On Jackknife-After-Bootstrap Method for Dependent Data

In this paper, we adapt sufficient and ordered non-overlapping block bootsrap methods into jackknife-after-bootstrap (JaB) algorithm to estimate the standard error of a statistic where observations form a stationary sequence. We also extend the JaB algorithm to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illustrated by an extensive simulation study and they are applied to S&P 500 stock index data. Our findings reveal that the proposed algorithm often exhibits improved performance and, is computationally more efficient compared to conventional JaB method. © 2018, Springer Science+Business Media, LLC, part of Springer Nature

Assessment of individual bioequivalence using sufficient bootstrap procedure

This paper proposes a sufficient bootstrap method, which uses only the unique observations in the resamples, to assess the individual bioequivalence under 2 × 4 randomized crossover design. The finite sample performance of the proposed method is illustrated by extensive Monte Carlo simulations as well as a real-experimental data set, and the results are compared with those obtained by the traditional bootstrap technique. Our records reveal that the proposed method is a good competitor or even better than the classical percentile bootstrap confidence limits. © 2019 John Wiley & Sons, Ltd

Robust change point detection for linear regression models

Linear models incorporating change points are very common in many scientific fields including genetics, medicine, ecology, and finance. Outlying or unusual data points pose another challenge for fitting such models, as outlying data may impact change point detection and estimation. In this paper, we propose a robust approach to estimate the change point/s in a linear regression model in the presence of potential outlying point/s or with non-normal error structure. The statistic that we propose is a partial F statistic based on the weighted likelihood residuals. We examine its asymptotic properties and finite sample properties using both simulated data and in two real data sets. © 2019, International Press of Boston, Inc

Murine genetic models of obesity: type I error rates and the power of commonly used analyses as assessed by plasmode-based simulation

Background/Objectives: Genetic contributors to obesity are frequently studied in murine models. However, the sample sizes of these studies are often small, and the data may violate assumptions of common statistical tests, such as normality of distributions. We examined whether, in these cases, type I error rates and power are affected by the choice of statistical test. Subjects/Methods: We conducted “plasmode”-based simulation using empirical data on body mass (weight) from murine genetic models of obesity. For the type I error simulation, the weight distributions were adjusted to ensure no difference in means between control and mutant groups. For the power simulation, the distributions of the mutant groups were shifted to ensure specific effect sizes. Three to twenty mice were resampled from the empirical distributions to create a plasmode. We then computed type I error rates and power for five common tests on the plasmodes: Student’s t test, Welch’s t test, Wilcoxon rank sum test (aka, Mann–Whitney U test), permutation test, and bootstrap test. Results: We observed type I error inflation for all tests, except the bootstrap test, with small samples (≤5). Type I error inflation decreased as sample size increased (≥8) but remained. The Wilcoxon test should be avoided because of heterogeneity of distributions. For power, a departure from the reference was observed with small samples for all tests. Compared with the other tests, the bootstrap test had less power with small samples. Conclusions: Overall, the bootstrap test is recommended for small samples to avoid type I error inflation, but this benefit comes at the cost of lower power. When sample size is large enough, Welch’s t test is recommended because of high power with minimal type I error inflation. © 2020, The Author(s), under exclusive licence to Springer Nature Limited

Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay. © 2021 Elsevier Lt

prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation–the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)–were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R2=.92), and with all variables as inputs at Station II (R2=.97). All the ML models performed well in predicting evaporation at the investigated locations. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group