A Study of Gjestvang and Singh Randomized Response Model Using Ranked Set Sampling

In this chapter, the authors investigate the performance of the Gjestvang and Singh randomized response model for estimating the mean of a sensitive variable using ranked set sampling along the lines of Bouza. The proposed estimator is found to be unbiased, and a variance expression is derived. Then a simulation study is carried out to judge the magnitude of relative efficiency in various situations. At the end, the proposed model is assessed based on real secondary data applications. A set of SAS codes is also included.

Machine-Learning-Aided Prediction of Flexural Strength and ASR Expansion for Waste Glass Cementitious Composite

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively.

An improved scrambled model for estimating the mean of a sensitive character

This paper suggests a new randomized response model useful for gathering information on quantitative sensitive variable such as drug usage, tax evasion and induced abortions etc. The resultant estimator has been found to more efficient than the estimator of the Saha (2007) under some realistic conditions. We have illustrated results numerically.

Variance Estimation Procedure Using Scrambled Responses and Multi-Auxiliary Variables In Multi-Phase Sampling

Variations in the population can be estimated by variance estimation. In this study, we consider variance estimation procedure using scrambled randomized response for sensitive variable using multi-auxiliary variables in multi-phase sampling. Under Noor-ul-Amin et al. (2018) RRT model, generalized exponential regression type estimator for case-1and case-2 are derived. A simulation study is presented to illustrate the application and computational details. It is observed that proposed model showed better results under both cases.

ANALISA RENCANA INVESTASI PADA PENGEMBANGAN LINTASAN PRODUKSI NEW MODEL CRANK CASE TIPE SPORT DI PT XYZ

XYZ Company is currently planning the production machining line for crank case new model sports type of 2200 units / day. There are two alternative investment plan that is manual and automation to get the best alternative in terms of finance .Analysis tool used is the Net Present Value (NPV), Internal Rate of Return (IRR) and Payback Period (PBP) as the basis for determining which alternative is chosen. Sensitivity analysis is used to determine which is the most sensitive variable to the NPV .Both alternatives feasible to be realized, but the automation system was chosen because of the analysis results is greater than the value of the manual system with an IRR of 57.52% (IRR > MARR), which MARR 7.88% and a NPV of USD 299 002 634 271 (NPV > 0), and payback of 1 years for 0,27 month, which is faster than the economic life of the machine that 8 years. Total interest rate is the most sensitive factor in changing the NPV

Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.