Brazilian Journal of Probability and Statistics (01030752)35(2)pp. 375-391
This work deals with problem of estimating the odds using judgment post stratification (JPS) sampling design. Several estimators of the odds are described and the asymptotic normality of each of them is established. Monte Carlo simulation study is then used to compare different estimators of the odds in the JPS with the standard estimator in simple random sampling (SRS) with replacement for both perfect/imperfect ranking and for both JPS data with/without empty strata. The comparison results indicate that the estimators developed here can be highly more efficient than their SRS counterpart in some certain circumstances. Finally, a real dataset from the third National Health and Nutrition Examination Survey (NHANES III) is employed for illustration purposes. © Brazilian Statistical Association, 2021.
Journal of Modern Applied Statistical Methods (15389472)17(1)pp. 1-27
Most control charts require the assumption of normal distribution for observations. When distribution is not normal, one can use non-parametric control charts such as sign control chart. A deficiency of such control charts could be the loss of information due to replacing an observation with its sign or rank. Furthermore, because the chart statistics of T2 are correlated, the T2 chart is not a desire performance. Non-parametric bootstrap algorithm could help to calculate control chart parameters using the original observations while no assumption regarding the distribution is needed. In this paper, first, a bootstrap multivariate control chart is presented based on Hotelling’s T2 statistic then the performance of the bootstrap multivariate control chart is compared to a Hotelling’s T2 parametric multivariate control chart, a multivariate sign control chart, and a multivariate Wilcoxon control chart using a simulation study. Ultimately, the bootstrap multivariate control chart is used in an empirical example to study the process of sugar production. © 2018 JMASM, Inc.
Applied Stochastic Models in Business and Industry (15264025)33(6)pp. 694-716
Various charts such as |S|, W, and G are used for monitoring process dispersion. Most of these charts are based on the normality assumption, while exact distribution of the control statistic is unknown, and thus limiting distribution of control statistic is employed which is applicable for large sample sizes. In practice, the normality assumption of distribution might be violated, while it is not always possible to collect large sample size. Furthermore, to use control charts in practice, the in-control state usually has to be estimated. Such estimation has a negative effect on the performance of control chart. Non-parametric bootstrap control charts can be considered as an alternative when the distribution is unknown or a collection of large sample size is not possible or the process parameters are estimated from a Phase I data set. In this paper, non-parametric bootstrap multivariate control charts |S|, W, and G are introduced, and their performances are compared against Shewhart-type control charts. The proposed method is based on bootstrapping the data used for estimating the in-control state. Simulation results show satisfactory performance for the bootstrap control charts. Ultimately, the proposed control charts are applied to a real case study. Copyright © 2017 John Wiley & Sons, Ltd.
Computational Statistics and Data Analysis (01679473)55(1)pp. 578-587
Efron (1979) introduced the bootstrap method for independent data but it cannot be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is related to the block size which is difficult to select. In the moving block bootstrap method also the variance estimator is underestimated. In this paper, first the semi-parametric bootstrap is used to estimate the precision measures of estimators in spatial data analysis. In the semi-parametric bootstrap method, we use the estimation of the spatial correlation structure. Then, we compare the semi-parametric bootstrap with a moving block bootstrap for variance estimation of estimators in a simulation study. Finally, we use the semi-parametric bootstrap to analyze the coal-ash data. © 2010 Elsevier B.V. All rights reserved.
Procedia Environmental Sciences (18780296)3pp. 81-86
In the spatial statistics it is often assumed that the data follow a Gaussian random field. Efron introduced bootstrap method for independent data analysis (IIDB) but it can not be applied in spatial data analysis because of dependency of observations. In this paper, an algorithm is given for spatial semi-parametric bootstrap (SSPB) method to estimate the precision measures of plug-in kriging predictor of random field. We also compare IIDB and SSPB methods for analysis of plug-in kriging predictor in a Monte-Carlo simulation study. Finally, we use SSPB method for analysis of finite strain data in geology. © 2010 Published by Elsevier Ltd. Ltd.
Iranian Journal of Environmental Health Science and Engineering (17352746)7(1)pp. 71-80
The purpose of this study was to undertake a spatial analysis of total organic carbon, electrical conductivity and nitrate, in order to produce a pollution dispersion and prediction map for the investigated area in the province of Isfahan in Iran. The groundwater samples were collected from a zone as a pilot study area of 80 km2, including 25 water wells, based on the criteria of vulnerability assessment projects, that is, about one well per 3 km2, during four seasons in 2008-09. In order to make any inferences about the areas that did not have well data, a statistical relationship between explanatory total organic carbon, electrical conductivity and nitrate variables related to well coordination was developed. The probability of the presence of elevated levels of the three compounds in the groundwater was predicted using the best-fit variogram model. According to spatial analysis, the highest R2=0.789 achieved was related to electrical conductivity and followed the exponential model with 0.266 for NO3- (spherical model) and 0.322 for total organic carbon (exponential model) in the spring 2009. This showed the high confidence level for electrical conductivity dataset and forecasted trends. The results of the spatial analysis demonstrated that the transfer trends of electrical conductivity in the groundwater resources followed the route of groundwater movement in all seasons. However, for nitrate and total organic carbon, a definite trend was not obtained and pollution dispersion depended on many parameters.
Computers and Geosciences (00983004)35(3)pp. 626-634
In this paper, the finite strain data (FSD) collected from a Sheeprock thrust sheet in Utah are spatially analyzed. The steps and special manner of spatial analysis of these data are examined due to the importance and influence of data characteristics on spatial prediction accuracy. To do so, the exponential and spherical parametric variogram models with and without nugget effects are fitted by maximum likelihood and restricted maximum likelihood methods on original and normally transformed data. We also fitted other models to classical and robust empirical variograms by ordinary least squares and weighted least squares methods. Next, these models are applied on data and edited data by using robust kriging. Finally, the accuracy of the obtained results are compared with Mukul's model by cross-validation in order to choose the best variogram model for FSD and also specify the importance of attention to characteristics of the data in spatial data analysis. © 2008 Elsevier Ltd. All rights reserved.
