scholarly journals Evidence-Based Psychiatry: Paraclinical Diagnostics of Asthenic Syndrome in Schizophrenia Based on the Determination of Leukocyte-Inhibitory Index

Psychiatry ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 6-12
Author(s):  
A. N. Simonov ◽  
T. P. Klyushnik ◽  
S. A. Zozulya
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Clinical Signs ◽  
Total Sample ◽  
Delta Method ◽  
Proteolytic System ◽  
Leukocyte Elastase ◽  
Objective Criterion ◽  
Regression Methods ◽  
Symptom Complex

A leukocyte-inhibitory index (LII) is the ratio of the proteolytic enzyme leukocyte elastase (LE) to its inhibitor, an α1- proteinase inhibitor (α1-PI). LII characterizes the activity of the proteolytic system and can be considered as a potential objective criterion that determines both the course and the outcome of the disease. The changes of LII in schizophrenia patients with clinically diagnosed asthenia (schizoasthenia) and patients with schizophrenia without clinical signs of this syndrome were revealed. The objective: to study the possibility of the 95% confidence intervals for a comparative assessment of LII in patients with schizoasthenia and patients with schizophrenia without clinical signs of asthenic syndrome to obtain correct statistical conclusions. Patients and methods: Overall, 95 patients aged 20–55 years with paroxysmal-progressive (F20.x1) and paranoid (F20.00) schizophrenia were examined: 61 patients in the total sample were clinically diagnosed with asthenic symptom-complex. The enzymatic activity of LE and the functional activity of α1-PI were determined in blood serum. LII was calculated according to the formula. The confidence intervals were built using 4 different methods: Fieller’s theorem, delta method, regression methods and bootstrap method. Results: the statistical analysis indicates that the 95% confidence intervals of these indicators for the examined patient groups do not overlap. Therefore, these indicators relate to different populations, which mean the examined groups are characterized by different variants of the ratio of the proteolytic system components. Conclusion: the assessment of LII can serve as an objective statistically correct criterion for presence or absence of asthenic disorder in patients with schizophrenia in addition to clinical examination.

scholarly journals Evaluation of Inter-Observer Reliability of Animal Welfare Indicators: Which Is the Best Index to Use?

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1445
Author(s):  
Mauro Giammarino ◽  
Silvana Mattiello ◽  
Monica Battini ◽  
Piero Quatto ◽  
Luca Maria Battaglini ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Dairy Goat ◽  
Microsoft Excel ◽  
Bootstrap Methods ◽  
Welfare Indicators ◽  
Observer Reliability ◽  
High Concordance ◽  
Variance Estimates

This study focuses on the problem of assessing inter-observer reliability (IOR) in the case of dichotomous categorical animal-based welfare indicators and the presence of two observers. Based on observations obtained from Animal Welfare Indicators (AWIN) project surveys conducted on nine dairy goat farms, and using udder asymmetry as an indicator, we compared the performance of the most popular agreement indexes available in the literature: Scott’s π, Cohen’s k, kPABAK, Holsti’s H, Krippendorff’s α, Hubert’s Γ, Janson and Vegelius’ J, Bangdiwala’s B, Andrés and Marzo’s ∆, and Gwet’s γ(AC1). Confidence intervals were calculated using closed formulas of variance estimates for π, k, kPABAK, H, α, Γ, J, ∆, and γ(AC1), while the bootstrap and exact bootstrap methods were used for all the indexes. All the indexes and closed formulas of variance estimates were calculated using Microsoft Excel. The bootstrap method was performed with R software, while the exact bootstrap method was performed with SAS software. k, π, and α exhibited a paradoxical behavior, showing unacceptably low values even in the presence of very high concordance rates. B and γ(AC1) showed values very close to the concordance rate, independently of its value. Both bootstrap and exact bootstrap methods turned out to be simpler compared to the implementation of closed variance formulas and provided effective confidence intervals for all the considered indexes. The best approach for measuring IOR in these cases is the use of B or γ(AC1), with bootstrap or exact bootstrap methods for confidence interval calculation.

scholarly journals Which particles to select, and if yes, how many?

2021 ◽  
Author(s):  
Christian Schwaferts ◽  
Patrick Schwaferts ◽  
Elisabeth von der Esch ◽  
Martin Elsner ◽  
Natalia P. Ivleva
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Analytical Techniques ◽  
Single Particles ◽  
Minimal Sample ◽  
Error Quantification ◽  
Measurement Algorithm ◽  
Reliable Quantification ◽  
Window Selection ◽  
Number Of Particles

AbstractMicro- and nanoplastic contamination is becoming a growing concern for environmental protection and food safety. Therefore, analytical techniques need to produce reliable quantification to ensure proper risk assessment. Raman microspectroscopy (RM) offers identification of single particles, but to ensure that the results are reliable, a certain number of particles has to be analyzed. For larger MP, all particles on the Raman filter can be detected, errors can be quantified, and the minimal sample size can be calculated easily by random sampling. In contrast, very small particles might not all be detected, demanding a window-based analysis of the filter. A bootstrap method is presented to provide an error quantification with confidence intervals from the available window data. In this context, different window selection schemes are evaluated and there is a clear recommendation to employ random (rather than systematically placed) window locations with many small rather than few larger windows. Ultimately, these results are united in a proposed RM measurement algorithm that computes confidence intervals on-the-fly during the analysis and, by checking whether given precision requirements are already met, automatically stops if an appropriate number of particles are identified, thus improving efficiency.

scholarly journals PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI

2014 ◽  
Vol 3 (4) ◽  
pp. 130
Author(s):  
NI MADE METTA ASTARI ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Regression Analysis ◽  
Statistical Analysis ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Least Square ◽  
Prediction Tool ◽  
Analysis Method ◽  
Ordinary Least Square ◽  
Independent Variable ◽  
Parameter Estimators

Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ??OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.

scholarly journals Parametric Bootstrap Methods for Estimating Model Parameters of Non-homogeneous Gamma Process

2018 ◽  
Vol 3 (2) ◽  
pp. 167-176 ◽  
Author(s):  
Yasuhiro Saito ◽  
Tadashi Dohi
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Parametric Bootstrap ◽  
Gamma Process ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Trend Function ◽  
Bootstrap Methods ◽  
Parametric Bootstrapping ◽  
Simulation Based

Non-Homogeneous Gamma Process (NHGP) is characterized by an arbitrary trend function and a gamma renewal distribution. In this paper, we estimate the confidence intervals of model parameters of NHGP via two parametric bootstrap methods: simulation-based approach and re-sampling-based approach. For each bootstrap method, we apply three methods to construct the confidence intervals. Through simulation experiments, we investigate each parametric bootstrapping and each construction method of confidence intervals in terms of the estimation accuracy. Finally, we find the best combination to estimate the model parameters in trend function and gamma renewal distribution in NHGP.

Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions

2013 ◽  
pp. 92-102
Author(s):  
Yalin Jiao ◽  
Yongmin Zhong ◽  
Shesheng Gao ◽  
Bijan Shirinzadeh
Keyword(s):  
Confidence Intervals ◽  
Coverage Probability ◽  
Bootstrap Method ◽  
Experimental Results ◽  
Discrete Distributions ◽  
Estimation Accuracy ◽  
Weighting Method ◽  
Random Weighting Method ◽  
Random Weighting ◽  
The Bootstrap Method

This paper presents a new random weighting method for estimation of one-sided confidence intervals in discrete distributions. It establishes random weighting estimations for the Wald and Score intervals. Based on this, a theorem of coverage probability is rigorously proved by using the Edgeworth expansion for random weighting estimation of the Wald interval. Experimental results demonstrate that the proposed random weighting method can effectively estimate one-sided confidence intervals, and the estimation accuracy is much higher than that of the bootstrap method.

scholarly journals Confidence intervals for the coefficient of L-variation in hydrological applications

2010 ◽  
Vol 14 (11) ◽  
pp. 2229-2242 ◽  
Author(s):  
A. Viglione
Keyword(s):  
Confidence Intervals ◽  
Frequency Analysis ◽  
Bootstrap Method ◽  
Flood Frequency ◽  
Point Estimation ◽  
Regional Frequency Analysis ◽  
Flood Frequency Analysis ◽  
Hydrological Variable ◽  
Regional Flood Frequency Analysis ◽  
T Distribution

Abstract. The coefficient of L-variation (L-CV) is commonly used in statistical hydrology, in particular in regional frequency analysis, as a measure of steepness for the frequency curve of the hydrological variable of interest. As opposed to the point estimation of the L-CV, in this work we are interested in the estimation of the interval of values (confidence interval) in which the L-CV is included at a given level of probability (confidence level). Several candidate distributions are compared in terms of their suitability to provide valid estimators of confidence intervals for the population L-CV. Monte-Carlo simulations of synthetic samples from distributions frequently used in hydrology are used as a basis for the comparison. The best estimator proves to be provided by the log-Student t distribution whose parameters are estimated without any assumption on the underlying parent distribution of the hydrological variable of interest. This estimator is shown to also outperform the non parametric bias-corrected and accelerated bootstrap method. An illustrative example of how this result can be used in hydrology is presented, namely in the comparison of methods for regional flood frequency analysis. In particular, it is shown that the confidence intervals for the L-CV can be used to assess the amount of spatial heterogeneity of flood data not explained by regionalization models.

Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms

2020 ◽  
Vol 68 (3) ◽  
pp. 949-964
Author(s):  
Dimitris Bertsimas ◽  
Bradley Sturt
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Synthetic Data ◽  
Randomized Algorithm ◽  
Data Sets ◽  
Bootstrap Confidence Intervals ◽  
Integer Points ◽  
Deterministic Algorithms ◽  
New Perspective ◽  
The Bootstrap Method

The bootstrap method is one of the major developments in statistics in the 20th century for computing confidence intervals directly from data. However, the bootstrap method is traditionally approximated with a randomized algorithm, which can sometimes produce inaccurate confidence intervals. In “Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms,” Bertsimas and Sturt present a new perspective of the bootstrap method through the lens of counting integer points in a polyhedron. Through this perspective, the authors develop the first computational complexity results and efficient deterministic approximation algorithm (fully polynomial time approximation scheme) for bootstrap confidence intervals, which unlike traditional methods, has guaranteed bounds on its error. In experiments on real and synthetic data sets from clinical trials, the proposed deterministic algorithms quickly produce reliable confidence intervals, which are significantly more accurate than those from randomization.

Confidence Intervals for Weibull Fast-Fracture Parameters

2002 ◽  
Author(s):  
Mandar Chati ◽  
Curtis Johnson ◽  
Ahmet Kaya ◽  
Bjoern Schenk
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Likelihood Ratio ◽  
Failure Modes ◽  
Bootstrap Method ◽  
Parametric Bootstrap ◽  
Nonparametric Bootstrap ◽  
Weibull Parameters ◽  
Fracture Data ◽  
Fast Fracture

Practical limits on number of specimens that can be tested lead to uncertainty in the estimated Weibull parameters. This paper presents an evaluation of four techniques for estimating confidence intervals for size-scaled Weibull parameters of monolithic ceramics. The techniques include normal approximation method, likelihood ratio technique, nonparametric bootstrap, and parametric bootstrap methods. For uncensored fast-fracture data, the confidence intervals for Weibull parameters are compared to the method used in ASTM Standard C1239. A simulation fracture experiment is conducted to evaluate the statistical characteristics, in particular coverage probability, of the four methods. For fast-fracture data with multiple failure modes, the statistical assessment of the confidence interval techniques for size-scaled Weibull parameters complement the existing literature. Overall, it was observed that the likelihood ratio technique and parametric bootstrap method perform very well. These techniques can also be extended for confidence interval estimation using fast-fracture data obtained from various geometry’s of test specimens and/or loading conditions (pooled data).

Reliability-Based Design Optimization With Confidence Level for Non-Gaussian Distributions Using Bootstrap Method

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Yoojeong Noh ◽  
Kyung K. Choi ◽  
Ikjin Lee ◽  
David Gorsich ◽  
David Lamb
Keyword(s):  
Statistical Model ◽  
Design Optimization ◽  
Confidence Intervals ◽  
Confidence Level ◽  
Bootstrap Method ◽  
Random Variables ◽  
Reliability Based Design Optimization ◽  
Gaussian Distributions ◽  
Reliability Based Design ◽  
Non Gaussian

For reliability-based design optimization (RBDO), generating an input statistical model with confidence level has been recently proposed to offset inaccurate estimation of the input statistical model with Gaussian distributions. For this, the confidence intervals for the mean and standard deviation are calculated using Gaussian distributions of the input random variables. However, if the input random variables are non-Gaussian, use of Gaussian distributions of the input variables will provide inaccurate confidence intervals, and thus yield an undesirable confidence level of the reliability-based optimum design meeting the target reliability βt. In this paper, an RBDO method using a bootstrap method, which accurately calculates the confidence intervals for the input parameters for non-Gaussian distributions, is proposed to obtain a desirable confidence level of the output performance for non-Gaussian distributions. The proposed method is examined by testing a numerical example and M1A1 Abrams tank roadarm problem.

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