scholarly journals Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling

2004 ◽  
Vol 32 (6) ◽  
pp. 2616-2641 ◽  
Author(s):  
Hidetoshi Shimodaira
Keyword(s):  
Bootstrap Resampling ◽  
Approximately Unbiased

Optimization of the Ratio of ω-6 Linoleic and ω-3 α-Linolenic Fatty Acids of Hemp Seed Oil with Jackknife and Bootstrap Resampling

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Vibha Devi ◽  
Shabina Khanam
Keyword(s):  
Fatty Acids ◽  
Standard Deviation ◽  
Flow Rate ◽  
Seed Oil ◽  
Cannabis Sativa ◽  
Bootstrap Resampling ◽  
Hemp Seed ◽  
Solvent Flow Rate ◽  
The Mean ◽  
Experimental Runs

Abstract In the present work, supercritical fluid extraction (SFE) of hemp (Cannabis sativa) seed oil at various ranges of SFE parameters is performed. These parameters and respective ranges are temperature (40–80) °C, pressure (200–350) bar, solvent (CO2) flow rate (5–15) g/min, particle size (0.43–1.02) mm and amount of co-solvent (ethanol) (0–10) % of solvent flow rate. Central composite design (CCD) suggests 32 experimental runs to perform through SFE. The obtained oil is analysed through gas chromatography to identify its fatty acids concentrations. The ratio of ω-6 linoleic and ω-3 α-linolenic fatty acids (ω-6/ω-3) is optimized through CCD to obtain the desired amount of 3:1 as this ratio is highly preferred for various health benefits. Ratio of ω-6/ω-3 is obtained in the range from 2.11 to 3.06:1 for all experimental runs. The effect of SFE parameters on this ratio is investigated. Further, cross-validation is peformed on the experimental data obtained for the concentrations of both fatty acids by jackknife and bootstrap resampling to authenticate the obtained data. Small value of standard deviation (~1), less standard error of the mean (SEM) (<0.8) and less variance coefficient (<0.11) confirms the validity of the obtained data. All the estimators’ values such as standard deviation, variance coefficients and SEM are observed in 95 % of confidence intervals.

scholarly journals A Novel Nomogram for Individualized Gonadotropin Starting Dose in GnRH Antagonist Protocol

2021 ◽  
Vol 12 ◽  
Author(s):  
Yubin Li ◽  
Yuwei Duan ◽  
Xi Yuan ◽  
Bing Cai ◽  
Yanwen Xu ◽  
...  
Keyword(s):  
Regression Model ◽  
Ovarian Stimulation ◽  
Multivariate Regression ◽  
Gnrh Antagonist ◽  
Bootstrap Resampling ◽  
Multivariate Regression Model ◽  
Starting Dose ◽  
Gnrh Antagonist Protocol ◽  
Antagonist Protocol

Controlled ovarian stimulation (COS) is one of the most vital parts of in vitro fertilization-embryo transfer (IVF-ET). At present, no matter what kinds of COS protocols are used, clinicians have to face the challenge of selection of gonadotropin starting dose. Although several nomograms have been developed to calculate the appropriate gonadotropin starting dose in gonadotropin releasing hormone (GnRH) agonist protocol, no nomogram was suitable for GnRH antagonist protocol. This study aimed to develop a predictive nomogram for individualized gonadotropin starting dose in GnRH antagonist protocol. Single-center prospective cohort study was conducted, with 198 women aged 20-45 years underwent IVF/intracytoplasmic sperm injection (ICSI)-ET cycles. Blood samples were collected on the second day of the menstrual cycle. All women received ovarian stimulation using GnRH antagonist protocol. Univariate and multivariate analysis were performed to identify predictive factors of ovarian sensitivity (OS). A nomogram for gonadotropin starting dose was developed based on the multivariate regression model. Validation was performed using concordance statistics and bootstrap resampling. A multivariate regression model based on serum anti-Müllerian hormone (AMH) level, antral follicle count (AFC), and body mass index (BMI) was developed and accounted for 59% of the variability of OS. An easy-to-use predictive nomogram for gonadotropin starting dose was established with excellent accuracy. The concordance index (C-index) of the nomogram was 0.833 (95% CI, 0.829-0.837). Internal validation using bootstrap resampling further showed the good performance of the nomogram. In conclusion, gonadotropin starting dose in antagonist protocol can be predicted precisely by a novel nomogram.

Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model

2020 ◽  
Vol 11 (4) ◽  
pp. 579-589
Author(s):  
Muhamad Husnain Mohd Noh ◽  
Mohd Akramin Mohd Romlay ◽  
Chuan Zun Liang ◽  
Mohd Shamil Shaari ◽  
Akiyuki Takahashi
Keyword(s):  
Stress Intensity Factor ◽  
Finite Element ◽  
Finite Element Model ◽  
Simulation Analysis ◽  
Element Model ◽  
Bootstrap Resampling ◽  
Confidence Bounds ◽  
Content Type ◽  
Resampling Method ◽  
The Mean

PurposeFailure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis.Design/methodology/approachThe bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model.FindingsThe mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error.Originality/valueThe bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

BOOTSTRAP RESAMPLING FOR EXTREME WAVE HEIGHTS: IMPLICATIONS FOR COASTAL RISKS

2007 ◽  
Author(s):  
Dominic Reeve ◽  
Ying Li ◽  
Agustin Sanchez-Arcilla ◽  
Jesús Gómez
Keyword(s):  
Bootstrap Resampling ◽  
Extreme Wave ◽  
Coastal Risks ◽  
Wave Heights

Unbiased Estimation of Total Rural Loans Advanced and Incurred in an Indian State Along with Unbiased Estimation of Variance in Estimation

2017 ◽  
Vol 69 (1) ◽  
pp. 71-75
Author(s):  
Arijit Chaudhuri
Keyword(s):  
Sampling Technique ◽  
Unbiased Estimation ◽  
Approximately Unbiased ◽  
Rural Farmers ◽  
Statistical Institute ◽  
Indian State ◽  
Unbiased Estimates ◽  
Indian Statistical ◽  
Year 2000 ◽  
Estimation Of Variance

Around the year 2000, the problem of reconciling the estimate of loans advanced by the banks and the estimate of loans incurred by the rural farmers was studied in the Indian Statistical Institute. Some approximately unbiased estimates were examined along with approximately unbiased estimates of their approximate variances. Utilizing “Constrained Network” sampling technique exactly unbiased counterparts are presented as alternatives.

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