scholarly journals Variability of Vomitoxin in Truckloads of Wheat in a Wheat Scab Epidemic Year

Plant Disease ◽  
1998 ◽  
Vol 82 (6) ◽  
pp. 625-630 ◽  
Author(s):  
L. P. Hart ◽  
O. Schabenberger
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Gibberella Zeae ◽  
Wheat Scab ◽  
Wheat Products ◽  
Serious Disease ◽  
Ground Wheat ◽  
Precise Assessment ◽  
The U.S ◽  
Sample Estimate

Wheat scab, caused by Gibberella zeae, has been a serious disease in parts of the Midwest. One factor contributing to the importance of wheat scab is the contamination of grain by the mycotoxin vomitoxin (deoxynivalenol, DON), a toxic secondary metabolite. The U.S. Food and Drug Administration advisory levels for vomitoxin in wheat and wheat products require an accurate and precise assessment of vomitoxin concentration. In this study, randomly collecting probes of wheat from individual trucks and analyzing the ground wheat from each probe produced significantly less variability than subsampling and analyzing 50 g of whole kernels from the probes. The variability introduced by subsampling the probes and analyzing 50 g of whole kernels affects the precision and confidence of vomitoxin estimates. Tables of confidence intervals were developed for different sampling and subsampling patterns. To be 95% certain that the true vomitoxin concentration does not exceed the sample estimate by 1 μg/g, analyzing either four individual probes or 5–12% subsamples of these probes would be sufficient. To increase the accuracy to about 0.5 μg/g, either an analysis of seven probes or a 5–12% subsample of 10 probes would be necessary, based on a one-sided confidence interval.

Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Genetic Model ◽  
Original Data ◽  
Nonparametric Bootstrap ◽  
Test Protocol ◽  
Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

Evaluation of Modification of the 3M™ Molecular Detection Assay (MDA) Salmonella Method (2013.09) for the Detection of Salmonella in Selected Foods: Collaborative Study

2014 ◽  
Vol 97 (5) ◽  
pp. 1329-1342 ◽  
Author(s):  
Patrick Bird ◽  
Kiel Fisher ◽  
Megan Boyle ◽  
Travis Huffman ◽  
M Joseph Benzinger ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Molecular Detection ◽  
Collaborative Study ◽  
Ground Beef ◽  
Inoculum Level ◽  
Test Portion ◽  
Low Level ◽  
Dog Food ◽  
Detection Assay ◽  
The U.S

Abstract The 3M™ Molecular Detection Assay (MDA) Salmonella utilizes isothermal amplification of nucleic acid sequences with high specificity, efficiency, rapidity and bioluminescence to detect amplification of Salmonella spp. in food, food-related, and environmental samples after enrichment. A method modification and matrix extension study of the previously approved AOAC Official MethodSM 2013.09 was conducted, and approval of the modification was received on March 20, 2014. Using an unpaired study design in a multilaboratory collaborative study, the 3M MDA Salmonella method was compared to the U.S. Department of Agriculture/Food Safety and Inspection Service (USDA/FSIS) Microbiology Laboratory Guidebook (MLG) 4.05 (2011), Isolation and Identification of Salmonella from Meat, Poultry, Pasteurized Egg, and Catfish Products for raw ground beef and the U.S. Food and Drug Administration (FDA)/Bacteriological Analytical Manual (BAM) Chapter 5, Salmonella reference method for wet dog food following the current AOAC guidelines. A total of 20 laboratories participated. For the 3M MDA Salmonella method, raw ground beef was analyzed using 25 g test portions, and wet dog food was analyzed using 375 g test portions. For the reference methods, 25 g test portions of each matrix were analyzed. Each matrix was artificially contaminated with Salmonella at three inoculation levels: an uninoculated control level (0 CFU/test portion), a low inoculum level (0.2–2 CFU/test portion), and a high inoculum level (2–5 CFU/test portion). In this study, 1512 unpaired replicate samples were analyzed. Statistical analysis was conducted according to the probability of detection (POD). For the low-level raw ground beef test portions, the following dLPOD (difference between the LPODs of the reference and candidate method) values with 95% confidence intervals were obtained: –0.01 (–0.14, +0.12). For the low-level wet dog food test portions, the following dLPOD with 95% confidence intervals were obtained: –0.04 (–0.16, +0.09). No significant differences were observed in the number of positive samples detected by the 3M MDA Salmonella method versus either the USDA/FSIS-MLG or FDA/BAM methods.

Measuring Pump Efficiency: Uncertainty Considerations

2005 ◽  
Vol 127 (4) ◽  
pp. 280-284 ◽  
Author(s):  
Noah D. Manring
Keyword(s):  
Experimental Data ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Academic Research ◽  
Pump Efficiency ◽  
The Past ◽  
Industrial Sales ◽  
Efficiency Data

The objective of this paper is to analyze the uncertainty associated with pump efficiency measurements and to determine reasonable confidence intervals for these data. In the past, many industrial sales and some pieces of academic research have been based upon the experimental data of pump efficiencies; yet few have questioned the accuracy of the experimental data and no one has provided a confidence interval which reflects the range of uncertainty in the measurement. In this paper, a method for calculating this confidence interval is presented and it is shown that substantially large confidence intervals exist within the testing results of a pump. Furthermore, it is recommended that these confidence intervals be included with the efficiency data whenever it is reported.

scholarly journals Assessment of decrease in renal function associated with hypothyroidism

2021 ◽  
Vol 12 (1) ◽  
pp. 275-286
Author(s):  
Ayesha Ammar ◽  
Kahkashan Bashir Mir ◽  
Sadaf Batool ◽  
Noreen Marwat ◽  
Maryam Saeed ◽  
...  
Keyword(s):  
Renal Function ◽  
Confidence Interval ◽  
Creatinine Clearance ◽  
Confidence Intervals ◽  
Standard Error ◽  
Radioactive Iodine ◽  
T Test ◽  
Euthyroid State ◽  
Significant Difference ◽  
The Mean

Objective: Study was aimed to see the effects of hypothyroidism on GFR as a renal function. Material and methods: Total of Fifty-eight patients were included in the study. Out of those forty-eight patients were female and the rest were male. Out of fifty eight patients, fifty three patients were of thyroid cancer in which hypothyroidism was due to discontinuation of thyroxine before the administration of radioactive iodine for Differentiated thyroid cancer.Moreover, remaining five patients were post radioactive iodine treatment (for hyperthyroidism) hypothyroid. All of the patients were above eighteen years of age with TSH value > 30µIU/ml. Pregnant and lactating females were excluded.Renal function tests (urea/creatinine, creatinine clearance) and serum electrolytes followed by Tc-99m-DTPA renal scan for GFR assessment (GATES’ method) were carried out in all subjects twice during the study, One study during hypothyroid state (TSH > 30 µIU/ml) and other during euthyroid state (TSH between 0.4 to 4µ IU/ml). The results of Student’s t-test showed significant difference in renal functions (Urea, creatinine, creatinine clearance, GFR values) in euthyroid state and hypothyroid state (p-value <0.05). RESULTS: In case of creatinine the paired t test reveal the mean 1.014±0.428, with standard error of 0.669 within 95% confidence interval, for creatinine clearance 80.11±14.12 with standard error of 1.94 within 95% confidence intervals, for urea the mean 28±12.13 with standard error of 1.607 within 95% confidence intervals and for GFR for individual kidney is 38.056±8.56 with standard error of 1.3717 within 95% confidence interval. There was no difference in the outcome of the 2 groups. Conclusion: Hypothyroidism impairs renal function to a significant level and hence needs to be prevented and corrected as early as possible.

scholarly journals Statistical analysis of cytogenetic data

2021 ◽  
Vol 28 ◽  
pp. 146-150
Author(s):  
L. A. Atramentova
Keyword(s):  
Data Structure ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Statistical Method ◽  
Confidence Intervals ◽  
Study Design ◽  
Cytogenetic Study ◽  
Data Set ◽  
Statistical Errors ◽  
Cytogenetic Data

Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample indicators with confidence intervals instead of statistical errors. Attention is drawn to the need to take into account the size of the analyzed shares when choosing a statistical method. It shows how the same data set can be analyzed in different ways depending on the purpose of the study. The algorithm of correct statistical analysis and the form of the tabular presentation of the results are described. Keywords: data structure, numerically unbalanced complex, confidence interval.

scholarly journals Coverage versus Confidence

2021 ◽  
Vol 23 ◽  
Author(s):  
Peyton Cook
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Coverage Probability ◽  
Negative Binomial ◽  
Asymptotic Confidence Interval ◽  
Population Proportion ◽  
Coverage Probabilities ◽  
The Mean

This article is intended to help students understand the concept of a coverage probability involving confidence intervals. Mathematica is used as a language for describing an algorithm to compute the coverage probability for a simple confidence interval based on the binomial distribution. Then, higher-level functions are used to compute probabilities of expressions in order to obtain coverage probabilities. Several examples are presented: two confidence intervals for a population proportion based on the binomial distribution, an asymptotic confidence interval for the mean of the Poisson distribution, and an asymptotic confidence interval for a population proportion based on the negative binomial distribution.

scholarly journals On the Correction of the Asymptotic Distribution of the Likelihood Ratio Statistic If Nuisance Parameters Are Estimated Based on an External Source

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Marianne Jonker ◽  
Aad Van der Vaart
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Confidence Intervals ◽  
Likelihood Ratio ◽  
Statistical Models ◽  
Likelihood Ratio Statistic ◽  
External Source ◽  
Nuisance Parameters ◽  
Testing Hypotheses

AbstractIn practice, nuisance parameters in statistical models are often replaced by estimates based on an external source, for instance if estimates were published before or a second dataset is available. Next these estimates are assumed to be known when the parameter of interest is estimated, a hypothesis is tested or confidence intervals are constructed. By this assumption, the level of the test is, in general, higher than supposed and the coverage of the confidence interval is too low. In this article, we derive the asymptotic distribution of the likelihood ratio statistic if the nuisance parameters are estimated based on a dataset that is independent of the data used for estimating the parameter of interest. This distribution can be used for correctly testing hypotheses and constructing confidence intervals. Four theoretical and practical examples are given as illustration.

Statistical software fault management based on bootstrap confidence intervals

2020 ◽  
Vol 37 (6/7) ◽  
pp. 905-923
Author(s):  
Tadashi Dohi ◽  
Hiroyuki Okamura ◽  
Cun Hua Qian
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Fault Management ◽  
Parametric Bootstrap ◽  
Software Systems ◽  
Statistical Software ◽  
System Availability ◽  
Content Type ◽  
Software Rejuvenation ◽  
Software Fault

PurposeIn this paper, the authors propose two construction methods to estimate confidence intervals of the time-based optimal software rejuvenation policy and its associated maximum system availability via a parametric bootstrap method. Through simulation experiments the authors investigate their asymptotic behaviors and statistical properties.Design/methodology/approachThe present paper is the first challenge to derive the confidence intervals of the optimal software rejuvenation schedule, which maximizes the system availability in the sense of long run. In other words, the authors concern the statistical software fault management by employing an idea of process control in quality engineering and a parametric bootstrap.FindingsAs a remarkably different point from the existing work, the authors carefully take account of a special case where the two-sided confidence interval of the optimal software rejuvenation time does not exist due to that fact that the estimator distribution of the optimal software rejuvenation time is defective. Here the authors propose two useful construction methods of the two-sided confidence interval: conditional confidence interval and heuristic confidence interval.Research limitations/implicationsAlthough the authors applied a simulation-based bootstrap confidence method in this paper, another re-sampling-based approach can be also applied to the same problem. In addition, the authors just focused on a parametric bootstrap, but a non-parametric bootstrap method can be also applied to the confidence interval estimation of the optimal software rejuvenation time interval, when the complete knowledge on the distribution form is not available.Practical implicationsThe statistical software fault management techniques proposed in this paper are useful to control the system availability of operational software systems, by means of the control chart.Social implicationsThrough the online monitoring in operational software systems, it would be possible to estimate the optimal software rejuvenation time and its associated system availability, without applying any approximation. By implementing this function on application programming interface (API), it is possible to realize the low-cost fault-tolerance for software systems with aging.Originality/valueIn the past literature, almost all authors employed parametric and non-parametric inference techniques to estimate the optimal software rejuvenation time but just focused on the point estimation. This may often lead to the miss-judgment based on over-estimation or under-estimation under uncertainty. The authors overcome the problem by introducing the two-sided confidence interval approach.

Breast cancer overdiagnosis in stop-screen trials: More uncertainty than previously reported

2020 ◽  
pp. 096914132095078
Author(s):  
Stuart G Baker ◽  
Philip C Prorok
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Confidence Interval ◽  
Breast Cancer Screening ◽  
Confidence Intervals ◽  
Latent Class ◽  
Screening Program ◽  
Screen Interval ◽  
Binomial Confidence Interval

Objective According to the Independent UK Panel on Breast Cancer Screening, the most reliable estimates of overdiagnosis for breast cancer screening come from stop-screen trials Canada 1, Canada 2, and Malmo. The screen-interval overdiagnosis fraction is the fraction of cancers in a screening program that are overdiagnosed. We used the cumulative incidence method to estimate screen-interval overdiagnosis fraction. Our goal was to derive confidence intervals for estimated screen-interval overdiagnosis fraction and adjust for refusers in these trials. Methods We first show that the UK Panel’s use of a 95% binomial confidence interval for estimated screen-interval overdiagnosis fraction was incorrect. We then derive a correct 95% binomial-Poisson confidence interval. We also use the method of latent-class instrumental variables to adjust for refusers. Results For the Canada 1 trial, the estimated screen-interval overdiagnosis fraction was 0.23 with a 95% binomial confidence interval of (0.18, 0.27) and a 95% binomial-Poisson confidence interval of (0.04, 0.41). For the Canada 2 trial, the estimated screen-interval overdiagnosis fraction was 0.16 with a 95% binomial confidence interval of (0.12, 0.19) and a 95% binomial-Poisson confidence interval of (−0.01, 0.32). For the Malmo trial, the estimated screen-interval overdiagnosis fraction was 0.19 with a 95% binomial confidence interval of (0.15, 0.22). Adjusting for refusers, the estimated screen-interval overdiagnosis fraction was 0.26 with a 95% binomial-Poisson confidence interval of (0.03, 0.50). Conclusion The correct 95% binomial-Poisson confidence interval s for the estimated screen-interval overdiagnosis fraction based on the Canada 1, Canada 2, and Malmo stop-screen trials are much wider than the previously reported incorrect 95% binomial confidence intervals. The 95% binomial-Poisson confidence intervals widen as follow-up time increases, an unappreciated downside of longer follow-up in stop-screen trials.

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