scholarly journals Prefabricated Concrete Component Geometry Deviation Statistical Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaoyong Luo ◽  
Hao Long ◽  
Shuang Dong ◽  
Jingyi Wu
Keyword(s):  
Goodness Of Fit ◽  
Transverse Dimension ◽  
Cumulative Distribution ◽  
Component Size ◽  
Significant Difference ◽  
Composite Slabs ◽  
Kolmogorov Smirnov ◽  
3D Photogrammetry ◽  
Geometry Deviation ◽  
Concrete Component

The research objects of this paper were the prefabricated concrete components produced by four enterprises in China, and the dimension deviation data of more than 1400 prefabricated concrete components are measured with high-precision 3D photogrammetry technology. The nonparametric Kruskal–Wallis test was carried out for the size deviation of the same type of components produced by different enterprises. The distribution characteristics of geometric parameters of typical components of prefabricated structures in China, such as beams, columns, wall boards, and composite slabs, were analyzed by using the probability statistical method. The Kolmogorov–Smirnov goodness-of-fit method was used to test the cumulative distribution function of dimension deviation, and the size distribution of fabricated components was studied. The results showed that there was no significant difference in the size deviation of the same-type component produced by different enterprises, and the range of geometric parameter uncertainty random variables was small, which was between 0.99 and 1.02. Also, the fluctuation was small, the coefficient of variation was below 0.0093, and the variability of component size deviation was small. The transverse dimension of the component shows a positive deviation, the vertical dimension of component shows a negative deviation, and the dimension deviation of prefabricated concrete components follows the normal distribution.

scholarly journals Gamma variance model: Fractional Fourier Transform (FRFT)

2021 ◽  
Vol 2090 (1) ◽  
pp. 012094
Author(s):  
A H Nzokem
Keyword(s):  
Fourier Transform ◽  
Probability Density ◽  
Goodness Of Fit ◽  
Fractional Fourier Transform ◽  
Infinite Divisibility ◽  
Probability Density Functions ◽  
Cumulative Distribution ◽  
Variance Model ◽  
Sample Data ◽  
Kolmogorov Smirnov

Abstract The paper examines the Fractional Fourier Transform (FRFT) based technique as a tool for obtaining the probability density function and its derivatives; and mainly for fitting stochastic model with the fundamental probabilistic relationships of infinite divisibility. The probability density functions are computed, and the distributional proprieties are reviewed for Variance-Gamma (VG) model. The VG model has been increasingly used as an alternative to the Classical Lognormal Model (CLM) in modelling asset prices. The VG model was estimated by the FRFT. The data comes from the SPY ETF historical prices. The Kolmogorov-Smirnov (KS) goodness-of-fit shows that the VG model fits better the cumulative distribution of the sample data than the CLM. The best VG model comes from the FRFT estimation.

LABORATORY TESTS OF THYROID FUNCTION IN HYPERTHYROIDISM II.

1969 ◽  
Vol 62 (4_Suppla) ◽  
pp. S23-S35
Author(s):  
B.-A. Lamberg ◽  
O. P. Heinonen ◽  
K. Liewendahl ◽  
G. Kvist ◽  
M. Viherkoski ◽  
...  
Keyword(s):  
Thyroid Function ◽  
Laboratory Tests ◽  
Goodness Of Fit ◽  
Point Of View ◽  
Accurate Estimation ◽  
Diagnostic Efficiency ◽  
Normal Limits ◽  
Significant Difference ◽  
Crossing Point ◽  
Hyperthyroid Patients

ABSTRACT The distributions of 13 variables based on 10 laboratory tests measuring thyroid function were studied in euthyroid controls and in patients with toxic diffuse or toxic multinodular goitre. Density functions were fitted to the empirical data and the goodness of fit was evaluated by the use of the χ2-test. In a few instances there was a significant difference but the material available was in some respects too small to allow a very accurate estimation. The normal limits for each variable was defined by the 2.5 and 97.5 percentiles. It appears that in some instances these limits are too rigorous from the practical point of view. It is emphasized that the crossing point of the functions for euthyroid controls and hyperthyroid patients may be a better limit to use. In a preliminary analysis of the diagnostic efficiency the variables of total or free hormone concentration in the blood proved clearily superior to all other variables.

Bootstrap Analysis

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Goodness Of Fit ◽  
Null Distribution ◽  
Real Data ◽  
Confidence Regions ◽  
Confidence Bands ◽  
Cumulative Distribution ◽  
Attractive Alternative ◽  
Bootstrap Analysis ◽  
Parametric Bootstrapping ◽  
Anderson Darling

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

Beyond the heuristic approach to Kolmogorov-Smirnov theorems

1982 ◽  
Vol 19 (A) ◽  
pp. 359-365 ◽  
Author(s):  
David Pollard
Keyword(s):  
Weak Convergence ◽  
Goodness Of Fit ◽  
Empirical Distribution ◽  
Distribution Functions ◽  
Heuristic Approach ◽  
Convergence Theory ◽  
Simple Method ◽  
Starting Point ◽  
Kolmogorov Smirnov ◽  
The Subject

The theory of weak convergence has developed into an extensive and useful, but technical, subject. One of its most important applications is in the study of empirical distribution functions: the explication of the asymptotic behavior of the Kolmogorov goodness-of-fit statistic is one of its greatest successes. In this article a simple method for understanding this aspect of the subject is sketched. The starting point is Doob's heuristic approach to the Kolmogorov-Smirnov theorems, and the rigorous justification of that approach offered by Donsker. The ideas can be carried over to other applications of weak convergence theory.

Investigation of The Nutritional Approaches of Student Athletes During The Covid-19 Epidemic

2021 ◽  
Vol 15 (7) ◽  
pp. 1940-1944
Author(s):  
Sevcan Altun ◽  
Aykut Aksu ◽  
Osman Imamoglu ◽  
Murat Erdogdu ◽  
Kursat Karacabey
Keyword(s):  
Student Athletes ◽  
Eating Habits ◽  
T Test ◽  
Outbreak Period ◽  
Significant Difference ◽  
Package Program ◽  
Kolmogorov Smirnov ◽  
The Subject ◽  
The University ◽  
Smirnov Test

The aim of this study is to investigate the nutritional approaches of student athletes studying at the university during the coronavirus outbreak period. Participants consisted of students studying and doing sports at the University. 446 students, 246 males and 200 females, participated in the study. Besides the personal form, students were filled the questionnaire testing questionnaire. Students voluntarily participated. The surveys were done on social media. Nutritional habits questionnaire consists of 12 questions. In the preparation of the survey questions, the questions proved validity of the researches which have been done on the subject before have been used. SPSS 23.00 package program was used in statistical analyses. Kolmogorov-Smirnov test was performed to test whether the data was normally distributed and it was determined that the data showed normal distribution. Independent t-test, paired t-test, unidirectional variance analysis and LSD tests were used in statistical operations. There was no significant difference in students' nutrition approaches by gender, both in the pre-outbreak period and in the outbreak period points (p> 0.05). Nutrition scores were significantly increased during the outbreak period (p <0.001). A significant difference was found between the students who felt bad before the epidemic and those who felt well before the epidemic and their nutritional scores according to the levels they felt (p <0.05). A significant difference was found between the pre-outbreak period and post-epidemic nutrition scores of the sports faculty students (p <0.05). During the coronavirus epidemic, university student athletes have either increased their nutritional opportunities or have changed their eating habits positively to keep their immune systems strong or both. The fact that sports faculty students have better nutrition compared to other faculty students can be attributed to their taking courses in nutrition, health and similar. It is recommended to give lectures or seminars on nutrition to athlete students. Keywords: Student, Nutrition, Sports Nutrition, Nutritional Approach, Covid-19

scholarly journals Mathematical Models for Tumor Growth and the Reduction of Overtreatment

2018 ◽  
Vol 80 (01) ◽  
pp. 072-078 ◽  
Author(s):  
Berdine Heesterman ◽  
John-Melle Bokhorst ◽  
Lisa de Pont ◽  
Berit Verbist ◽  
Jean-Pierre Bayley ◽  
...  
Keyword(s):  
Tumor Growth ◽  
Mathematical Models ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Age At Onset ◽  
Growth Curves ◽  
Magnetic Resonance Images ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Significant Difference

Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation. Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination (R 2) and root-mean-squared error. The models were compared with Kruskal–Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated. Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R 2: 0.996–1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations (p < 0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model. Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data.

scholarly journals Analysis of Small-Scale Fading Distributions in Vehicle-to-Vehicle Communications

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Vicent M. Rodrigo-Peñarrocha ◽  
Juan Reig ◽  
Lorenzo Rubio ◽  
Herman Fernández ◽  
Susana Loredo
Keyword(s):  
Goodness Of Fit ◽  
Urban Environments ◽  
The Other ◽  
Small Scale ◽  
Channel Measurements ◽  
Experimental Distribution ◽  
Vehicle To Vehicle ◽  
Kolmogorov Smirnov ◽  
Inference Techniques ◽  
The City

This work analyzes the characteristics of the small-scale fading distribution in vehicle-to-vehicle (V2V) channels. The analysis is based on a narrowband channel measurements campaign at 5.9 GHz designed specifically for that purpose. The measurements were carried out in highway and urban environments around the city of Valencia, Spain. The experimental distribution of the small-scale fading is compared to several analytical distributions traditionally used to model the fast fading in wireless communications, such as Rayleigh, Nakagami-m, Weibull, Rice, andα-μdistributions. The parameters of the distributions are derived through statistical inference techniques and their goodness-of-fit is evaluated using the Kolmogorov-Smirnov (K-S) test. Our results show that theα-μdistribution exhibits a better fit compared to the other distributions, making its use interesting to model the small-scale fading in V2V channels.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

2021 ◽  
Author(s):  
Wei-mei Ma ◽  
Jiao Li ◽  
Shuang-gang Chen ◽  
Pei-qiang Cai ◽  
Shen Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Cone Beam ◽  
Imaging Biomarkers ◽  
Training Set ◽  
Test Set ◽  
Ps Ii ◽  
Contrast Enhanced ◽  
Significant Difference

Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Women Entrepreneurs and Determinants of Their Success: An Empirical Study

2020 ◽  
Vol 47 (2) ◽  
pp. 115-129
Author(s):  
Sudarshan Maity ◽  
Tarak Nath Sahu
Keyword(s):  
Women Entrepreneurs ◽  
Goodness Of Fit ◽  
Economic Status ◽  
Family Members ◽  
Easy Access ◽  
Access To Credit ◽  
Significant Difference ◽  
The Family ◽  
Lemeshow Test ◽  
Ease Of Doing Business

Using a Logistic regression model the present study investigates the important factors that influence on ease of doing business by the women entrepreneurs. The respondents are from the four different backward regions in West Bengal, India. The goodness of fit of the model is checked in terms of the Hosmer–Lemeshow test. Moreover, a large share of the women-owned business is a single person enterprise, generates lower revenues, and is smaller in size. The study shows that most women are engaged in small entrepreneurs to help their families financially. Though, women entrepreneurs are facing challenges of financing and non-cooperation from their family members. Even from non-cooperation from the family, they are engaged in entrepreneurship after managing their households’ work. The level of productivity will be enhanced significantly in case easy access to credit and support received from the family members. The support may be in terms of mental as well by helping in households’ work. Further, the application of the Welch’s t-test shows the non-existence of a significant difference in income level among the two groups, who have faced complexities and who have no complexities into running the entrepreneurship. To improve the socio-economic status of the female counterpart of the society, initiatives from the grassroots level are absolutely essential.

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