores were collected during the administration of Reading and Listening sections of the Test of English as a Foreign Language Internet-Based Test (TOEFL iBT) to 824 native speakers of English. Those confidence scores were correlated…
Monitoring tigers with confidence.
Linkie, Matthew; Guillera-Arroita, Gurutzeta; Smith, Joseph; Rayan, D Mark
2010-12-01
With only 5% of the world's wild tigers (Panthera tigris Linnaeus, 1758) remaining since the last century, conservationists urgently need to know whether or not the management strategies currently being employed are effectively protecting these tigers. This knowledge is contingent on the ability to reliably monitor tiger populations, or subsets, over space and time. In the this paper, we focus on the 2 seminal methodologies (camera trap and occupancy surveys) that have enabled the monitoring of tiger populations with greater confidence. Specifically, we: (i) describe their statistical theory and application in the field; (ii) discuss issues associated with their survey designs and state variable modeling; and, (iii) discuss their future directions. These methods have had an unprecedented influence on increasing statistical rigor within tiger surveys and, also, surveys of other carnivore species. Nevertheless, only 2 published camera trap studies have gone beyond single baseline assessments and actually monitored population trends. For low density tiger populations (e.g. <1 adult tiger/100 km(2)) obtaining sufficient precision for state variable estimates from camera trapping remains a challenge because of insufficient detection probabilities and/or sample sizes. Occupancy surveys have overcome this problem by redefining the sampling unit (e.g. grid cells and not individual tigers). Current research is focusing on developing spatially explicit capture-mark-recapture models and estimating abundance indices from landscape-scale occupancy surveys, as well as the use of genetic information for identifying and monitoring tigers. The widespread application of these monitoring methods in the field now enables complementary studies on the impact of the different threats to tiger populations and their response to varying management intervention.
Interval Estimation of the Population Squared Multiple Correlation
ERIC Educational Resources Information Center
Pohlmann, John T.; Moore, James F.
1977-01-01
A technique is presented which applies the Neyman theory of confidence intervals to interval estimation of the squared multiple correlation coefficient. A computer program is presented which can be used to apply the technique. (Author/JKS)
Improved interval estimation of comparative treatment effects
NASA Astrophysics Data System (ADS)
Van Krevelen, Ryne Christian
Comparative experiments, in which subjects are randomized to one of two treatments, are performed often. There is no shortage of papers testing whether a treatment effect exists and providing confidence intervals for the magnitude of this effect. While it is well understood that the object and scope of inference for an experiment will depend on what assumptions are made, these entities are not always clearly presented. We have proposed one possible method, which is based on the ideas of Jerzy Neyman, that can be used for constructing confidence intervals in a comparative experiment. The resulting intervals, referred to as Neyman-type confidence intervals, can be applied in a wide range of cases. Special care is taken to note which assumptions are made and what object and scope of inference are being investigated. We have presented a notation that highlights which parts of a problem are being treated as random. This helps ensure the focus on the appropriate scope of inference. The Neyman-type confidence intervals are compared to possible alternatives in two different inference settings: one in which inference is made about the units in the sample and one in which inference is made about units in a fixed population. A third inference setting, one in which inference is made about a process distribution, is also discussed. It is stressed that certain assumptions underlying this third type of inference are unverifiable. When these assumptions are not met, the resulting confidence intervals may cover their intended target well below the desired rate. Through simulation, we demonstrate that the Neyman-type intervals have good coverage properties when inference is being made about a sample or a population. In some cases the alternative intervals are much wider than necessary on average. Therefore, we recommend that researchers consider using our Neyman-type confidence intervals when carrying out inference about a sample or a population as it may provide them with more
Confidence limits and their errors
Rajendran Raja
2002-03-22
Confidence limits are common place in physics analysis. Great care must be taken in their calculation and use especially in cases of limited statistics. We introduce the concept of statistical errors of confidence limits and argue that not only should limits be calculated but also their errors in order to represent the results of the analysis to the fullest. We show that comparison of two different limits from two different experiments becomes easier when their errors are also quoted. Use of errors of confidence limits will lead to abatement of the debate on which method is best suited to calculate confidence limits.
Measuring Vaccine Confidence: Introducing a Global Vaccine Confidence Index
Larson, Heidi J; Schulz, William S; Tucker, Joseph D; Smith, David M D
2015-01-01
Background. Public confidence in vaccination is vital to the success of immunisation programmes worldwide. Understanding the dynamics of vaccine confidence is therefore of great importance for global public health. Few published studies permit global comparisons of vaccination sentiments and behaviours against a common metric. This article presents the findings of a multi-country survey of confidence in vaccines and immunisation programmes in Georgia, India, Nigeria, Pakistan, and the United Kingdom (UK) – these being the first results of a larger project to map vaccine confidence globally. Methods. Data were collected from a sample of the general population and from those with children under 5 years old against a core set of confidence questions. All surveys were conducted in the relevant local-language in Georgia, India, Nigeria, Pakistan, and the UK. We examine confidence in immunisation programmes as compared to confidence in other government health services, the relationships between confidence in the system and levels of vaccine hesitancy, reasons for vaccine hesitancy, ultimate vaccination decisions, and their variation based on country contexts and demographic factors. Results. The numbers of respondents by country were: Georgia (n=1000); India (n=1259); Pakistan (n=2609); UK (n=2055); Nigerian households (n=12554); and Nigerian health providers (n=1272). The UK respondents with children under five years of age were more likely to hesitate to vaccinate, compared to other countries. Confidence in immunisation programmes was more closely associated with confidence in the broader health system in the UK (Spearman’s ρ=0.5990), compared to Nigeria (ρ=0.5477), Pakistan (ρ=0.4491), and India (ρ=0.4240), all of which ranked confidence in immunisation programmes higher than confidence in the broader health system. Georgia had the highest rate of vaccine refusals (6 %) among those who reported initial hesitation. In all other countries surveyed most
Why Aren't They Called Probability Intervals?
ERIC Educational Resources Information Center
Devlin, Thomas F.
2008-01-01
This article offers suggestions for teaching confidence intervals, a fundamental statistical tool often misinterpreted by beginning students. A historical perspective presenting the interpretation given by their inventor is supported with examples and the use of technology. A method for determining confidence intervals for the seldom-discussed…
A Comparison of Approximate Interval Estimators for the Bernoulli Parameter
1993-12-01
The goal of this paper is to compare the accuracy of two approximate confidence interval estimators for the Bernoulli parameter p. The approximate...is appropriate for certain sample sizes and point estimators. Confidence interval , Binomial distribution, Bernoulli distribution, Poisson distribution.
A comparison of approximate interval estimators for the Bernoulli parameter
NASA Technical Reports Server (NTRS)
Leemis, Lawrence; Trivedi, Kishor S.
1993-01-01
The goal of this paper is to compare the accuracy of two approximate confidence interval estimators for the Bernoulli parameter p. The approximate confidence intervals are based on the normal and Poisson approximations to the binomial distribution. Charts are given to indicate which approximation is appropriate for certain sample sizes and point estimators.
Predicting confidence in flashbulb memories.
Day, Martin V; Ross, Michael
2014-01-01
Years after a shocking news event many people confidently report details of their flashbulb memories (e.g., what they were doing). People's confidence is a defining feature of their flashbulb memories, but it is not well understood. We tested a model that predicted confidence in flashbulb memories. In particular we examined whether people's social bond with the target of a news event predicts confidence. At a first session shortly after the death of Michael Jackson participants reported their sense of attachment to Michael Jackson, as well as their flashbulb memories and emotional and other reactions to Jackson's death. At a second session approximately 18 months later they reported their flashbulb memories and confidence in those memories. Results supported our proposed model. A stronger sense of attachment to Jackson was related to reports of more initial surprise, emotion, and rehearsal during the first session. Participants' bond with Michael Jackson predicted their confidence but not the consistency of their flashbulb memories 18 months later. We also examined whether participants' initial forecasts regarding the persistence of their flashbulb memories predicted the durability of their memories. Participants' initial forecasts were more strongly related to participants' subsequent confidence than to the actual consistency of their memories.
Bi- and Poly- Optimal Confidence Limits for a Location and Parameter.
1983-11-01
In this report we define poly-optimal confidence intervals for a location-parameter. The formulas are given for the case of two shapes, but can easily be extended to the case of many shapes. For the case of two situations, the Gaussian and the slash, the resulting family of confidence interval estimators is examined. These interval estimators are competitors of existing so-called robust procedures. A comparison to a few of these is included. (Author)
NASA Astrophysics Data System (ADS)
Oxley, Mark E.; Schubert, Christine M.; Thorsen, Steven N.
2010-04-01
A Classification system such as an Automatic Target Recognition (ATR) system with N possible output labels (or decisions) will have N(N-1) possible errors. The Receiver Operating Characteristic (ROC) manifold was created to quantify all of these errors. Finite truth data will produce an approximation to a ROC manifold. How well does the approximate ROC manifold approximate the TRUE ROC manifold? Several metrics exist that quantify the approximation ability, but researchers really wish to quantify the confidence in the approximate ROC manifold. This paper will review different confidence definitions for ROC curves and will derive an expression for confidence of a ROC manifold. The foundation of the confidence expression is based upon the Chebychev inequality..
Confidence rating for eutrophication assessments.
Brockmann, Uwe H; Topcu, Dilek H
2014-05-15
Confidence of monitoring data is dependent on their variability and representativeness of sampling in space and time. Whereas variability can be assessed as statistical confidence limits, representative sampling is related to equidistant sampling, considering gradients or changing rates at sampling gaps. By the proposed method both aspects are combined, resulting in balanced results for examples of total nitrogen concentrations in the German Bight/North Sea. For assessing sampling representativeness surface areas, vertical profiles and time periods are divided into regular sections for which individually the representativeness is calculated. The sums correspond to the overall representativeness of sampling in the defined area/time period. Effects of not sampled sections are estimated along parallel rows by reducing their confidence, considering their distances to next sampled sections and the interrupted gradients/changing rates. Confidence rating of time sections is based on maximum differences of sampling rates at regular time steps and related means of concentrations.
Testing 40 Predictions from the Transtheoretical Model Again, with Confidence
ERIC Educational Resources Information Center
Velicer, Wayne F.; Brick, Leslie Ann D.; Fava, Joseph L.; Prochaska, James O.
2013-01-01
Testing Theory-based Quantitative Predictions (TTQP) represents an alternative to traditional Null Hypothesis Significance Testing (NHST) procedures and is more appropriate for theory testing. The theory generates explicit effect size predictions and these effect size estimates, with related confidence intervals, are used to test the predictions.…
How to Fire a President: Voting "No Confidence" with Confidence
ERIC Educational Resources Information Center
Schmidt, Peter
2009-01-01
College faculties often use votes of "no confidence" to try to push out the leaders of their institutions. Many do so, however, without giving much thought to what such a vote actually means, whether they are using it appropriately, or how it will affect their campus--and their own future. Mae Kuykendall, a professor of law at Michigan State…
Targeting Low Career Confidence Using the Career Planning Confidence Scale
ERIC Educational Resources Information Center
McAuliffe, Garrett; Jurgens, Jill C.; Pickering, Worth; Calliotte, James; Macera, Anthony; Zerwas, Steven
2006-01-01
The authors describe the development and validation of a test of career planning confidence that makes possible the targeting of specific problem issues in employment counseling. The scale, developed using a rational process and the authors' experience with clients, was tested for criterion-related validity against 2 other measures. The scale…
7 CFR 210.18 - Administrative reviews.
Code of Federal Regulations, 2012 CFR
2012-01-01
...-sided 95 percent confidence interval is no more than 2 percentage points less than the point estimate... the right to a personal appearance before the review official, unless the review official agrees...
7 CFR 210.18 - Administrative reviews.
Code of Federal Regulations, 2011 CFR
2011-01-01
...-sided 95 percent confidence interval is no more than 2 percentage points less than the point estimate... the right to a personal appearance before the review official, unless the review official agrees...
de Lafuente, Victor; Romo, Ranulfo
2014-08-20
In this issue of Neuron, Fetsch et al. (2014) show that microstimulation of motion-sensitive neurons in the visual cortex (MT/MST) of primates mimics the addition of sensory information for which the stimulated neurons are selective. Such microstimulation increases the confidence that monkeys have in their decisions about motion direction.
QT-Interval Duration and Mortality Rate
Zhang, Yiyi; Post, Wendy S.; Dalal, Darshan; Blasco-Colmenares, Elena; Tomaselli, Gordon F.; Guallar, Eliseo
2012-01-01
Background Extreme prolongation or reduction of the QT interval predisposes patients to malignant ventricular arrhythmias and sudden cardiac death, but the association of variations in the QT interval within a reference range with mortality end points in the general population is unclear. Methods We included 7828 men and women from the Third National Health and Nutrition Examination Survey. Baseline QT interval was measured via standard 12-lead electrocardiographic readings. Mortality end points were assessed through December 31, 2006 (2291 deaths). Results After an average follow-up of 13.7 years, the association between QT interval and mortality end points was U-shaped. The multivariate-adjusted hazard ratios comparing participants at or above the 95th percentile of age-, sex-, race-, and R-R interval–corrected QT interval (≥439 milliseconds) with participants in the middle quintile (401 to <410 milliseconds) were 2.03 (95% confidence interval, 1.46-2.81) for total mortality, 2.55 (1.59-4.09) for mortality due to cardiovascular disease (CVD), 1.63 (0.96-2.75) for mortality due to coronary heart disease, and 1.65 (1.16-2.35) for non-CVD mortality. The corresponding hazard ratios comparing participants with a corrected QT interval below the fifth percentile (<377 milliseconds) with those in the middle quintile were 1.39 (95% confidence interval, 1.02-1.88) for total mortality, 1.35 (0.77-2.36) for CVD mortality, 1.02 (0.44-2.38) for coronary heart disease mortality, and 1.42 (0.97-2.08) for non-CVD mortality. Increased mortality also was observed with less extreme deviations of QT-interval duration. Similar, albeit weaker, associations also were observed with Bazett-corrected QT intervals. Conclusion Shortened and prolonged QT-interval durations, even within a reference range, are associated with increased mortality risk in the general population. PMID:22025428
Confidence Probability versus Detection Probability
Axelrod, M
2005-08-18
In a discovery sampling activity the auditor seeks to vet an inventory by measuring (or inspecting) a random sample of items from the inventory. When the auditor finds every sample item in compliance, he must then make a confidence statement about the whole inventory. For example, the auditor might say: ''We believe that this inventory of 100 items contains no more than 5 defectives with 95% confidence.'' Note this is a retrospective statement in that it asserts something about the inventory after the sample was selected and measured. Contrast this to the prospective statement: ''We will detect the existence of more than 5 defective items in this inventory with 95% probability.'' The former uses confidence probability while the latter uses detection probability. For a given sample size, the two probabilities need not be equal, indeed they could differ significantly. Both these probabilities critically depend on the auditor's prior belief about the number of defectives in the inventory and how he defines non-compliance. In other words, the answer strongly depends on how the question is framed.
Confidence-Based Feature Acquisition
NASA Technical Reports Server (NTRS)
Wagstaff, Kiri L.; desJardins, Marie; MacGlashan, James
2010-01-01
Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).
Interval arithmetic in calculations
NASA Astrophysics Data System (ADS)
Bairbekova, Gaziza; Mazakov, Talgat; Djomartova, Sholpan; Nugmanova, Salima
2016-10-01
Interval arithmetic is the mathematical structure, which for real intervals defines operations analogous to ordinary arithmetic ones. This field of mathematics is also called interval analysis or interval calculations. The given math model is convenient for investigating various applied objects: the quantities, the approximate values of which are known; the quantities obtained during calculations, the values of which are not exact because of rounding errors; random quantities. As a whole, the idea of interval calculations is the use of intervals as basic data objects. In this paper, we considered the definition of interval mathematics, investigated its properties, proved a theorem, and showed the efficiency of the new interval arithmetic. Besides, we briefly reviewed the works devoted to interval analysis and observed basic tendencies of development of integral analysis and interval calculations.
Normal probability plots with confidence.
Chantarangsi, Wanpen; Liu, Wei; Bretz, Frank; Kiatsupaibul, Seksan; Hayter, Anthony J; Wan, Fang
2015-01-01
Normal probability plots are widely used as a statistical tool for assessing whether an observed simple random sample is drawn from a normally distributed population. The users, however, have to judge subjectively, if no objective rule is provided, whether the plotted points fall close to a straight line. In this paper, we focus on how a normal probability plot can be augmented by intervals for all the points so that, if the population distribution is normal, then all the points should fall into the corresponding intervals simultaneously with probability 1-α. These simultaneous 1-α probability intervals provide therefore an objective mean to judge whether the plotted points fall close to the straight line: the plotted points fall close to the straight line if and only if all the points fall into the corresponding intervals. The powers of several normal probability plot based (graphical) tests and the most popular nongraphical Anderson-Darling and Shapiro-Wilk tests are compared by simulation. Based on this comparison, recommendations are given in Section 3 on which graphical tests should be used in what circumstances. An example is provided to illustrate the methods.
ERIC Educational Resources Information Center
Charles, Eric P.
2005-01-01
The correction for attenuation due to measurement error (CAME) has received many historical criticisms, most of which can be traced to the limited ability to use CAME inferentially. Past attempts to determine confidence intervals for CAME are summarized and their limitations discussed. The author suggests that inference requires confidence sets…
Confidence-related decision making.
Insabato, Andrea; Pannunzi, Mario; Rolls, Edmund T; Deco, Gustavo
2010-07-01
Neurons have been recorded that reflect in their firing rates the confidence in a decision. Here we show how this could arise as an emergent property in an integrate-and-fire attractor network model of decision making. The attractor network has populations of neurons that respond to each of the possible choices, each biased by the evidence for that choice, and there is competition between the attractor states until one population wins the competition and finishes with high firing that represents the decision. Noise resulting from the random spiking times of individual neurons makes the decision making probabilistic. We also show that a second attractor network can make decisions based on the confidence in the first decision. This system is supported by and accounts for neuronal responses recorded during decision making and makes predictions about the neuronal activity that will be found when a decision is made about whether to stay with a first decision or to abort the trial and start again. The research shows how monitoring can be performed in the brain and this has many implications for understanding cognitive functioning.
Confidence in ASCI scientific simulations
Ang, J.A.; Trucano, T.G.; Luginbuhl, D.R.
1998-06-01
The US Department of Energy`s (DOE) Accelerated Strategic Computing Initiative (ASCI) program calls for the development of high end computing and advanced application simulations as one component of a program to eliminate reliance upon nuclear testing in the US nuclear weapons program. This paper presents results from the ASCI program`s examination of needs for focused validation and verification (V and V). These V and V activities will ensure that 100 TeraOP-scale ASCI simulation code development projects apply the appropriate means to achieve high confidence in the use of simulations for stockpile assessment and certification. The authors begin with an examination of the roles for model development and validation in the traditional scientific method. The traditional view is that the scientific method has two foundations, experimental and theoretical. While the traditional scientific method does not acknowledge the role for computing and simulation, this examination establishes a foundation for the extension of the traditional processes to include verification and scientific software development that results in the notional framework known as Sargent`s Framework. This framework elucidates the relationships between the processes of scientific model development, computational model verification and simulation validation. This paper presents a discussion of the methodologies and practices that the ASCI program will use to establish confidence in large-scale scientific simulations. While the effort for a focused program in V and V is just getting started, the ASCI program has been underway for a couple of years. The authors discuss some V and V activities and preliminary results from the ALEGRA simulation code that is under development for ASCI. The breadth of physical phenomena and the advanced computational algorithms that are employed by ALEGRA make it a subject for V and V that should typify what is required for many ASCI simulations.
Overconfidence in Interval Estimates: What Does Expertise Buy You?
ERIC Educational Resources Information Center
McKenzie, Craig R. M.; Liersch, Michael J.; Yaniv, Ilan
2008-01-01
People's 90% subjective confidence intervals typically contain the true value about 50% of the time, indicating extreme overconfidence. Previous results have been mixed regarding whether experts are as overconfident as novices. Experiment 1 examined interval estimates from information technology (IT) professionals and UC San Diego (UCSD) students…
A Mathematical Framework for Statistical Decision Confidence.
Hangya, Balázs; Sanders, Joshua I; Kepecs, Adam
2016-09-01
Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here, we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice, and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations.
Beginning Teachers' Confidence before and after Induction
ERIC Educational Resources Information Center
Turley, Steve; Powers, Kristin; Nakai, Karen
2006-01-01
Levels of confidence of novice teachers were investigated pre- and post-induction program. In sum, 119 beginning teachers completed a New Teacher Confidence Survey on 28 teaching performance behaviors. There was a statistically significant confidence gain from pre- to postinduction on 20 of 28 teaching behaviors. Statistically significant gains…
Local and Global Judgments of Confidence
ERIC Educational Resources Information Center
Liberman, Varda
2004-01-01
Studies of calibration have shown that people's mean confidence in their answers (local confidence) tends to be greater than their overall estimate of the percentage of correct answers (global confidence). Moreover, whereas the former exhibits overconfidence, the latter often exhibits underconfidence. Three studies present evidence that global…
Ross, Deborah; Choi, Jonathan; Purves, Dale
2007-06-05
Throughout history and across cultures, humans have created music using pitch intervals that divide octaves into the 12 tones of the chromatic scale. Why these specific intervals in music are preferred, however, is not known. In the present study, we analyzed a database of individually spoken English vowel phones to examine the hypothesis that musical intervals arise from the relationships of the formants in speech spectra that determine the perceptions of distinct vowels. Expressed as ratios, the frequency relationships of the first two formants in vowel phones represent all 12 intervals of the chromatic scale. Were the formants to fall outside the ranges found in the human voice, their relationships would generate either a less complete or a more dilute representation of these specific intervals. These results imply that human preference for the intervals of the chromatic scale arises from experience with the way speech formants modulate laryngeal harmonics to create different phonemes.
Reliable prediction intervals with regression neural networks.
Papadopoulos, Harris; Haralambous, Haris
2011-10-01
This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well calibrated and tight enough to be useful in practice.
NASA Astrophysics Data System (ADS)
Matsakis, Nicholas D.; Gross, Thomas R.
Intervals are a new, higher-level primitive for parallel programming with which programmers directly construct the program schedule. Programs using intervals can be statically analyzed to ensure that they do not deadlock or contain data races. In this paper, we demonstrate the flexibility of intervals by showing how to use them to emulate common parallel control-flow constructs like barriers and signals, as well as higher-level patterns such as bounded-buffer producer-consumer. We have implemented intervals as a publicly available library for Java and Scala.
Approximate Interval Estimation Methods for the Reliability of Systems Using Discrete Component Data
1990-09-01
Three lower confidence interval estimation procedures for system reliability of coherent systems with cyclic components are developed and their...components. The combined procedure may yield a reasonably accurate lower confidence interval procedure for the reliability of coherent systems with mixtures of continuous and cyclic components.
Meta-Analytic Interval Estimation for Standardized and Unstandardized Mean Differences
ERIC Educational Resources Information Center
Bonett, Douglas G.
2009-01-01
The fixed-effects (FE) meta-analytic confidence intervals for unstandardized and standardized mean differences are based on an unrealistic assumption of effect-size homogeneity and perform poorly when this assumption is violated. The random-effects (RE) meta-analytic confidence intervals are based on an unrealistic assumption that the selected…
Interval estimations in metrology
NASA Astrophysics Data System (ADS)
Mana, G.; Palmisano, C.
2014-06-01
This paper investigates interval estimation for a measurand that is known to be positive. Both the Neyman and Bayesian procedures are considered and the difference between the two, not always perceived, is discussed in detail. A solution is proposed to a paradox originated by the frequentist assessment of the long-run success rate of Bayesian intervals.
Direct interval volume visualization.
Ament, Marco; Weiskopf, Daniel; Carr, Hamish
2010-01-01
We extend direct volume rendering with a unified model for generalized isosurfaces, also called interval volumes, allowing a wider spectrum of visual classification. We generalize the concept of scale-invariant opacity—typical for isosurface rendering—to semi-transparent interval volumes. Scale-invariant rendering is independent of physical space dimensions and therefore directly facilitates the analysis of data characteristics. Our model represents sharp isosurfaces as limits of interval volumes and combines them with features of direct volume rendering. Our objective is accurate rendering, guaranteeing that all isosurfaces and interval volumes are visualized in a crack-free way with correct spatial ordering. We achieve simultaneous direct and interval volume rendering by extending preintegration and explicit peak finding with data-driven splitting of ray integration and hybrid computation in physical and data domains. Our algorithm is suitable for efficient parallel processing for interactive applications as demonstrated by our CUDA implementation.
Estimating Confidence In Data Via Trend Analysis
NASA Technical Reports Server (NTRS)
Gutow, David A.; Arnold, Gregory A.
1991-01-01
Recursive equation defines probability-density function storing trends in noisy or potentially erroneous data. Part of algorithm for analysis of trends in data. Developed to determine reliability of data from sensor used to guide robot, and mathematical approach that it represents proves valuable in other applications. Enables computation of statistic (called "confidence statistic") indicating level of confidence in new datum. Generates confidence statistic high or low, depending on whether new datum consistent or inconsistent with previous trends.
A recipe for the construction of confidence limits
Iain A Bertram et al.
2000-04-12
In this note, the authors present the recipe recommended by the Search Limits Committee for the construction of confidence intervals for the use of D0 collaboration. In another note, currently in preparation, they present the rationale for this recipe, a critique of the current literature on this topic, and several examples of the use of the method. This note is intended to fill the need of the collaboration to have a reference available until the more complete note is finished. Section 2 introduces the notation used in this note, and Section 3 contains the suggested recipe.
ERIC Educational Resources Information Center
Penfield, Randall D.; Miller, Jeffrey M.
2004-01-01
As automated scoring of complex constructed-response examinations reaches operational status, the process of evaluating the quality of resultant scores, particularly in contrast to scores of expert human graders, becomes as complex as the data itself. Using a vignette from the Architectural Registration Examination (ARE), this article explores the…
ERIC Educational Resources Information Center
Young, Jamaal Rashad; Young, Jemimah Lea
2016-01-01
In this article, the authors provide a single group summary using the Mathematics Anxiety Rating Scale (MARS) to characterize and delineate the measurement of mathematics anxiety (MA) reported among Black students. Two research questions are explored: (a) What are the characteristics of studies administering the MARS and its derivatives to…
An alternative approach to confidence interval estimation for the win ratio statistic.
Luo, Xiaodong; Tian, Hong; Mohanty, Surya; Tsai, Wei Yann
2015-03-01
Pocock et al. (2012, European Heart Journal 33, 176-182) proposed a win ratio approach to analyzing composite endpoints comprised of outcomes with different clinical priorities. In this article, we establish a statistical framework for this approach. We derive the null hypothesis and propose a closed-form variance estimator for the win ratio statistic in all pairwise matching situation. Our simulation study shows that the proposed variance estimator performs well regardless of the magnitude of treatment effect size and the type of the joint distribution of the outcomes.
Small Sample Confidence Intervals in Log Space Back-Transformed from Normal Space
2006-06-01
BACK-TRANSFORMED FROM NORMAL SPACE I. Introduction 1.1 General Issue In regression modeling , transformations are often applied to satisfy the...reasons as to why the logarithm should interest us: linearity. When a statistical model is used to describe the relationship between two measure... Regres - sion Approach to Earned Value (Tracy, 2005). In this thesis, a data set was transformed us- ing the natural logarithm to obtain normalization
ERIC Educational Resources Information Center
Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.
2011-01-01
Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…
Computing confidence intervals on solution costs for stochastic grid generation expansion problems.
Woodruff, David L..; Watson, Jean-Paul
2010-12-01
A range of core operations and planning problems for the national electrical grid are naturally formulated and solved as stochastic programming problems, which minimize expected costs subject to a range of uncertain outcomes relating to, for example, uncertain demands or generator output. A critical decision issue relating to such stochastic programs is: How many scenarios are required to ensure a specific error bound on the solution cost? Scenarios are the key mechanism used to sample from the uncertainty space, and the number of scenarios drives computational difficultly. We explore this question in the context of a long-term grid generation expansion problem, using a bounding procedure introduced by Mak, Morton, and Wood. We discuss experimental results using problem formulations independently minimizing expected cost and down-side risk. Our results indicate that we can use a surprisingly small number of scenarios to yield tight error bounds in the case of expected cost minimization, which has key practical implications. In contrast, error bounds in the case of risk minimization are significantly larger, suggesting more research is required in this area in order to achieve rigorous solutions for decision makers.
Intersection Point Confidence Intervals as an Alternative to the Johnson-Neyman Technique.
ERIC Educational Resources Information Center
Schafer, William D.; And Others
An alternative is proposed for the Johnson-Neyman procedure (P. O. Johnson and J. Neyman, 1936). Used when heterogeneous regression lines for two groups are analyzed, the Johnson-Neyman procedure is a technique in which the difference between the two linear regression surfaces for the criterion variate (Y) is estimated conditional on a realization…
Bayesian Methods and Confidence Intervals for Automatic Target Recognition of SAR Canonical Shapes
2014-03-27
k1=1 ck1 N∑ k2=1 ck2 ... N∑ kD=1 ckD p(y | θk1 , θk2 ...θkD)p(θk1 , θk2 ...θkD) ≈ N∑ k1=1 N∑ k2=1 ... N∑ kD=1 ( ck1ck2 ... ckD ) p(y | θk1 , θk2...object (which causes aliasing) may be different than the actual scene extent Dmax. 3.2.3 Pose Parameters. As noted above, there is coupling between...received data vector y. The vector y is stored in the GPU high-speed shared memory. A final CUDA function applies the weight coefficients ck1 ... ckD and the
ERIC Educational Resources Information Center
Tryon, Warren W.; Lewis, Charles
2008-01-01
Evidence of group matching frequently takes the form of a nonsignificant test of statistical difference. Theoretical hypotheses of no difference are also tested in this way. These practices are flawed in that null hypothesis statistical testing provides evidence against the null hypothesis and failing to reject H[subscript 0] is not evidence…
Statistical Significance, Effect Size Reporting, and Confidence Intervals: Best Reporting Strategies
ERIC Educational Resources Information Center
Capraro, Robert M.
2004-01-01
With great interest the author read the May 2002 editorial in the "Journal for Research in Mathematics Education (JRME)" (King, 2002) regarding changes to the 5th edition of the "Publication Manual of the American Psychological Association" (APA, 2001). Of special note to him, and of great import to the field of mathematics education research, are…
ERIC Educational Resources Information Center
Padilla, Miguel A.; Divers, Jasmin
2016-01-01
Coefficient omega and alpha are both measures of the composite reliability for a set of items. Unlike coefficient alpha, coefficient omega remains unbiased with congeneric items with uncorrelated errors. Despite this ability, coefficient omega is not as widely used and cited in the literature as coefficient alpha. Reasons for coefficient omega's…
ERIC Educational Resources Information Center
Fraile, Rubén; Bosch-Morell, Francisco
2015-01-01
Lecturer promotion and tenure decisions are critical both for university management and for the affected lecturers. Therefore, they should be made cautiously and based on reliable information. Student evaluations of teaching quality are among the most used and analysed sources of such information. However, to date little attention has been paid in…
McLoughlin, K.
2016-01-11
The overall aim of this project is to develop a software package, called MetaQuant, that can determine the constituents of a complex microbial sample and estimate their relative abundances by analysis of metagenomic sequencing data. The goal for Task 1 is to create a generative model describing the stochastic process underlying the creation of sequence read pairs in the data set. The stages in this generative process include the selection of a source genome sequence for each read pair, with probability dependent on its abundance in the sample. The other stages describe the evolution of the source genome from its nearest common ancestor with a reference genome, breakage of the source DNA into short fragments, and the errors in sequencing the ends of the fragments to produce read pairs.
McLoughlin, K.
2016-01-22
The software application “MetaQuant” was developed by our group at Lawrence Livermore National Laboratory (LLNL). It is designed to profile microbial populations in a sample using data from whole-genome shotgun (WGS) metagenomic DNA sequencing. Several other metagenomic profiling applications have been described in the literature. We ran a series of benchmark tests to compare the performance of MetaQuant against that of a few existing profiling tools, using real and simulated sequence datasets. This report describes our benchmarking procedure and results.
Raising Confident, Competent Daughters: Strategies for Parents.
ERIC Educational Resources Information Center
Ransome, Whitney, Ed.; And Others
This booklet contains five essays designed to help parents raise confident, competent daughters. They focus on ways that parents can help their preadolescent and adolescent daughters: (1) speak up in class, articulate their thoughts, and speak with self-confidence in various academic and social situations; (2) develop an interest and aptitude for…
Preservice Educators' Confidence in Addressing Sexuality Education
ERIC Educational Resources Information Center
Wyatt, Tammy Jordan
2009-01-01
This study examined 328 preservice educators' level of confidence in addressing four sexuality education domains and 21 sexuality education topics. Significant differences in confidence levels across the four domains were found for gender, academic major, sexuality education philosophy, and sexuality education knowledge. Preservice educators…
New Teacher Confidence: How Does It Develop?
ERIC Educational Resources Information Center
Tran, MyLuong T.; Young, Russell L.; Mathison, Carla; Hahn, Brenda Terry
The purpose of this study was to investigate the confidence levels of new teachers and related factors. Seventy-seven first and second year teachers participating in a new teacher retention project filled out a survey. Teachers felt most confident in their communication with colleagues, sensitivity to the needs of a multicultural classroom, and…
A Confidence Paradigm for Classification Systems
2008-09-01
will have an associated confidence function. Class specific classifier confidence is esti- mated using multiattribute preference theory . The appropriate...Preference Theory Application Multiattribute preference theory is a branch of decision analysis that allows a decision maker to determine preferences...for alternatives when each of the alternatives has more than one attribute. Within multiattribute preference theory , there are two classes of
Enhancing Confidence in the Gender Sensitive Curriculum.
ERIC Educational Resources Information Center
Pryor, John
Starting from the idea that even well-intentioned teachers often undermine girls' confidence in gender-sensitive areas of school curriculum, this paper outlines methods that might be used to enhance this confidence. It presents impressions from a 1-year observation of a class of 10-year-olds in a British school as part of a project called Group…
Building Scientific Confidence in the Development and ...
Building Scientific Confidence in the Development and Evaluation of Read-Across Using Tox21 Approaches Slide presentation at GlobalChem conference and workshop in Washington, DC on Case Study on Building Scientific Confidence in the Development and Evaluation of Read-Across Using Tox21 Approaches
Confidence in Parenting: Is Parent Education Working?
ERIC Educational Resources Information Center
Stanberry, J. Phillip; Stanberry, Anne M.
This study examined parents' feelings of confidence in their parenting ability among 56 individuals enrolled in 5 parent education programs in Mississippi, hypothesizing that there would be significant correlations between personal authority in the family system and a parent's confidence in performing the various roles of parenting. Based on…
Gender, Family Structure, and Adolescents' Primary Confidants
ERIC Educational Resources Information Center
Nomaguchi, Kei M.
2008-01-01
Using data from the National Longitudinal Survey of Youth 1997 (N = 4,190), this study examined adolescents' reports of primary confidants. Results showed that nearly 30% of adolescents aged 16-18 nominated mothers as primary confidants, 25% nominated romantic partners, and 20% nominated friends. Nominating romantic partners or friends was related…
Confidence Wagering during Mathematics and Science Testing
ERIC Educational Resources Information Center
Jack, Brady Michael; Liu, Chia-Ju; Chiu, Hoan-Lin; Shymansky, James A.
2009-01-01
This proposal presents the results of a case study involving five 8th grade Taiwanese classes, two mathematics and three science classes. These classes used a new method of testing called confidence wagering. This paper advocates the position that confidence wagering can predict the accuracy of a student's test answer selection during…
Decision Making and Confidence Given Uncertain Advice
ERIC Educational Resources Information Center
Lee, Michael D.; Dry, Matthew J.
2006-01-01
We study human decision making in a simple forced-choice task that manipulates the frequency and accuracy of available information. Empirically, we find that people make decisions consistent with the advice provided, but that their subjective confidence in their decisions shows 2 interesting properties. First, people's confidence does not depend…
Examining Response Confidence in Multiple Text Tasks
ERIC Educational Resources Information Center
List, Alexandra; Alexander, Patricia A.
2015-01-01
Students' confidence in their responses to a multiple text-processing task and their justifications for those confidence ratings were investigated. Specifically, 215 undergraduates responded to two academic questions, differing by type (i.e., discrete and open-ended) and by domain (i.e., developmental psychology and astrophysics), using a digital…
Confidence and Doubt in Relation to Mathematics.
ERIC Educational Resources Information Center
Boekaerts, Monique
This paper reviews literature documenting gender differences in beliefs related to mathematics achievement. The areas included in the discussion are: (1) gender differences in mathematics achievement and self-referenced cognitions, (2) the effect of confidence and anxiety on mathematics performance and expenditure of effort, and (3) confidence and…
Hypercorrection of High Confidence Errors in Children
ERIC Educational Resources Information Center
Metcalfe, Janet; Finn, Bridgid
2012-01-01
Three experiments investigated whether the hypercorrection effect--the finding that errors committed with high confidence are easier, rather than more difficult, to correct than are errors committed with low confidence--occurs in grade school children as it does in young adults. All three experiments showed that Grade 3-6 children hypercorrected…
Confidence and Competence with Mathematical Procedures
ERIC Educational Resources Information Center
Foster, Colin
2016-01-01
Confidence assessment (CA), in which students state alongside each of their answers a confidence level expressing how certain they are, has been employed successfully within higher education. However, it has not been widely explored with school pupils. This study examined how school mathematics pupils (N?=?345) in five different secondary schools…
An informative confidence metric for ATR.
Bow, Wallace Johnston Jr.; Richards, John Alfred; Bray, Brian Kenworthy
2003-03-01
Automatic or assisted target recognition (ATR) is an important application of synthetic aperture radar (SAR). Most ATR researchers have focused on the core problem of declaration-that is, detection and identification of targets of interest within a SAR image. For ATR declarations to be of maximum value to an image analyst, however, it is essential that each declaration be accompanied by a reliability estimate or confidence metric. Unfortunately, the need for a clear and informative confidence metric for ATR has generally been overlooked or ignored. We propose a framework and methodology for evaluating the confidence in an ATR system's declarations and competing target hypotheses. Our proposed confidence metric is intuitive, informative, and applicable to a broad class of ATRs. We demonstrate that seemingly similar ATRs may differ fundamentally in the ability-or inability-to identify targets with high confidence.
Fransen, K; Steffens, N K; Haslam, S A; Vanbeselaere, N; Vande Broek, G; Boen, F
2016-12-01
The present research examines the impact of leaders' confidence in their team on the team confidence and performance of their teammates. In an experiment involving newly assembled soccer teams, we manipulated the team confidence expressed by the team leader (high vs neutral vs low) and assessed team members' responses and performance as they unfolded during a competition (i.e., in a first baseline session and a second test session). Our findings pointed to team confidence contagion such that when the leader had expressed high (rather than neutral or low) team confidence, team members perceived their team to be more efficacious and were more confident in the team's ability to win. Moreover, leaders' team confidence affected individual and team performance such that teams led by a highly confident leader performed better than those led by a less confident leader. Finally, the results supported a hypothesized mediational model in showing that the effect of leaders' confidence on team members' team confidence and performance was mediated by the leader's perceived identity leadership and members' team identification. In conclusion, the findings of this experiment suggest that leaders' team confidence can enhance members' team confidence and performance by fostering members' identification with the team.
Communication confidence in persons with aphasia.
Babbitt, Edna M; Cherney, Leora R
2010-01-01
Communication confidence is a construct that has not been explored in the aphasia literature. Recently, national and international organizations have endorsed broader assessment methods that address quality of life and include participation, activity, and impairment domains as well as psychosocial areas. Individuals with aphasia encounter difficulties in all these areas on a daily basis in living with a communication disorder. Improvements are often reflected in narratives that are not typically included in standard assessments. This article illustrates how a new instrument measuring communication confidence might fit into a broad assessment framework and discusses the interaction of communication confidence, autonomy, and self-determination for individuals living with aphasia.
Developing Confidence Limits For Reliability Of Software
NASA Technical Reports Server (NTRS)
Hayhurst, Kelly J.
1991-01-01
Technique developed for estimating reliability of software by use of Moranda geometric de-eutrophication model. Pivotal method enables straightforward construction of exact bounds with associated degree of statistical confidence about reliability of software. Confidence limits thus derived provide precise means of assessing quality of software. Limits take into account number of bugs found while testing and effects of sampling variation associated with random order of discovering bugs.
Toward a Theory of Assurance Case Confidence
2012-09-01
assurance case claim. The framework is based on the notion of eliminative induction—the princi- ple (first put forward by Francis Bacon ) that confidence in...eliminative induction. As first proposed by Francis Bacon [Schum 2001] and extended by L. Jonathan Cohen [Cohen 1970, 1977, 1989], eliminative induction is...eliminative in- duction—the principle (first put forward by Francis Bacon ) that confidence in the truth of a hypothesis (or claim) increases as reasons for
Confidence regions of planar cardiac vectors
NASA Technical Reports Server (NTRS)
Dubin, S.; Herr, A.; Hunt, P.
1980-01-01
A method for plotting the confidence regions of vectorial data obtained in electrocardiology is presented. The 90%, 95% and 99% confidence regions of cardiac vectors represented in a plane are obtained in the form of an ellipse centered at coordinates corresponding to the means of a sample selected at random from a bivariate normal distribution. An example of such a plot for the frontal plane QRS mean electrical axis for 80 horses is also presented.
Worse than enemies. The CEO's destructive confidant.
Sulkowicz, Kerry J
2004-02-01
The CEO is often the most isolated and protected employee in the organization. Few leaders, even veteran CEOs, can do the job without talking to someone about their experiences, which is why most develop a close relationship with a trusted colleague, a confidant to whom they can tell their thoughts and fears. In his work with leaders, the author has found that many CEO-confidant relationships function very well. The confidants keep their leaders' best interests at heart. They derive their gratification vicariously, through the help they provide rather than through any personal gain, and they are usually quite aware that a person in their position can potentially abuse access to the CEO's innermost secrets. Unfortunately, almost as many confidants will end up hurting, undermining, or otherwise exploiting CEOs when the executives are at their most vulnerable. These confidants rarely make the headlines, but behind the scenes they do enormous damage to the CEO and to the organization as a whole. What's more, the leader is often the last one to know when or how the confidant relationship became toxic. The author has identified three types of destructive confidants. The reflector mirrors the CEO, constantly reassuring him that he is the "fairest CEO of them all." The insulator buffers the CEO from the organization, preventing critical information from getting in or out. And the usurper cunningly ingratiates himself with the CEO in a desperate bid for power. This article explores how the CEO-confidant relationship plays out with each type of adviser and suggests ways CEOs can avoid these destructive relationships.
Gsteiger, S; Bretz, F; Liu, W
2011-07-01
Many applications in biostatistics rely on nonlinear regression models, such as, for example, population pharmacokinetic and pharmacodynamic modeling, or modeling approaches for dose-response characterization and dose selection. Such models are often expressed as nonlinear mixed-effects models, which are implemented in all major statistical software packages. Inference on the model curve can be based on the estimated parameters, from which pointwise confidence intervals for the mean profile at any single point in the covariate region (time, dose, etc.) can be derived. These pointwise confidence intervals, however, should not be used for simultaneous inferences beyond that single covariate value. If assessment over the entire covariate region is required, the joint coverage probability by using the combined pointwise confidence intervals is likely to be less than the nominal coverage probability. In this paper we consider simultaneous confidence bands for the mean profile over the covariate region of interest and propose two large-sample methods for their construction. The first method is based on the Schwarz inequality and an asymptotic χ(2) distribution. The second method relies on simulating from a multivariate normal distribution. We illustrate the methods with the pharmacokinetics of theophylline. In addition, we report the results of an extensive simulation study to investigate the operating characteristics of the two construction methods. Finally, we present extensions to construct simultaneous confidence bands for the difference of two models and to assess equivalence between two models in biosimilarity applications.
Resuscitation of the Newborn: Simulating for Confidence
Woodman, Anna; McCay, Wendy; Bates, Sarah E
2016-01-01
Introduction Non-pediatric trainees working in pediatrics in the UK are expected to attend newborn deliveries and provide initial newborn life support if needed. In Swindon, new junior doctors receive a 90-minute teaching session at the start of their pediatrics rotation, but the content has not previously been standardized, and it may be several weeks before a doctor attends a newborn delivery. Thus, the confidence and competence in newborn resuscitation of doctors attending deliveries can vastly vary. Methods A standardized teaching package was developed as part of the pediatrics induction program. This includes an interactive lecture on the physiology of the newborn, skills stations, and mini-simulations to consolidate skills. This is accompanied by a program of regular neonatal mini-simulations as part of the departmental morning teaching program. These sessions allow junior doctors to practice their skills in a safe, simulated environment and reinforce the newborn life support pathway. Results Qualitative and quantitative feedback was sought following delivery of the induction training session. Junior doctors were asked to rate their confidence before and after the induction session using Likert scales from 1 (least confident) to 5 (most confident). Median confidence in attending term deliveries increased from 2 (range 1 - 4) to 4 (2 - 5), P=0.008. There was evidence that confidence was maintained at one month following induction. Conclusions A simulation program has been successful at improving confidence among junior doctors in attending newborn deliveries. This has the potential to improve patient care and trainees’ experiences of their pediatrics placement. PMID:27774358
A note on the empirical likelihood confidence band for hazards ratio with covariate adjustment.
Zhu, Shihong; Yang, Yifan; Zhou, Mai
2015-09-01
In medical studies comparing two treatments in the presence of censored data, the stratified Cox model is an important tool that has the ability to flexibly handle non-proportional hazards while allowing parsimonious covariate adjustment. In order to capture the cumulative treatment effect, the ratio of the treatment specific cumulative baseline hazards is often used as a measure of the treatment effect. Pointwise and simultaneous confidence bands associated with the estimated ratio provide a global picture of how the treatment effect evolves over time. Recently, Dong and Matthews (2012, Biometrics 68, 408-418) proposed to construct a pointwise confidence interval for the ratio using a plug-in type empirical likelihood approach. However, their result on the limiting distribution of the empirical likelihood ratio is generally incorrect and the resulting confidence interval is asymptotically undercovering. In this article, we derive the correct limiting distribution for the likelihood ratio statistic. We also present simulation studies to demonstrate the effectiveness of our approach.
Neyman-Pearson and Bayes interval estimates for sampling by attributes
Mason, R.M.; Ryl, P.; Williams, J.W.
1984-12-01
This paper compares confidence intervals for single and multistage sampling schemes with Bayesian interval estimates obtained with a uniform prior distribution. Examples are presented in graphical form for sampling by attributes from an infinite population, or from a finite population with replacement. A general proof is given that the Neyman-Pearson confidence level associated with a confidence interval for the binomial parameter p will be no greater than the Bayesian confidence level calculated using a uniform prior distribution. A demonstration is provided for a fact published earlier, viz., that the Bayesian prior distribution can be selected so as to provide equality between one-sided Neyman-Pearson and Bayesian confidence bounds. Applications to EMP analysis are discussed in the final section.
Simplified approach to confidence limits in radioimmunoassay
Baxter, R.C.
1980-05-01
A simple method of calculating confidence limits for radioimmunoassay data is presented. The method involves the use of the within-assay variation in dose estimate of three routine quality-control specimens, measured in repeated assays, to estimate the confidence limits for results on unknown samples. Results for control specimens are combined by calculating the unique quadratic curve fitting a graph of within-assay standard deviation vs mean value for each control. This method requires no special data accumulation or advanced computing equipment. For cortisol, lutropin, and thyroxine radioimmunassays, confidence limits calculated in this way have been compared with three calculated from the variance of the response variable B/Bq in repeated standard curves. Both methods agree well with actual limits observed when plasma pools containing a wide range of hormone concentrations are assayed repeatedly.
Chua, Elizabeth F.; Hannula, Deborah E.; Ranganath, Charan
2012-01-01
It is generally believed that accuracy and confidence in one’s memory are related, but there are many instances when they diverge. Accordingly, it is important to disentangle the factors which contribute to memory accuracy and confidence, especially those factors that contribute to confidence, but not accuracy. We used eye movements to separately measure fluent cue processing, the target recognition experience, and relative evidence assessment on recognition confidence and accuracy. Eye movements were monitored during a face-scene associative recognition task, in which participants first saw a scene cue, followed by a forced-choice recognition test for the associated face, with confidence ratings. Eye movement indices of the target recognition experience were largely indicative of accuracy, and showed a relationship to confidence for accurate decisions. In contrast, eye movements during the scene cue raised the possibility that more fluent cue processing was related to higher confidence for both accurate and inaccurate recognition decisions. In a second experiment, we manipulated cue familiarity, and therefore cue fluency. Participants showed higher confidence for cue-target associations for when the cue was more familiar, especially for incorrect responses. These results suggest that over-reliance on cue familiarity and under-reliance on the target recognition experience may lead to erroneous confidence. PMID:22171810
Chua, Elizabeth F; Hannula, Deborah E; Ranganath, Charan
2012-01-01
It is generally believed that accuracy and confidence in one's memory are related, but there are many instances when they diverge. Accordingly it is important to disentangle the factors that contribute to memory accuracy and confidence, especially those factors that contribute to confidence, but not accuracy. We used eye movements to separately measure fluent cue processing, the target recognition experience, and relative evidence assessment on recognition confidence and accuracy. Eye movements were monitored during a face-scene associative recognition task, in which participants first saw a scene cue, followed by a forced-choice recognition test for the associated face, with confidence ratings. Eye movement indices of the target recognition experience were largely indicative of accuracy, and showed a relationship to confidence for accurate decisions. In contrast, eye movements during the scene cue raised the possibility that more fluent cue processing was related to higher confidence for both accurate and inaccurate recognition decisions. In a second experiment we manipulated cue familiarity, and therefore cue fluency. Participants showed higher confidence for cue-target associations for when the cue was more familiar, especially for incorrect responses. These results suggest that over-reliance on cue familiarity and under-reliance on the target recognition experience may lead to erroneous confidence.
Detecting Disease in Radiographs with Intuitive Confidence
Jaeger, Stefan
2015-01-01
This paper argues in favor of a specific type of confidence for use in computer-aided diagnosis and disease classification, namely, sine/cosine values of angles represented by points on the unit circle. The paper shows how this confidence is motivated by Chinese medicine and how sine/cosine values are directly related with the two forces Yin and Yang. The angle for which sine and cosine are equal (45°) represents the state of equilibrium between Yin and Yang, which is a state of nonduality that indicates neither normality nor abnormality in terms of disease classification. The paper claims that the proposed confidence is intuitive and can be readily understood by physicians. The paper underpins this thesis with theoretical results in neural signal processing, stating that a sine/cosine relationship between the actual input signal and the perceived (learned) input is key to neural learning processes. As a practical example, the paper shows how to use the proposed confidence values to highlight manifestations of tuberculosis in frontal chest X-rays. PMID:26495433
Evaluating Measures of Optimism and Sport Confidence
ERIC Educational Resources Information Center
Fogarty, Gerard J.; Perera, Harsha N.; Furst, Andrea J.; Thomas, Patrick R.
2016-01-01
The psychometric properties of the Life Orientation Test-Revised (LOT-R), the Sport Confidence Inventory (SCI), and the Carolina SCI (CSCI) were examined in a study involving 260 athletes. The study aimed to test the dimensional structure, convergent and divergent validity, and invariance over competition level of scores generated by these…
Current Developments in Measuring Academic Behavioural Confidence
ERIC Educational Resources Information Center
Sander, Paul
2009-01-01
Using published findings and by further analyses of existing data, the structure, validity and utility of the Academic Behavioural Confidence scale (ABC) is critically considered. Validity is primarily assessed through the scale's relationship with other existing scales as well as by looking for predicted differences. The utility of the ABC scale…
Confident Communication: Speaking Tips for Educators.
ERIC Educational Resources Information Center
Parker, Douglas A.
This resource book seeks to provide the building blocks needed for public speaking while eliminating the fear factor. The book explains how educators can perfect their oratorical capabilities as well as enjoy the security, confidence, and support needed to create and deliver dynamic speeches. Following an Introduction: A Message for Teachers,…
Training for Job-Skill Confidence.
ERIC Educational Resources Information Center
Lingg, Mary-Ann
1996-01-01
Evaluation of the effectiveness of the Kmart Employment for Youth job preparation program showed that the 43 African American students ages 16-19 gained confidence in such employable skills as the ability to plan and develop a career, to make decisions, and to find a job. (JOW)
Using seismology for regional confidence building
Nakanishi, K.K.
1997-03-01
Confidence building between regional parties can be facilitated through cooperative seismological research activities. Shared data, facilities, technology, and research results can (1) assure participants that nuclear testing is not taking place, (2) provide information that can be used to characterize the geophysical parameters of a region for earthquake hazard mitigation, and (3) support basic seismic research.
Establishing confidence in CCV/ACT technology
NASA Technical Reports Server (NTRS)
Holloway, R. B.; Shomber, H. A.
1976-01-01
Despite significant advancements in controls configured vehicles/active controls technology (CCV/ACT) in the past decade, few applications of this promising technology have appeared in recent aircraft designs. The status of CCV/ACT is summarized, and some of the constraints which are retarding its wider application are described. Suggestions toward establishing an increased level of confidence in the technology are given.
Observed Consultation: Confidence and Accuracy of Assessors
ERIC Educational Resources Information Center
Tweed, Mike; Ingham, Christopher
2010-01-01
Judgments made by the assessors observing consultations are widely used in the assessment of medical students. The aim of this research was to study judgment accuracy and confidence and the relationship between these. Assessors watched recordings of consultations, scoring the students on: a checklist of items; attributes of consultation; a…
ERIC Educational Resources Information Center
Warren, Richard Daniel
2012-01-01
The purpose of this research was to investigate the effects of including adaptive confidence strategies in instructionally sound computer-assisted instruction (CAI) on learning and learner confidence. Seventy-one general educational development (GED) learners recruited from various GED learning centers at community colleges in the southeast United…
Credible Intervals for Precision and Recall Based on a K-Fold Cross-Validated Beta Distribution.
Wang, Yu; Li, Jihong
2016-08-01
In typical machine learning applications such as information retrieval, precision and recall are two commonly used measures for assessing an algorithm's performance. Symmetrical confidence intervals based on K-fold cross-validated t distributions are widely used for the inference of precision and recall measures. As we confirmed through simulated experiments, however, these confidence intervals often exhibit lower degrees of confidence, which may easily lead to liberal inference results. Thus, it is crucial to construct faithful confidence (credible) intervals for precision and recall with a high degree of confidence and a short interval length. In this study, we propose two posterior credible intervals for precision and recall based on K-fold cross-validated beta distributions. The first credible interval for precision (or recall) is constructed based on the beta posterior distribution inferred by all K data sets corresponding to K confusion matrices from a K-fold cross-validation. Second, considering that each data set corresponding to a confusion matrix from a K-fold cross-validation can be used to infer a beta posterior distribution of precision (or recall), the second proposed credible interval for precision (or recall) is constructed based on the average of K beta posterior distributions. Experimental results on simulated and real data sets demonstrate that the first credible interval proposed in this study almost always resulted in degrees of confidence greater than 95%. With an acceptable degree of confidence, both of our two proposed credible intervals have shorter interval lengths than those based on a corrected K-fold cross-validated t distribution. Meanwhile, the average ranks of these two credible intervals are superior to that of the confidence interval based on a K-fold cross-validated t distribution for the degree of confidence and are superior to that of the confidence interval based on a corrected K-fold cross-validated t distribution for the
Building Public Confidence in Nuclear Activities
Isaacs, T
2002-02-13
Achieving public acceptance has become a central issue in discussions regarding the future of nuclear power and associated nuclear activities. Effective public communication and public participation are often put forward as the key building blocks in garnering public acceptance. A recent international workshop in Finland provided insights into other features that might also be important to building and sustaining public confidence in nuclear activities. The workshop was held in Finland in close cooperation with Finnish stakeholders. This was most appropriate because of the recent successes in achieving positive decisions at the municipal, governmental, and Parliamentary levels, allowing the Finnish high-level radioactive waste repository program to proceed, including the identification and approval of a proposed candidate repository site Much of the workshop discussion appropriately focused on the roles of public participation and public communications in building public confidence. It was clear that well constructed and implemented programs of public involvement and communication and a sense of fairness were essential in building the extent of public confidence needed to allow the repository program in Finland to proceed. It was also clear that there were a number of other elements beyond public involvement that contributed substantially to the success in Finland to date. And, in fact, it appeared that these other factors were also necessary to achieving the Finnish public acceptance. In other words, successful public participation and communication were necessary but not sufficient. What else was important? Culture, politics, and history vary from country to country, providing differing contexts for establishing and maintaining public confidence. What works in one country will not necessarily be effective in another. Nonetheless, there appear to be certain elements that might be common to programs that are successful in sustaining public confidence, and some of
Building Public Confidence in Nuclear Activities
Isaacs, T
2002-03-27
Achieving public acceptance has become a central issue in discussions regarding the future of nuclear power and associated nuclear activities. Effective public communication and public participation are often put forward as the key building blocks in garnering public acceptance. A recent international workshop in Finland provided insights into other features that might also be important to building and sustaining public confidence in nuclear activities. The workshop was held in Finland in close cooperation with Finnish stakeholders. This was most appropriate because of the recent successes in achieving positive decisions at the municipal, governmental, and Parliamentary levels, allowing the Finnish high-level radioactive waste repository program to proceed, including the identification and approval of a proposed candidate repository site. Much of the workshop discussion appropriately focused on the roles of public participation and public communications in building public confidence. It was clear that well constructed and implemented programs of public involvement and communication and a sense of fairness were essential in building the extent of public confidence needed to allow the repository program in Finland to proceed. It was also clear that there were a number of other elements beyond public involvement that contributed substantially to the success in Finland to date. And, in fact, it appeared that these other factors were also necessary to achieving the Finnish public acceptance. In other words, successful public participation and communication were necessary but not sufficient. What else was important? Culture, politics, and history vary from country to country, providing differing contexts for establishing and maintaining public confidence. What works in one country will not necessarily be effective in another. Nonetheless, there appear to be certain elements that might be common to programs that are successful in sustaining public confidence and some of
Nurturing Confidence in Preservice Elementary Science Teachers
NASA Astrophysics Data System (ADS)
Bleicher, Robert E.
2006-06-01
The purpose of this study was to examine changes in personal science teaching self-efficacy (PSTE), outcome expectancy (STOE), and science conceptual understanding and relationships among these in preservice teachers. Seventy preservice teachers enrolled in science teaching methods courses participated in this study. PSTE, STOE, and science conceptual understanding increased significantly during participation in the course. The study established that novice learners with minimal prior knowledge couldn't be expected to understand and employ core concepts in their learning schema without extensive guidance. The relationship between science learning confidence and science teaching confidence has not been theoretically delineated in the area of science teacher education. Findings suggest that there may be important connections between the two for preservice teachers that would be fruitful areas for future research.
Nurturing Confidence in Preservice Elementary Science Teachers
NASA Astrophysics Data System (ADS)
Bleicher, Robert E.
2007-12-01
This study examined changes in personal science teaching self-efficacy (PSTE), outcome expectancy (STOE), and science conceptual understanding and relationships among these in preservice teachers. Seventy preservice teachers enrolled in science teaching methods courses participated in this study. PSTE, STOE, and science conceptual understanding increased significantly during participation in the course. The study established that novice learners with minimal prior knowledge could not be expected to understand and employ core concepts in their learning schema without extensive guidance. The relationship between science learning confidence and science teaching confidence has not been theoretically delineated in the area of science teacher education. Findings suggest there may be important connections between the 2 for preservice teachers that would be fruitful areas for future research.
;Agreement; in the IPCC Confidence measure
NASA Astrophysics Data System (ADS)
Rehg, William; Staley, Kent
2017-02-01
The Intergovernmental Panel on Climate Change (IPCC) has, in its most recent Assessment Report (AR5), articulated guidelines for evaluating and communicating uncertainty that include a qualitative scale of confidence. We examine one factor included in that scale: the ;degree of agreement.; Some discussions of the degree of agreement in AR5 suggest that the IPCC is employing a consensus-oriented social epistemology. We consider the application of the degree of agreement factor in practice in AR5. Our findings, though based on a limited examination, suggest that agreement attributions do not so much track the overall consensus among investigators as the degree to which relevant research findings substantively converge in offering support for IPCC claims. We articulate a principle guiding confidence attributions in AR5 that centers not on consensus but on the notion of support. In concluding, we tentatively suggest a pluralist approach to the notion of support.
NASA's approach to flight confidence. [and safety
NASA Technical Reports Server (NTRS)
Roth, G. L.
1984-01-01
NASA's confidence in the flight readiness of aerospace hardware and software is achieved by a thorough integration of safety activities into every program facet, from concept through the mission. This involves technical and administrative personnel, organizations that specify requirements, design, manufacturing, test and the operators. Reviews by inhouse and external specialists form an integral part of the assurance process. Examples of safety issues and their resolution for some power and propulsion functions are given (lithium cells, autoignition/fretting in high pressure oxygen environments, ignition sources from auxiliary power unit, and low thrust rocket engines). Finally, some comments on NASA's integrated safety activities and the Aerospace Safety Advisory Panel's role in the NASA review and assessment process all of which provides added confidence in achieving a high level of mission safety and success.
NASA Technical Reports Server (NTRS)
Longman, Richard W.; Juang, Jer-Nan
1988-01-01
The realization theory is developed in a systematic manner for the Eigensystem Realization Algorithm (ERA) used for system identification. First, perturbation results are obtained which describe the linearized changes in the identified parameters resulting from small change in the data. Formulas are then derived that can be used to evaluate the variance of each of the identified parameters, assuming that the noise level is sufficiently low to allow the application of linearized results. These variances can be converted to give confidence intervals for each of the parameters for any chosen confidence level.
Confidence Sets for a Change-Point.
1986-10-01
probability credible set for j. In fact, even without the explicit evaluation in (1), one knows from a general theorem of Stein (1965) and Hora and...confidence sets with smallest expected measure, Ann. Statist. , 10, 1283-94. Hora , R. B. and Buehler, R. J. (1966), Fiducial theory and invariant...simple cumulative sum type statistic for the change-point problem -’-"C with zero -one observations, Biometrika 67, 79-84. Raferty, A. E. and Akman, V
Simultaneous Confidence Bands for Autoregressive Spectra.
1982-06-01
CONFIDENCE BANDS FOR AUTOREGRESSIVE SPECTRA H. Joseph Newton Marcello Pagano Institute of Statistics Department of Biostatistics Texas A&M University...AUTHOR(4) 8. CONTRACT OR GRANT NUMIUER(a) H. Joseph Newton and Marcello Pagano ONR N00014-82-MP-2001 ARU DAAG 29-80-C-0070 0. PERFORMING ORGANIZATION NAME...Parzen (1974), Uirych and Bishop (1975), and Beamish and Priestley (1981), for example) despite 1) a continuing discussion of the problems of order
Confidence as Bayesian Probability: From Neural Origins to Behavior.
Meyniel, Florent; Sigman, Mariano; Mainen, Zachary F
2015-10-07
Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or "read out" from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions.
Confidence-Based Learning in Investment Analysis
NASA Astrophysics Data System (ADS)
Serradell-Lopez, Enric; Lara-Navarra, Pablo; Castillo-Merino, David; González-González, Inés
The aim of this study is to determine the effectiveness of using multiple choice tests in subjects related to the administration and business management. To this end we used a multiple-choice test with specific questions to verify the extent of knowledge gained and the confidence and trust in the answers. The tests were performed in a group of 200 students at the bachelor's degree in Business Administration and Management. The analysis made have been implemented in one subject of the scope of investment analysis and measured the level of knowledge gained and the degree of trust and security in the responses at two different times of the course. The measurements have been taken into account different levels of difficulty in the questions asked and the time spent by students to complete the test. The results confirm that students are generally able to obtain more knowledge along the way and get increases in the degree of trust and confidence in the answers. It is confirmed as the difficulty level of the questions set a priori by the heads of the subjects are related to levels of security and confidence in the answers. It is estimated that the improvement in the skills learned is viewed favourably by businesses and are especially important for job placement of students.
On the Confidence Limit of Hilbert Spectrum
NASA Technical Reports Server (NTRS)
Huang, Norden
2003-01-01
Confidence limit is a routine requirement for Fourier spectral analysis. But this confidence limit is established based on ergodic theory: For stationary process, temporal average equals the ensemble average. Therefore, one can divide the data into n-sections and treat each section as independent realization. Most natural processes in general, and climate data in particular, are not stationary; therefore, there is a need for the Hilbert Spectral analysis for such processes. Here ergodic theory is no longer applicable. We propose to use various adjustable parameters in the shifting processes of the Empirical Mode Decomposition (EMD) method to obtain an ensemble of Intrinsic Mode Function 0 sets. Based on such an ensemble, we introduce a statistical measure in. a form of confidence limits for the Intrinsic Mode Functions, and consequently, the Hilbert spectra. The criterion of selecting the various adjustable parameters is based on the orthogonality test of the resulting M F sets. Length-of-day data from 1962 to 2001 will be used to illustrate this new approach. Its implication in climate data analysis will also be discussed.
Bernardi, L
2001-01-01
Interval hypoxic training (IHT) is a technique developed in the former Soviet Union, that consists of repeated exposures to 5-7 minutes of steady or progressive hypoxia, interrupted by equal periods of recovery. It has been proposed for training in sports, to acclimatize to high altitude, and to treat a variety of clinical conditions, spanning from coronary heart disease to Cesarean delivery. Some of these results may originate by the different effects of continuous vs. intermittent hypoxia (IH), which can be obtained by manipulating the repetition rate, the duration and the intensity of the hypoxic stimulus. The present article will attempt to examine some of the effects of IH, and, whenever possible, compare them to those of typical IHT. IH can modify oxygen transport and energy utilization, alter respiratory and blood pressure control mechanisms, induce permanent modifications in the cardiovascular system. IHT increases the hypoxic ventilatory response, increase red blood cell count and increase aerobic capacity. Some of these effects might be potentially beneficial in specific physiologic or pathologic conditions. At this stage, this technique appears interesting for its possible applications, but still largely to be explored for its mechanisms, potentials and limitations.
Computation of the intervals of uncertainties about the parameters found for identification
NASA Technical Reports Server (NTRS)
Mereau, P.; Raymond, J.
1982-01-01
A modeling method to calculate the intervals of uncertainty for parameters found by identification is described. The region of confidence and the general approach to the calculation of these intervals are discussed. The general subprograms for determination of dimensions are described. They provide the organizational charts for the subprograms, the tests carried out and the listings of the different subprograms.
NASA Technical Reports Server (NTRS)
Kraft, Ralph P.; Burrows, David N.; Nousek, John A.
1991-01-01
Two different methods, classical and Bayesian, for determining confidence intervals involving Poisson-distributed data are compared. Particular consideration is given to cases where the number of counts observed is small and is comparable to the mean number of background counts. Reasons for preferring the Bayesian over the classical method are given. Tables of confidence limits calculated by the Bayesian method are provided for quick reference.
Experimental uncertainty estimation and statistics for data having interval uncertainty.
Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)
2007-05-01
This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.
Bastian, Frederic B; Chibucos, Marcus C; Gaudet, Pascale; Giglio, Michelle; Holliday, Gemma L; Huang, Hong; Lewis, Suzanna E; Niknejad, Anne; Orchard, Sandra; Poux, Sylvain; Skunca, Nives; Robinson-Rechavi, Marc
2015-01-01
Biocuration has become a cornerstone for analyses in biology, and to meet needs, the amount of annotations has considerably grown in recent years. However, the reliability of these annotations varies; it has thus become necessary to be able to assess the confidence in annotations. Although several resources already provide confidence information about the annotations that they produce, a standard way of providing such information has yet to be defined. This lack of standardization undermines the propagation of knowledge across resources, as well as the credibility of results from high-throughput analyses. Seeded at a workshop during the Biocuration 2012 conference, a working group has been created to address this problem. We present here the elements that were identified as essential for assessing confidence in annotations, as well as a draft ontology--the Confidence Information Ontology--to illustrate how the problems identified could be addressed. We hope that this effort will provide a home for discussing this major issue among the biocuration community. Tracker URL: https://github.com/BgeeDB/confidence-information-ontology Ontology URL: https://raw.githubusercontent.com/BgeeDB/confidence-information-ontology/master/src/ontology/cio-simple.obo
Comparing interval estimates for small sample ordinal CFA models
Natesan, Prathiba
2015-01-01
Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading
Mediated Sources of Public Confidence: Lazarsfeld and Merton Revisited.
ERIC Educational Resources Information Center
Simonson, Peter
1999-01-01
Contributes to scholarship on mass media's role in generating public confidence. Discusses the current crisis of confidence, confidence as "faith-together," varied routes by which media confer status, and ways both journalistic expose and public debate can generate cynicism and undercut public confidence. Sketches three types of civil…
Engineering Student Self-Assessment through Confidence-Based Scoring
ERIC Educational Resources Information Center
Yuen-Reed, Gigi; Reed, Kyle B.
2015-01-01
A vital aspect of an answer is the confidence that goes along with it. Misstating the level of confidence one has in the answer can have devastating outcomes. However, confidence assessment is rarely emphasized during typical engineering education. The confidence-based scoring method described in this study encourages students to both think about…
Lyons-Amos, Mark; Padmadas, Sabu S; Durrant, Gabriele B
2014-01-01
Objectives To test the contraceptive confidence hypothesis in a modern context. The hypothesis is that women using effective or modern contraceptive methods have increased contraceptive confidence and hence a shorter interval between marriage and first birth than users of ineffective or traditional methods. We extend the hypothesis to incorporate the role of abortion, arguing that it acts as a substitute for contraception in the study context. Setting Moldova, a country in South-East Europe. Moldova exhibits high use of traditional contraceptive methods and abortion compared with other European countries. Participants Data are from a secondary analysis of the 2005 Moldovan Demographic and Health Survey, a nationally representative sample survey. 5377 unmarried women were selected. Primary and secondary outcome measures The outcome measure was the interval between marriage and first birth. This was modelled using a piecewise-constant hazard regression, with abortion and contraceptive method types as primary variables along with relevant sociodemographic controls. Results Women with high contraceptive confidence (modern method users) have a higher cumulative hazard of first birth 36 months following marriage (0.88 (0.87 to 0.89)) compared with women with low contraceptive confidence (traditional method users, cumulative hazard: 0.85 (0.84 to 0.85)). This is consistent with the contraceptive confidence hypothesis. There is a higher cumulative hazard of first birth among women with low (0.80 (0.79 to 0.80)) and moderate abortion propensities (0.76 (0.75 to 0.77)) than women with no abortion propensity (0.73 (0.72 to 0.74)) 24 months after marriage. Conclusions Effective contraceptive use tends to increase contraceptive confidence and is associated with a shorter interval between marriage and first birth. Increased use of abortion also tends to increase contraceptive confidence and shorten birth duration, although this effect is non-linear—women with a very high
Can We Confidently Study VO2 Kinetics in Young People?
Fawkner, Samantha G.; Armstrong, Neil
2007-01-01
The study of VO2 kinetics offers the potential to non-invasively examine the cardiorespiratory and metabolic response to dynamic exercise and limitations to every day physical activity. Its non-invasive nature makes it hugely attractive for use with young people, both healthy and those with disease, and yet the literature, whilst growing with respect to adults, remains confined to a cluster of studies with these special populations. It is most likely that this is partly due to the methodological difficulties involved in studying VO2 kinetics in young people which are not present, or present to a lesser degree, with adults. This article reviews these methodological issues, and explains the main procedures that might be used to overcome them. Key pointsThe VO2 kinetic response to exercise represents the combined efficiency of the cardiovascular, pulmonary and metabolic systems, and an accurate assessment of the response potentially provides a great deal of useful information via non-invasive methodology.An accurate assessment of the VO2 kinetic response is however inherently difficult with children and especially those with reduced exercise tolerance, due primarily to the apparent breath-by-breath noise which masks the true underlying physiological response, and the small amplitudes of the response signal.Despite this, it is possible to assess and quantify the VO2 kinetic response with children if appropriate steps are taken to apply carefully selected methodologies and report response variables with confidence intervals. In this way, both the researcher and the reader can be confident that the data reported is meaningful. PMID:24149413
Vaccination Confidence and Parental Refusal/Delay of Early Childhood Vaccines
Gilkey, Melissa B.; McRee, Annie-Laurie; Magnus, Brooke E.; Reiter, Paul L.; Dempsey, Amanda F.; Brewer, Noel T.
2016-01-01
Objective To support efforts to address parental hesitancy towards early childhood vaccination, we sought to validate the Vaccination Confidence Scale using data from a large, population-based sample of U.S. parents. Methods We used weighted data from 9,354 parents who completed the 2011 National Immunization Survey. Parents reported on the immunization history of a 19- to 35-month-old child in their households. Healthcare providers then verified children’s vaccination status for vaccines including measles, mumps, and rubella (MMR), varicella, and seasonal flu. We used separate multivariable logistic regression models to assess associations between parents’ mean scores on the 8-item Vaccination Confidence Scale and vaccine refusal, vaccine delay, and vaccination status. Results A substantial minority of parents reported a history of vaccine refusal (15%) or delay (27%). Vaccination confidence was negatively associated with refusal of any vaccine (odds ratio [OR] = 0.58, 95% confidence interval [CI], 0.54–0.63) as well as refusal of MMR, varicella, and flu vaccines specifically. Negative associations between vaccination confidence and measures of vaccine delay were more moderate, including delay of any vaccine (OR = 0.81, 95% CI, 0.76–0.86). Vaccination confidence was positively associated with having received vaccines, including MMR (OR = 1.53, 95% CI, 1.40–1.68), varicella (OR = 1.54, 95% CI, 1.42–1.66), and flu vaccines (OR = 1.32, 95% CI, 1.23–1.42). Conclusions Vaccination confidence was consistently associated with early childhood vaccination behavior across multiple vaccine types. Our findings support expanding the application of the Vaccination Confidence Scale to measure vaccination beliefs among parents of young children. PMID:27391098
Temporal binding of interval markers
Derichs, Christina; Zimmermann, Eckart
2016-01-01
How we estimate the passage of time is an unsolved mystery in neuroscience. Illusions of subjective time provide an experimental access to this question. Here we show that time compression and expansion of visually marked intervals result from a binding of temporal interval markers. Interval markers whose onset signals were artificially weakened by briefly flashing a whole-field mask were bound in time towards markers with a strong onset signal. We explain temporal compression as the consequence of summing response distributions of weak and strong onset signals. Crucially, temporal binding occurred irrespective of the temporal order of weak and strong onset markers, thus ruling out processing latencies as an explanation for changes in interval duration judgments. If both interval markers were presented together with a mask or the mask was shown in the temporal interval center, no compression occurred. In a sequence of two intervals, masking the middle marker led to time compression for the first and time expansion for the second interval. All these results are consistent with a model view of temporal binding that serves a functional role by reducing uncertainty in the final estimate of interval duration. PMID:27958311
Diagnosing Anomalous Network Performance with Confidence
Settlemyer, Bradley W; Hodson, Stephen W; Kuehn, Jeffery A; Poole, Stephen W
2011-04-01
Variability in network performance is a major obstacle in effectively analyzing the throughput of modern high performance computer systems. High performance interconnec- tion networks offer excellent best-case network latencies; how- ever, highly parallel applications running on parallel machines typically require consistently high levels of performance to adequately leverage the massive amounts of available computing power. Performance analysts have usually quantified network performance using traditional summary statistics that assume the observational data is sampled from a normal distribution. In our examinations of network performance, we have found this method of analysis often provides too little data to under- stand anomalous network performance. Our tool, Confidence, instead uses an empirically derived probability distribution to characterize network performance. In this paper we describe several instances where the Confidence toolkit allowed us to understand and diagnose network performance anomalies that we could not adequately explore with the simple summary statis- tics provided by traditional measurement tools. In particular, we examine a multi-modal performance scenario encountered with an Infiniband interconnection network and we explore the performance repeatability on the custom Cray SeaStar2 interconnection network after a set of software and driver updates.
Towards Measurement of Confidence in Safety Cases
NASA Technical Reports Server (NTRS)
Denney, Ewen; Paim Ganesh J.; Habli, Ibrahim
2011-01-01
Arguments in safety cases are predominantly qualitative. This is partly attributed to the lack of sufficient design and operational data necessary to measure the achievement of high-dependability targets, particularly for safety-critical functions implemented in software. The subjective nature of many forms of evidence, such as expert judgment and process maturity, also contributes to the overwhelming dependence on qualitative arguments. However, where data for quantitative measurements is systematically collected, quantitative arguments provide far more benefits over qualitative arguments, in assessing confidence in the safety case. In this paper, we propose a basis for developing and evaluating integrated qualitative and quantitative safety arguments based on the Goal Structuring Notation (GSN) and Bayesian Networks (BN). The approach we propose identifies structures within GSN-based arguments where uncertainties can be quantified. BN are then used to provide a means to reason about confidence in a probabilistic way. We illustrate our approach using a fragment of a safety case for an unmanned aerial system and conclude with some preliminary observations
Triage of OCR results using confidence scores
NASA Astrophysics Data System (ADS)
Sarkar, Prateek; Baird, Henry S.; Henderson, John
2001-12-01
We describe a technique for modeling the character recognition accuracy of an OCR system -- treated as a black box -- on a particular page of printed text based on an examination only of the output top-choice character classifications and, for each, a confidence score such as is supplied by many commercial OCR systems. Latent conditional independence (LCI) models perform better on this task, in our experience, than naive uniform thresholding methods. Given a sufficiently large and representative dataset of OCR (errorful) output and manually proofed (correct) text, we can automatically infer LCI models that exhibit a useful degree of reliability. A collaboration between a PARC research group and a Xerox legacy conversion service bureau has demonstrated that such models can significantly improve the productivity of human proofing staff by triaging -- that is, selecting to bypass manual inspection -- pages whose estimated OCR accuracy exceeds a threshold chosen to ensure that a customer-specified per-page accuracy target will be met with sufficient confidence. We report experimental results on over 1400 pages. Our triage software tools are running in production and will be applied to more than 5 million pages of multi-lingual text.
The 2010 Retirement Confidence Survey: confidence stabilizing, but preparations continue to erode.
Helman, Ruth; Copeland, Craig; VanDerhei, Jack
2010-03-01
20TH ANNUAL RCS: The 2010 Retirement Confidence Survey-the 20th annual wave of this survey-finds that the record-low confidence levels measured during the past two years of economic decline appear to have bottomed out. The percentage of workers veryconfident about having enough money for a comfortable retirement has stabilized at 16 percent, which is statistically equivalent to the 20-year low of 13 percent measured in 2009 (Fig. 1, pg. 7). Retiree confidence about having a financially secure retirement has also stabilized, with 19 percent saying now they are very confident (statistically equivalent to the 20 percent measured in 2009) (Fig. 2, pg. 8). Worker confidence about paying for basic expenses in retirement has rebounded slightly, with 29 percent now saying they are very confident about having enough money to pay for basic expenses during retirement (up from 25 percent in 2009, but still down from 34 percent in 2008) (Fig. 3, pg. 9). PREPARATIONS STILL ERODING: Fewer workers report that they and/or their spouse have saved for retirement (69 percent, down from 75 percent in 2009 but statistically equivalent to 72 percent in 2008) (Fig. 11, page 14). Moreover, fewer workers say that they and/or their spouse are currently saving for retirement (60 percent, down from 65 percent in 2009 but statistically equivalent to percentages measured in other years) (Fig. 13, pg. 15). MORE PEOPLE HAVE NO SAVINGS AT ALL: An increased percentage of workers report they have virtually no savings and investments. Among RCS workers providing this type of information, 27 percent say they have less than $1,000 in savings (up from 20 percent in 2009). In total, more than half of workers (54 percent) report that the total value of their household's savings and investments, excluding the value of their primary home and any defined benefit plans, is less than $25,000 (Fig. 14, pg. 16). CLUELESS ABOUT SAVINGS GOALS: Many workers continue to be unaware of how much they need to save for
Confidence and rejection in automatic speech recognition
NASA Astrophysics Data System (ADS)
Colton, Larry Don
Automatic speech recognition (ASR) is performed imperfectly by computers. For some designated part (e.g., word or phrase) of the ASR output, rejection is deciding (yes or no) whether it is correct, and confidence is the probability (0.0 to 1.0) of it being correct. This thesis presents new methods of rejecting errors and estimating confidence for telephone speech. These are also called word or utterance verification and can be used in wordspotting or voice-response systems. Open-set or out-of-vocabulary situations are a primary focus. Language models are not considered. In vocabulary-dependent rejection all words in the target vocabulary are known in advance and a strategy can be developed for confirming each word. A word-specific artificial neural network (ANN) is shown to discriminate well, and scores from such ANNs are shown on a closed-set recognition task to reorder the N-best hypothesis list (N=3) for improved recognition performance. Segment-based duration and perceptual linear prediction (PLP) features are shown to perform well for such ANNs. The majority of the thesis concerns vocabulary- and task-independent confidence and rejection based on phonetic word models. These can be computed for words even when no training examples of those words have been seen. New techniques are developed using phoneme ranks instead of probabilities in each frame. These are shown to perform as well as the best other methods examined despite the data reduction involved. Certain new weighted averaging schemes are studied but found to give no performance benefit. Hierarchical averaging is shown to improve performance significantly: frame scores combine to make segment (phoneme state) scores, which combine to make phoneme scores, which combine to make word scores. Use of intermediate syllable scores is shown to not affect performance. Normalizing frame scores by an average of the top probabilities in each frame is shown to improve performance significantly. Perplexity of the wrong
Automatic Error Analysis Using Intervals
ERIC Educational Resources Information Center
Rothwell, E. J.; Cloud, M. J.
2012-01-01
A technique for automatic error analysis using interval mathematics is introduced. A comparison to standard error propagation methods shows that in cases involving complicated formulas, the interval approach gives comparable error estimates with much less effort. Several examples are considered, and numerical errors are computed using the INTLAB…
Children's Discrimination of Melodic Intervals.
ERIC Educational Resources Information Center
Schellenberg, E. Glenn; Trehub, Sandra E.
1996-01-01
Adults and children listened to tone sequences and were required to detect changes either from intervals with simple frequency ratios to intervals with complex ratios or vice versa. Adults performed better on changes from simple to complex ratios than on the reverse changes. Similar performance was observed for 6-year olds who had never taken…
Interval Recognition in Minimal Context.
ERIC Educational Resources Information Center
Shatzkin, Merton
1984-01-01
Music majors were asked to identify interval when it was either preceded or followed by a tone moving in the same direction. Difficulties in interval recognition in context appear to be an effect not just of placement within the context or of tonality, but of particular combinations of these aspects. (RM)
Modal confidence factor in vibration testing
NASA Technical Reports Server (NTRS)
Ibrahim, S. R.
1978-01-01
The modal confidence factor (MCF) is a number calculated for every identified mode for a structure under test. The MCF varies from 0.00 for a distorted nonlinear, or noise mode to 100.0 for a pure structural mode. The theory of the MCF is based on the correlation that exists between the modal deflection at a certain station and the modal deflection at the same station delayed in time. The theory and application of the MCF are illustrated by two experiments. The first experiment deals with simulated responses from a two-degree-of-freedom system with 20%, 40%, and 100% noise added. The second experiment was run on a generalized payload model. The free decay response from the payload model contained 22% noise.
VARIABLE TIME-INTERVAL GENERATOR
Gross, J.E.
1959-10-31
This patent relates to a pulse generator and more particularly to a time interval generator wherein the time interval between pulses is precisely determined. The variable time generator comprises two oscillators with one having a variable frequency output and the other a fixed frequency output. A frequency divider is connected to the variable oscillator for dividing its frequency by a selected factor and a counter is used for counting the periods of the fixed oscillator occurring during a cycle of the divided frequency of the variable oscillator. This defines the period of the variable oscillator in terms of that of the fixed oscillator. A circuit is provided for selecting as a time interval a predetermined number of periods of the variable oscillator. The output of the generator consists of a first pulse produced by a trigger circuit at the start of the time interval and a second pulse marking the end of the time interval produced by the same trigger circuit.
TIME-INTERVAL MEASURING DEVICE
Gross, J.E.
1958-04-15
An electronic device for measuring the time interval between two control pulses is presented. The device incorporates part of a previous approach for time measurement, in that pulses from a constant-frequency oscillator are counted during the interval between the control pulses. To reduce the possible error in counting caused by the operation of the counter gating circuit at various points in the pulse cycle, the described device provides means for successively delaying the pulses for a fraction of the pulse period so that a final delay of one period is obtained and means for counting the pulses before and after each stage of delay during the time interval whereby a plurality of totals is obtained which may be averaged and multplied by the pulse period to obtain an accurate time- Interval measurement.
Germany and America: Crisis of confidence
Asmus, R.D.
1991-02-01
The paper examines the deterioration in German-American relations. The reasons for this downturn in German-American relations are quite simple. Washington views the Persian Gulf crisis as a defining moment in European-American relations and in the creation of a new world order. It is also the first diplomatic test of a unified Germany and a new German-American relationship. It is a test that Germany is thus far seen as having failed for three reasons. First, from the outset many Americans sensed that Germans did not comprehend what this crisis meant for the United States. A second and, in many ways, more worrying factor was the growing sense that the Germans were not being good Europeans. The third and most serious American concern, however, was the unsettling appearance of a very selective German definition of collective defense and common security. The result has been a crisis of confidence in the performance of the German political elite that goes beyond the problems in German-American relations during the early 1980s and the INF debate.
Modal confidence factor in vibration testing
NASA Technical Reports Server (NTRS)
Ibrahim, S. R.
1978-01-01
The theory and applications of a time domain modal test technique are presented. The method uses free decay of random responses from a structure under test to identify its modal characteristics namely, natural frequencies, damping factors, and mode shapes. The method can identify multimodal (highly coupled) systems and modes that have very small contribution in the responses. A method is presented to decrease the effects of high levels of noise in the data and thus improve the accuracy of identified parameters. This is accomplished using an oversized mathematical model. The concept of modal confidence factor (MCF) is developed. The MCF is a number calculated for every identified mode for a structure under test. The MCF varies from 0.000 for a distorted, nonlinear, or noise mode to 100.0 for a pure structural mode. The theory of the MCF is based on the correlation that exits between the modal deflection at a certain station and the modal deflection at the same station delayed in time. The theory and application of the MCF is illustrated by two experiments. The first experiment deals with simulated responses from a two degree of freedom system with 20 percent, 40 percent, and 100 percent noise added. The second experiment was run on a generalized payload model. The free decay response from the payload model contained about 22 percent noise.
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2015-01-01
A latent variable modeling procedure that can be used to evaluate intraclass correlation coefficients in two-level settings with discrete response variables is discussed. The approach is readily applied when the purpose is to furnish confidence intervals at prespecified confidence levels for these coefficients in setups with binary or ordinal…
Contrasting Academic Behavioural Confidence in Mexican and European Psychology Students
ERIC Educational Resources Information Center
Ochoa, Alma Rosa Aguila; Sander, Paul
2012-01-01
Introduction: Research with the Academic Behavioural Confidence scale using European students has shown that students have high levels of confidence in their academic abilities. It is generally accepted that people in more collectivist cultures have more realistic confidence levels in contrast to the overconfidence seen in individualistic European…
Opinion formation with time-varying bounded confidence.
Zhang, YunHong; Liu, QiPeng; Zhang, SiYing
2017-01-01
When individuals in social groups communicate with one another and are under the influence of neighbors' opinions, they typically revise their own opinions to adapt to such peer opinions. The individual threshold of bounded confidence will thus be affected by both a change in individual confidence and by neighbor influence. Individuals thus update their own opinions with new bounded confidence, while their updated opinions also influence their neighbors' opinions. Based on this reasoned factual assumption, we propose an opinion dynamics model with time-varying bounded confidence. A directed network is formed by the rule of the individual bounded confidence threshold. The threshold of individual bounded confidence involves both confidence variation and the in/out degree of the individual node. When the confidence variation is greater, an individual's confidence in persisting in his own opinion in interactions is weaker, and the individual is more likely to adopt neighbors' opinions. In networks, the in/out degree is determined by individual neighbors. Our main research involves the process of opinion evolution and the basic laws of opinion cluster formation. Group opinions converge exponentially to consensus with stable neighbors. An individual opinion evolution is determined by the average neighbor opinion effect strength. We also explore the conditions involved in forming a stable neighbor relationship and the influence of the confidence variation in the convergence of the threshold of bounded confidence. The results show that the influence on opinion evolution is greater with increased confidence variation.
Assessing Undergraduate Students' Conceptual Understanding and Confidence of Electromagnetics
ERIC Educational Resources Information Center
Leppavirta, Johanna
2012-01-01
The study examines how students' conceptual understanding changes from high confidence with incorrect conceptions to high confidence with correct conceptions when reasoning about electromagnetics. The Conceptual Survey of Electricity and Magnetism test is weighted with students' self-rated confidence on each item in order to infer how strongly…
Opinion formation with time-varying bounded confidence
Liu, QiPeng; Zhang, SiYing
2017-01-01
When individuals in social groups communicate with one another and are under the influence of neighbors’ opinions, they typically revise their own opinions to adapt to such peer opinions. The individual threshold of bounded confidence will thus be affected by both a change in individual confidence and by neighbor influence. Individuals thus update their own opinions with new bounded confidence, while their updated opinions also influence their neighbors’ opinions. Based on this reasoned factual assumption, we propose an opinion dynamics model with time-varying bounded confidence. A directed network is formed by the rule of the individual bounded confidence threshold. The threshold of individual bounded confidence involves both confidence variation and the in/out degree of the individual node. When the confidence variation is greater, an individual’s confidence in persisting in his own opinion in interactions is weaker, and the individual is more likely to adopt neighbors’ opinions. In networks, the in/out degree is determined by individual neighbors. Our main research involves the process of opinion evolution and the basic laws of opinion cluster formation. Group opinions converge exponentially to consensus with stable neighbors. An individual opinion evolution is determined by the average neighbor opinion effect strength. We also explore the conditions involved in forming a stable neighbor relationship and the influence of the confidence variation in the convergence of the threshold of bounded confidence. The results show that the influence on opinion evolution is greater with increased confidence variation. PMID:28264038
7 CFR 97.18 - Applications handled in confidence.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 3 2010-01-01 2010-01-01 false Applications handled in confidence. 97.18 Section 97.18 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING... confidence. (a) Pending applications shall be handled in confidence. Except as provided below, no...
49 CFR 1103.23 - Confidences of a client.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 49 Transportation 8 2010-10-01 2010-10-01 false Confidences of a client. 1103.23 Section 1103.23... Responsibilities Toward A Client § 1103.23 Confidences of a client. (a) The practitioner's duty to preserve his client's confidence outlasts the practitioner's employment by the client, and this duty extends to...
On how the brain decodes vocal cues about speaker confidence.
Jiang, Xiaoming; Pell, Marc D
2015-05-01
In speech communication, listeners must accurately decode vocal cues that refer to the speaker's mental state, such as their confidence or 'feeling of knowing'. However, the time course and neural mechanisms associated with online inferences about speaker confidence are unclear. Here, we used event-related potentials (ERPs) to examine the temporal neural dynamics underlying a listener's ability to infer speaker confidence from vocal cues during speech processing. We recorded listeners' real-time brain responses while they evaluated statements wherein the speaker's tone of voice conveyed one of three levels of confidence (confident, close-to-confident, unconfident) or were spoken in a neutral manner. Neural responses time-locked to event onset show that the perceived level of speaker confidence could be differentiated at distinct time points during speech processing: unconfident expressions elicited a weaker P2 than all other expressions of confidence (or neutral-intending utterances), whereas close-to-confident expressions elicited a reduced negative response in the 330-500 msec and 550-740 msec time window. Neutral-intending expressions, which were also perceived as relatively confident, elicited a more delayed, larger sustained positivity than all other expressions in the 980-1270 msec window for this task. These findings provide the first piece of evidence of how quickly the brain responds to vocal cues signifying the extent of a speaker's confidence during online speech comprehension; first, a rough dissociation between unconfident and confident voices occurs as early as 200 msec after speech onset. At a later stage, further differentiation of the exact level of speaker confidence (i.e., close-to-confident, very confident) is evaluated via an inferential system to determine the speaker's meaning under current task settings. These findings extend three-stage models of how vocal emotion cues are processed in speech comprehension (e.g., Schirmer & Kotz, 2006) by
Electrocardiographic QT interval and mortality: a meta-analysis
Zhang, Yiyi; Post, Wendy S.; Blasco-Colmenares, Elena; Dalal, Darshan; Tomaselli, Gordon F.; Guallar, Eliseo
2011-01-01
Background Extremely abnormal prolongation of the electrocardiographic QT interval is associated with malignant ventricular arrhythmias and sudden cardiac death. However, the implications of variations in QT-interval length within normal limits for mortality in the general population are still unclear. Methods We performed a meta-analysis to investigate the relation of QT interval with mortality endpoints. Inverse-variance weighted random-effects models were used to summarize the relative risks across studies. Twenty-three observational studies were included. Results The pooled relative risk estimates comparing the highest with the lowest categories of QT-interval length were 1.35 (95% confidence interval = 1.24–1.46) for total mortality, 1.51 (1.29–1.78) for cardiovascular mortality, 1.71 (1.36–2.15) for coronary heart disease mortality, and 1.44 (1.01–2.04) for sudden cardiac death. A 50 msec increase in QT interval was associated with a relative risk of 1.20 (1.15–1.26) for total mortality, 1.29 (1.15–1.46) for cardiovascular mortality, 1.49 (1.25–1.76) for coronary heart disease mortality, and 1.24 (0.97–1.60) for sudden cardiac death. Conclusions We found consistent associations between prolonged QT interval and increased risk of total, cardiovascular, coronary, and sudden cardiac death. QT-interval length is a determinant of mortality in the general population. PMID:21709561
Individual consistency in the accuracy and distribution of confidence judgments.
Ais, Joaquín; Zylberberg, Ariel; Barttfeld, Pablo; Sigman, Mariano
2016-01-01
We examine which aspects of the confidence distributions - its shape, its bias toward higher or lower values, and its ability to distinguish correct from erred trials - are idiosyncratic of the who (individual specificity), the when (variability across days) and the what (task specificity). Measuring confidence across different sessions of four different perceptual tasks we show that: (1) Confidence distributions are virtually identical when measured in different days for the same subject and the same task, constituting a subjective fingerprint, (2) The capacity of confidence reports to distinguish correct from incorrect responses is only modestly (but significantly) correlated when compared across tasks, (3) Confidence distributions are very similar for tasks that involve different sensory modalities but have similar structure, (4) Confidence accuracy is independent of the mean and width of the confidence distribution, (5) The mean of the confidence distribution (an individual's confidence bias) constitutes the most efficient indicator to infer a subject's identity from confidence reports and (6) Confidence bias measured in simple perceptual decisions correlates with an individual's optimism bias measured with standard questionnaire.
Confidence through consensus: a neural mechanism for uncertainty monitoring.
Paz, Luciano; Insabato, Andrea; Zylberberg, Ariel; Deco, Gustavo; Sigman, Mariano
2016-02-24
Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments that measure the sensory evidence's influence on choice and confidence contradict these classic models. We propose that the decision is taken by many loosely coupled modules each of which represent a stochastic sample of the sensory evidence integral. Confidence is then encoded in the dispersion between modules. We show that our proposal can account for the well established relations between confidence, and stimuli discriminability and reaction times, as well as the fluctuations influence on choice and confidence.
Relating confidence to information uncertainty in qualitative reasoning
Chavez, Gregory M; Zerkle, David K; Key, Brian P; Shevitz, Daniel W
2010-12-02
Qualitative reasoning makes use of qualitative assessments provided by subject matter experts to model factors such as security risk. Confidence in a result is important and useful when comparing competing security risk results. Quantifying the confidence in an evidential reasoning result must be consistent and based on the available information. A novel method is proposed to determine a qualitative measure of confidence in a qualitative reasoning result from the available information uncertainty in the result using membership values in the fuzzy sets of confidence. In this study information uncertainty is quantified through measures of non-specificity and conflict. Fuzzy values for confidence are established from information uncertainty values that lie between the measured minimum and maximum information uncertainty values. Measured values of information uncertainty in each result is used to obtain the confidence. The determined confidence values are used to compare competing scenarios and understand the influences on the desired result.
High resolution time interval meter
Martin, A.D.
1986-05-09
Method and apparatus are provided for measuring the time interval between two events to a higher resolution than reliability available from conventional circuits and component. An internal clock pulse is provided at a frequency compatible with conventional component operating frequencies for reliable operation. Lumped constant delay circuits are provided for generating outputs at delay intervals corresponding to the desired high resolution. An initiation START pulse is input to generate first high resolution data. A termination STOP pulse is input to generate second high resolution data. Internal counters count at the low frequency internal clock pulse rate between the START and STOP pulses. The first and second high resolution data are logically combined to directly provide high resolution data to one counter and correct the count in the low resolution counter to obtain a high resolution time interval measurement.
Finding Nested Common Intervals Efficiently
NASA Astrophysics Data System (ADS)
Blin, Guillaume; Stoye, Jens
In this paper, we study the problem of efficiently finding gene clusters formalized by nested common intervals between two genomes represented either as permutations or as sequences. Considering permutations, we give several algorithms whose running time depends on the size of the actual output rather than the output in the worst case. Indeed, we first provide a straightforward O(n 3) time algorithm for finding all nested common intervals. We reduce this complexity by providing an O(n 2) time algorithm computing an irredundant output. Finally, we show, by providing a third algorithm, that finding only the maximal nested common intervals can be done in linear time. Considering sequences, we provide solutions (modifications of previously defined algorithms and a new algorithm) for different variants of the problem, depending on the treatment one wants to apply to duplicated genes.
ERIC Educational Resources Information Center
Jackson, Dan; Bowden, Jack; Baker, Rose
2015-01-01
Moment-based estimators of the between-study variance are very popular when performing random effects meta-analyses. This type of estimation has many advantages including computational and conceptual simplicity. Furthermore, by using these estimators in large samples, valid meta-analyses can be performed without the assumption that the treatment…
We demonstrate how thermal-optical transmission analysis (TOT) for refractory light-absorbing carbon in atmospheric particulate matter was optimized with empirical response surface modeling. TOT employs pyrolysis to distinguish the mass of black carbon (BC) from organic carbon (...
ERIC Educational Resources Information Center
Jackson, Dan
2013-01-01
Statistical inference is problematic in the common situation in meta-analysis where the random effects model is fitted to just a handful of studies. In particular, the asymptotic theory of maximum likelihood provides a poor approximation, and Bayesian methods are sensitive to the prior specification. Hence, less efficient, but easily computed and…
ERIC Educational Resources Information Center
Dunst, Carl J.; Hamby, Deborah W.
2012-01-01
This paper includes a nontechnical description of methods for calculating effect sizes in intellectual and developmental disability studies. Different hypothetical studies are used to illustrate how null hypothesis significance testing (NHST) and effect size findings can result in quite different outcomes and therefore conflicting results. Whereas…
ERIC Educational Resources Information Center
Van Norman, Ethan R.
2016-01-01
Curriculum-based measurement of oral reading (CBM-R) progress monitoring data is used to measure student response to instruction. Federal legislation permits educators to use CBM-R progress monitoring data as a basis for determining the presence of specific learning disabilities. However, decision making frameworks originally developed for CBM-R…
ERIC Educational Resources Information Center
Zhou, Hong; Muellerleile, Paige; Ingram, Debra; Wong, Seok P.
2011-01-01
Intraclass correlation coefficients (ICCs) are commonly used in behavioral measurement and psychometrics when a researcher is interested in the relationship among variables of a common class. The formulas for deriving ICCs, or generalizability coefficients, vary depending on which models are specified. This article gives the equations for…
ERIC Educational Resources Information Center
Gu, Fei; Skorupski, William P.; Hoyle, Larry; Kingston, Neal M.
2011-01-01
Ramsay-curve item response theory (RC-IRT) is a nonparametric procedure that estimates the latent trait using splines, and no distributional assumption about the latent trait is required. For item parameters of the two-parameter logistic (2-PL), three-parameter logistic (3-PL), and polytomous IRT models, RC-IRT can provide more accurate estimates…
Technology Transfer Automated Retrieval System (TEKTRAN)
Slow canopy wilting in soybean has been identified as a potentially beneficial trait for ameliorating drought effects on yield. Previous research identified QTLs for slow wilting from two different bi-parental populations and this information was combined with data from three other populations to id...
2010-02-01
Army Materiel Systems Analysis Activity CDF - Cumulative Distribution Function ICEM - Integrated Casualty Estimation Model PDF - Probability...Casualty Estimation Model ( ICEM ) was used to evaluate the effectiveness of each helmet. This resulted in two binomially distributed statistics. For
Winsemius, David K
2006-01-01
Methods have been described for evaluating the statistical validity of insurance experience studies and the translation of medical literature studies to life insurance ratings. These have not seen widespread adoption in the insurance literature. This article is intended to encourage the use of these existing methods and describe other methods in the biostatistical literature.
ERIC Educational Resources Information Center
Zhang, Zhiyong; Yuan, Ke-Hai
2016-01-01
Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation…
Oxygen uptake in maximal effort constant rate and interval running.
Pratt, Daniel; O'Brien, Brendan J; Clark, Bradley
2013-01-01
This study investigated differences in average VO2 of maximal effort interval running to maximal effort constant rate running at lactate threshold matched for time. The average VO2 and distance covered of 10 recreational male runners (VO2max: 4158 ± 390 mL · min(-1)) were compared between a maximal effort constant-rate run at lactate threshold (CRLT), a maximal effort interval run (INT) consisting of 2 min at VO2max speed with 2 minutes at 50% of VO2 repeated 5 times, and a run at the average speed sustained during the interval run (CR submax). Data are presented as mean and 95% confidence intervals. The average VO2 for INT, 3451 (3269-3633) mL · min(-1), 83% VO2max, was not significantly different to CRLT, 3464 (3285-3643) mL · min(-1), 84% VO2max, but both were significantly higher than CR sub-max, 3464 (3285-3643) mL · min(-1), 76% VO2max. The distance covered was significantly greater in CLRT, 4431 (4202-3731) metres, compared to INT and CR sub-max, 4070 (3831-4309) metres. The novel finding was that a 20-minute maximal effort constant rate run uses similar amounts of oxygen as a 20-minute maximal effort interval run despite the greater distance covered in the maximal effort constant-rate run.
NASA Astrophysics Data System (ADS)
Hanna, Steven R.
Air quality models are used to make decisions regarding the construction of industrial plants, the types of fuel that will be burnt and the types of pollution control devices that will be used. It is important to know the uncertainties that are associated with these model predictions. Standard analytical methods found in elementary statistics textbooks for estimating uncertainties are generally not applicable since the distributions of performance measures related to air quality concentrations are not easily transformed to a Gaussian shape. This paper suggests several possible resampling procedures that can be used to calculate uncertainties or confidence limits on air quality model performance. In these resampling methods, many new data sets are drawn from the original data set using an empirical set of rules. A few alternate forms of the socalled bootstrap and jackknife resampling procedures are tested using a concocted data set with a Gaussian parent distributions, with the result that the jackknife is the most efficient procedure to apply, although its confidence bounds are slightly overestimated. The resampling procedures are then applied to predictions by seven air quality models for the Carpinteria coastal dispersion experiment. Confidence intervals on the fractional mean bias and the normalized mean square error are calculated for each model and for differences between models. It is concluded that these uncertainties are sometimes so large for data sets consisting of about 20 elements that it cannot be stated with 95% confidence that the performance measure for the 'best' model is significantly different from that for another model.
Puckett, Sarah L.; van Riper, Charles
2014-01-01
We examined the effects of a biologic control agent, the tamarisk leaf beetle (Diorhabda carinulata), on native avifauna in southwestern Colorado, specifically, addressing whether and to what degree birds eat tamarisk leaf beetles. In 2010, we documented avian foraging behavior, characterized the arthropod community, sampled bird diets, and undertook an experiment to determine whether tamarisk leaf beetles are palatable to birds. We observed that tamarisk leaf beetles compose 24.0 percent (95-percent-confidence interval, 19.9-27.4 percent) and 35.4 percent (95-percent-confidence interval, 32.4-45.1 percent) of arthropod abundance and biomass in the study area, respectively. Birds ate few tamarisk leaf beetles, despite a superabundance of D. carinulata in the environment. The frequency of occurrence of tamarisk leaf beetles in bird diets was 2.1 percent (95-percent-confidence interval, 1.3- 2.9 percent) by abundance and 3.4 percent (95-percent-confidence interval, 2.6-4.2 percent) by biomass. Thus, tamarisk leaf beetles probably do not contribute significantly to the diets of birds in areas where biologic control of tamarisk is being applied.
40 CFR 91.506 - Engine sample selection.
Code of Federal Regulations, 2010 CFR
2010-07-01
... paragraph (b)(2) of this section. It defines one-tail, 95 percent confidence intervals. σ=actual test sample... carry-over engine families: After one engine is tested, the manufacturer will combine the test with the last test result from the previous model year and then calculate the required sample size for the...
Measuring Patterns of Surgeon Confidence Using a Novel Assessment Tool.
Farrell, Timothy M; Ghaderi, Iman; McPhail, Lindsee E; Alger, Amy R; Meyers, Michael O; Meyer, Anthony A
2016-01-01
Confidence should increase during surgical training and practice. However, few data exist regarding confidence of surgeons across this continuum. Confidence may develop differently in clinical and personal domains, or may erode as specialization or age restricts practice. A reliable scale of confidence is needed to track this competency. A novel survey was distributed to surgeons in private and academic settings. One hundred and thirty-four respondents completed this cross-sectional survey. Surgeons reported anticipated reactions to clinical scenarios within three patient care domains (acute inpatient, nonacute inpatient, and outpatient) and in personal spheres. Confidence scores were plotted against years of experience. Curves of best fit were generated and trends assessed. A subgroup completed a second survey after four years to assess the survey's reliability over time. During residency, there is steep improvement in confidence reported by surgeons in all clinical domains, with further increase for inpatient domains during transition into practice. Confidence in personal spheres also increases quickly during residency and thereafter. The surgeon confidence scale captures the expected acquisition of confidence during early surgical experience, and will have value in following trends in surgeon confidence as training and practice patterns change.
Perri, Amanda M; O'Sullivan, Terri L; Harding, John C S; Wood, R Darren; Friendship, Robert M
2017-04-01
The evaluation of pig hematology and biochemistry parameters is rarely done largely due to the costs associated with laboratory testing and labor, and the limited availability of reference intervals needed for interpretation. Within-herd and between-herd biological variation of these values also make it difficult to establish reference intervals. Regardless, baseline reference intervals are important to aid veterinarians in the interpretation of blood parameters for the diagnosis and treatment of diseased swine. The objective of this research was to provide reference intervals for hematology and biochemistry parameters of 3-week-old commercial nursing piglets in Ontario. A total of 1032 pigs lacking clinical signs of disease from 20 swine farms were sampled for hematology and iron panel evaluation, with biochemistry analysis performed on a subset of 189 randomly selected pigs. The 95% reference interval, mean, median, range, and 90% confidence intervals were calculated for each parameter.
Anomalous Evidence, Confidence Change, and Theory Change.
Hemmerich, Joshua A; Van Voorhis, Kellie; Wiley, Jennifer
2016-08-01
A novel experimental paradigm that measured theory change and confidence in participants' theories was used in three experiments to test the effects of anomalous evidence. Experiment 1 varied the amount of anomalous evidence to see if "dose size" made incremental changes in confidence toward theory change. Experiment 2 varied whether anomalous evidence was convergent (of multiple types) or replicating (similar finding repeated). Experiment 3 varied whether participants were provided with an alternative theory that explained the anomalous evidence. All experiments showed that participants' confidence changes were commensurate with the amount of anomalous evidence presented, and that larger decreases in confidence predicted theory changes. Convergent evidence and the presentation of an alternative theory led to larger confidence change. Convergent evidence also caused more theory changes. Even when people do not change theories, factors pertinent to the evidence and alternative theories decrease their confidence in their current theory and move them incrementally closer to theory change.
High resolution time interval counter
NASA Technical Reports Server (NTRS)
Zhang, Victor S.; Davis, Dick D.; Lombardi, Michael A.
1995-01-01
In recent years, we have developed two types of high resolution, multi-channel time interval counters. In the NIST two-way time transfer MODEM application, the counter is designed for operating primarily in the interrupt-driven mode, with 3 start channels and 3 stop channels. The intended start and stop signals are 1 PPS, although other frequencies can also be applied to start and stop the count. The time interval counters used in the NIST Frequency Measurement and Analysis System are implemented with 7 start channels and 7 stop channels. Four of the 7 start channels are devoted to the frequencies of 1 MHz, 5 MHz or 10 MHz, while triggering signals to all other start and stop channels can range from 1 PPS to 100 kHz. Time interval interpolation plays a key role in achieving the high resolution time interval measurements for both counters. With a 10 MHz time base, both counters demonstrate a single-shot resolution of better than 40 ps, and a stability of better than 5 x 10(exp -12) (sigma(sub chi)(tau)) after self test of 1000 seconds). The maximum rate of time interval measurements (with no dead time) is 1.0 kHz for the counter used in the MODEM application and is 2.0 kHz for the counter used in the Frequency Measurement and Analysis System. The counters are implemented as plug-in units for an AT-compatible personal computer. This configuration provides an efficient way of using a computer not only to control and operate the counters, but also to store and process measured data.
1990-05-30
intervals for MOE data. These screening intevals will be incorporated into the rule-based AI system under development. The AI system is being designed to cite...data values for selected MOE according to the study experience of senior analysts. The distribution of MOE output data may be considered to be bounded ...regarding the concept of screening intervals primarily surfaced the notion of confidence intervals for estimation o population parameters using
Code of Federal Regulations, 2013 CFR
2013-07-01
... submitted the following results and the calculation shown in Equation 12: Run CE 1 94.2 2 97.6 3 90.5... for alternative CE protocols and test methods are presented in section 5. The recommended... Figure 1). This ensures that 95 percent of the time, when the DQO is met, the actual CE value will be...
Confidence measurement in the light of signal detection theory
Massoni, Sébastien; Gajdos, Thibault; Vergnaud, Jean-Christophe
2014-01-01
We compare three alternative methods for eliciting retrospective confidence in the context of a simple perceptual task: the Simple Confidence Rating (a direct report on a numerical scale), the Quadratic Scoring Rule (a post-wagering procedure), and the Matching Probability (MP; a generalization of the no-loss gambling method). We systematically compare the results obtained with these three rules to the theoretical confidence levels that can be inferred from performance in the perceptual task using Signal Detection Theory (SDT). We find that the MP provides better results in that respect. We conclude that MP is particularly well suited for studies of confidence that use SDT as a theoretical framework. PMID:25566135
Confidence measurement in the light of signal detection theory.
Massoni, Sébastien; Gajdos, Thibault; Vergnaud, Jean-Christophe
2014-01-01
We compare three alternative methods for eliciting retrospective confidence in the context of a simple perceptual task: the Simple Confidence Rating (a direct report on a numerical scale), the Quadratic Scoring Rule (a post-wagering procedure), and the Matching Probability (MP; a generalization of the no-loss gambling method). We systematically compare the results obtained with these three rules to the theoretical confidence levels that can be inferred from performance in the perceptual task using Signal Detection Theory (SDT). We find that the MP provides better results in that respect. We conclude that MP is particularly well suited for studies of confidence that use SDT as a theoretical framework.
The antecedents and belief-polarized effects of thought confidence.
Chou, Hsuan-Yi; Lien, Nai-Hwa; Liang, Kuan-Yu
2011-01-01
This article investigates 2 possible antecedents of thought confidence and explores the effects of confidence induced before or during ad exposure. The results of the experiments indicate that both consumers' dispositional optimism and spokesperson attractiveness have significant effects on consumers' confidence in thoughts that are generated after viewing the advertisement. Higher levels of thought confidence will influence the quality of the thoughts that people generate, lead to either positively or negatively polarized message processing, and therefore induce better or worse advertising effectiveness, depending on the valence of thoughts. The authors posit the belief-polarization hypothesis to explain these findings.
Misinterpreting eyewitness expressions of confidence: The featural justification effect.
Dodson, Chad S; Dobolyi, David G
2015-06-01
How do we know eyewitness statements of confidence are interpreted accurately by others? When eyewitnesses provide a verbal expression of confidence about a lineup identification, such as I'm fairly certain it's him, how well do others understand the intended meaning of this statement of confidence? And, how is this perception of the meaning influenced by justifications of the level of confidence, such as when eyewitnesses say, I remember his chin? The answers to these questions are unknown, as there is no research on how others interpret the intended meaning of eyewitness confidence. Three experiments show that an additional justification of confidence, relative to seeing a confidence statement alone, can increase misunderstanding in others' estimation of the meaning of the expression of confidence. Moreover, this justification-induced increase in misunderstanding only occurs when the justification refers to an observable facial feature and not when it refers to an unobservable quality (e.g., He is very familiar). Even more noteworthy, both Experiments 2 and 3 show that this featural justification effect is strongest when eyewitnesses express absolute certainty in an identification, such as by stating I am positive. When a highly confident assertion is accompanied by a featural justification others will be most likely to misinterpret the intended meaning.
Relating confidence to measured information uncertainty in qualitative reasoning
Chavez, Gregory M; Zerkle, David K; Key, Brian P; Shevitz, Daniel W
2010-10-07
Qualitative reasoning makes use of qualitative assessments provided by subject matter experts to model factors such as security risk. Confidence in a result is important and useful when comparing competing results. Quantifying the confidence in an evidential reasoning result must be consistent and based on the available information. A novel method is proposed to relate confidence to the available information uncertainty in the result using fuzzy sets. Information uncertainty can be quantified through measures of non-specificity and conflict. Fuzzy values for confidence are established from information uncertainty values that lie between the measured minimum and maximum information uncertainty values.
Cortical alpha activity predicts the confidence in an impending action
Kubanek, Jan; Hill, N. Jeremy; Snyder, Lawrence H.; Schalk, Gerwin
2015-01-01
When we make a decision, we experience a degree of confidence that our choice may lead to a desirable outcome. Recent studies in animals have probed the subjective aspects of the choice confidence using confidence-reporting tasks. These studies showed that estimates of the choice confidence substantially modulate neural activity in multiple regions of the brain. Building on these findings, we investigated the neural representation of the confidence in a choice in humans who explicitly reported the confidence in their choice. Subjects performed a perceptual decision task in which they decided between choosing a button press or a saccade while we recorded EEG activity. Following each choice, subjects indicated whether they were sure or unsure about the choice. We found that alpha activity strongly encodes a subject's confidence level in a forthcoming button press choice. The neural effect of the subjects' confidence was independent of the reaction time and independent of the sensory input modeled as a decision variable. Furthermore, the effect is not due to a general cognitive state, such as reward expectation, because the effect was specifically observed during button press choices and not during saccade choices. The neural effect of the confidence in the ensuing button press choice was strong enough that we could predict, from independent single trial neural signals, whether a subject was going to be sure or unsure of an ensuing button press choice. In sum, alpha activity in human cortex provides a window into the commitment to make a hand movement. PMID:26283892
Assessment of elderly people in general practice. 3. Confiding relationships.
Iliffe, S; Haines, A; Stein, A; Gallivan, S
1991-01-01
Little is known about the importance of confiding relationships in elderly people. Associations between lack of confiding relationships and depression, lifestyle characteristics, medication use, and contacts with doctors were studied by interviewing a random sample of 235 elderly people aged 75 years and over registered with nine general practices in inner London. It was found that men were not significantly more likely than women to report lack of confiding relationships. Married people of both sexes were more likely to have confiding relationships than those who were single, separated, divorced or widowed. Depression was not associated with lack of a confiding relationship, but those lacking such relationships were significantly more likely to smoke, and were prescribed significantly more medicines than those with confiding relationships. Individuals without a confiding relationship were significantly less likely to admit to any alcohol consumption in the previous three months, suggesting that alcohol consumption in this age group is largely a social phenomenon. Confiding relationships do not appear to confer strong protection against depression and a question on confiding relationships should not therefore be routinely incorporated into surveillance programmes for elderly people in the community. PMID:1807305
EEG interpretation reliability and interpreter confidence: a large single-center study.
Grant, Arthur C; Abdel-Baki, Samah G; Weedon, Jeremy; Arnedo, Vanessa; Chari, Geetha; Koziorynska, Ewa; Lushbough, Catherine; Maus, Douglas; McSween, Tresa; Mortati, Katherine A; Reznikov, Alexandra; Omurtag, Ahmet
2014-03-01
The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both the intrarater reliability and the interrater reliability of EEG interpretation based on the interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations and investigated sources of variance in EEG interpretations. During two distinct time intervals, six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies were re-interpreted to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen's kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. Cohen's kappa ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. Cohen's kappa was not significantly different across rater pairs (chi-square=17.3, df=14, p=0.24). Variance due to subjects (i.e., EEGs) was 65.3%, due to readers was 3.9%, and due to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary, but insufficient, condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated online application integrated into a continuing medical education module that measures and reports EEG I&IR to individual
Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants.
Cortese, Aurelio; Amano, Kaoru; Koizumi, Ai; Lau, Hakwan; Kawato, Mitsuo
2017-04-01
Neurofeedback studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the multi-voxel pattern decoding approach, allowing for fMRI to serve as a tool to manipulate fine-grained neural activity embedded in voxel patterns. Because of its tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Specifically, can the same participants learn to induce neural patterns in opposite directions in different sessions? If so, how does previous learning affect subsequent induction effectiveness? These questions are critical because neurofeedback effects can last for months, but the short- to mid-term dynamics of such effects are unknown. Here we employed a within-subjects design, where participants underwent two DecNef training sessions to induce behavioural changes of opposing directionality (up or down regulation of perceptual confidence in a visual discrimination task), with the order of training counterbalanced across participants. Behavioral results indicated that the manipulation was strongly influenced by the order and the directionality of neurofeedback training. We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional confidence changes in different sessions within single participants. Furthermore, the effect of up- compared to down-regulation was more prominent, and confidence changes (regardless of the direction) were largely preserved even after a week-long interval. Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference. These results are interpreted in the framework
Cancer mortality in workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin
Fingerhut, M.A.; Halperin, W.E.; Marlow, D.A.; Piacitelli, L.A.; Honchar, P.A.; Sweeney, M.H.; Greife, A.L.; Dill, P.A.; Steenland, K.; Suruda, A.J. )
1991-01-24
In both animal and epidemiologic studies, exposure to dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin, or TCDD) has been associated with an increased risk of cancer. We conducted a retrospective cohort study of mortality among the 5172 workers at 12 plants in the United States that produced chemicals contaminated with TCDD. Occupational exposure was documented by reviewing job descriptions and by measuring TCDD in serum from a sample of 253 workers. Causes of death were taken from death certificates. Mortality from several cancers previously associated with TCDD (stomach, liver, and nasal cancers, Hodgkin's disease, and non-Hodgkin's lymphoma) was not significantly elevated in this cohort. Mortality from soft-tissue sarcoma was increased, but not significantly (4 deaths; standardized mortality ratio (SMR), 338; 95 percent confidence interval, 92 to 865). In the subcohort of 1520 workers with greater than or equal to 1 year of exposure and greater than or equal to 20 years of latency, however, mortality was significantly increased for soft-tissue sarcoma (3 deaths; SMR, 922; 95 percent confidence interval, 190 to 2695) and for cancers of the respiratory system (SMR, 142; 95 percent confidence interval, 103 to 192). Mortality from all cancers combined was slightly but significantly elevated in the overall cohort (SMR, 115; 95 percent confidence interval, 102 to 130) and was higher in the subcohort with greater than or equal to 1 year of exposure and greater than or equal to 20 years of latency (SMR, 146; 95 percent confidence interval, 121 to 176). This study of mortality among workers with occupational exposure to TCDD does not confirm the high relative risks reported for many cancers in previous studies. Conclusions about an increase in the risk of soft-tissue sarcoma are limited by small numbers and misclassification on death certificates.
Music Education Preservice Teachers' Confidence in Resolving Behavior Problems
ERIC Educational Resources Information Center
Hedden, Debra G.
2015-01-01
The purpose of this study was to investigate whether there would be a change in preservice teachers' (a) confidence concerning the resolution of behavior problems, (b) tactics for resolving them, (c) anticipation of problems, (d) fears about management issues, and (e) confidence in methodology and pedagogy over the time period of a one-semester…
Confidence and memory: assessing positive and negative correlations.
Roediger, Henry L; DeSoto, K Andrew
2014-01-01
The capacity to learn and remember surely evolved to help animals solve problems in their quest to reproduce and survive. In humans we assume that metacognitive processes also evolved, so that we know when to trust what we remember (i.e., when we have high confidence in our memories) and when not to (when we have low confidence). However this latter feature has been questioned by researchers, with some finding a high correlation between confidence and accuracy in reports from memory and others finding little to no correlation. In two experiments we report a recognition memory paradigm that, using the same materials (categorised lists), permits the study of positive correlations, zero correlations, and negative correlations between confidence and accuracy within the same procedure. We had subjects study words from semantic categories with the five items most frequently produced in norms omitted from the list; later, subjects were given an old/new recognition test and made confidence ratings on their judgements. Although the correlation between confidence and accuracy for studied items was generally positive, the correlation for the five omitted items was negative in some methods of analysis. We pinpoint the similarity between lures and targets as creating inversions between confidence and accuracy in memory. We argue that, while confidence is generally a useful indicant of accuracy in reports from memory, in certain environmental circumstances even adaptive processes can foster illusions of memory. Thus understanding memory illusions is similar to understanding perceptual illusions: Processes that are usually adaptive can go awry under certain circumstances.
The Metamemory Approach to Confidence: A Test Using Semantic Memory
ERIC Educational Resources Information Center
Brewer, William F.; Sampaio, Cristina
2012-01-01
The metamemory approach to memory confidence was extended and elaborated to deal with semantic memory tasks. The metamemory approach assumes that memory confidence is based on the products and processes of a completed memory task, as well as metamemory beliefs that individuals have about how their memory products and processes relate to memory…
Confidence Sharing in the Vocational Counselling Interview: Emergence and Repercussions
ERIC Educational Resources Information Center
Olry-Louis, Isabelle; Bremond, Capucine; Pouliot, Manon
2012-01-01
Confidence sharing is an asymmetrical dialogic episode to which both parties consent, in which one reveals something personal to the other who participates in the emergence and unfolding of the confidence. We describe how this is achieved at a discursive level within vocational counselling interviews. Based on a corpus of 64 interviews, we analyse…
True and False Memories, Parietal Cortex, and Confidence Judgments
ERIC Educational Resources Information Center
Urgolites, Zhisen J.; Smith, Christine N.; Squire, Larry R.
2015-01-01
Recent studies have asked whether activity in the medial temporal lobe (MTL) and the neocortex can distinguish true memory from false memory. A frequent complication has been that the confidence associated with correct memory judgments (true memory) is typically higher than the confidence associated with incorrect memory judgments (false memory).…
A (revised) confidence index for the forecasting of meteor showers
NASA Astrophysics Data System (ADS)
Vaubaillon, J.
2016-01-01
A confidence index for the forecasting of meteor showers is presented. The goal is to provide users with information regarding the way the forecasting is performed, so several degrees of confidence is achieved. This paper presents the meaning of the index coding system.
True and false memories, parietal cortex, and confidence judgments.
Urgolites, Zhisen J; Smith, Christine N; Squire, Larry R
2015-11-01
Recent studies have asked whether activity in the medial temporal lobe (MTL) and the neocortex can distinguish true memory from false memory. A frequent complication has been that the confidence associated with correct memory judgments (true memory) is typically higher than the confidence associated with incorrect memory judgments (false memory). Accordingly, it has often been difficult to know whether a finding is related to memory confidence or memory accuracy. In the current study, participants made recognition memory judgments with confidence ratings in response to previously studied scenes and novel scenes. The left hippocampus and 16 other brain regions distinguished true and false memories when confidence ratings were different for the two conditions. Only three regions (all in the parietal cortex) distinguished true and false memories when confidence ratings were equated. These findings illustrate the utility of taking confidence ratings into account when identifying brain regions associated with true and false memories. Neural correlates of true and false memories are most easily interpreted when confidence ratings are similar for the two kinds of memories.
Confidence set interference with a prior quadratic bound. [in geophysics
NASA Technical Reports Server (NTRS)
Backus, George E.
1989-01-01
Neyman's (1937) theory of confidence sets is developed as a replacement for Bayesian interference (BI) and stochastic inversion (SI) when the prior information is a hard quadratic bound. It is recommended that BI and SI be replaced by confidence set interference (CSI) only in certain circumstances. The geomagnetic problem is used to illustrate the general theory of CSI.
Utilitarian Model of Measuring Confidence within Knowledge-Based Societies
ERIC Educational Resources Information Center
Jack, Brady Michael; Hung, Kuan-Ming; Liu, Chia Ju; Chiu, Houn Lin
2009-01-01
This paper introduces a utilitarian confidence testing statistic called Risk Inclination Model (RIM) which indexes all possible confidence wagering combinations within the confines of a defined symmetrically point-balanced test environment. This paper presents the theoretical underpinnings, a formal derivation, a hypothetical application, and…
Confidence Scoring of Speaking Performance: How Does Fuzziness become Exact?
ERIC Educational Resources Information Center
Jin, Tan; Mak, Barley; Zhou, Pei
2012-01-01
The fuzziness of assessing second language speaking performance raises two difficulties in scoring speaking performance: "indistinction between adjacent levels" and "overlap between scales". To address these two problems, this article proposes a new approach, "confidence scoring", to deal with such fuzziness, leading to "confidence" scores between…
Prospective Teachers' Problem Solving Skills and Self-Confidence Levels
ERIC Educational Resources Information Center
Gursen Otacioglu, Sena
2008-01-01
The basic objective of the research is to determine whether the education that prospective teachers in different fields receive is related to their levels of problem solving skills and self-confidence. Within the mentioned framework, the prospective teachers' problem solving and self-confidence levels have been examined under several variables.…
Confidence Limits for Maximum Word-Recognition Scores.
ERIC Educational Resources Information Center
Dubno, Judy R.; And Others
1995-01-01
This experiment sought to define a confidence limit for maximum word-recognition scores obtained from 212 young and elderly adults with confirmed cochlear hearing loss. A 95% confidence limit was found and supported through analysis, although it is suggested that, in some cases, word recognition should be measured at additional levels to obtain…
Information and Communication: Tools for Increasing Confidence in the Schools.
ERIC Educational Resources Information Center
Achilles, C. M.; Lintz, M. N.
Beginning with a review of signs and signals of public attitudes toward American education over the last 15 years, this paper analyzes some concerns regarding public confidence in public schools. Following a brief introduction, issues involved in the definition and behavioral attributes of confidence are mentioned. A synopsis of three approaches…