New calibration estimation procedure in the presence of unit non response

Jaiswal, AK; Usman, M.; Singh, G. N.

doi:10.1016/j.asej.2022.101910

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Halim et al [20] derived the transition matrices of the conditional misclassification probabilities of multiple above mentioned models and also worked on finding the association of variables while taking RR into account. Jaiswal et al [21] projected the calibrated estimator of population mean under a unit response condition using inverse linear, logistic, and exponential integrated models.…”

Section: Survey Sampling and Randomized Responsementioning

confidence: 99%

Estimation of Hidden Logits Using Several Randomized Response Techniques

Halim,

Arshad,

Alomair

et al. 2023

Symmetry

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In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.

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Section: Survey Sampling and Randomized Responsementioning

confidence: 99%

Estimation of Hidden Logits Using Several Randomized Response Techniques

Halim,

Arshad,

Alomair

et al. 2023

Symmetry

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“…Wei et al first established an RFLP metric system for predicting MOOC user learning behavior and attrition by improving the RFM model in the business domain; secondly, histogram and chi-square tests were used to determine the characteristic variables affecting MOOC user attrition; finally, a MOOC user attrition prediction model was constructed by combining the Grouped Data Processing (GMDH) network as a post-processing information system [17]. Smaili et al modified the model by adding diversity "D" as the fourth parameter, referring to the diversity of products purchased by a given customer, and the RFM-D based model was applied to the retail market to detect customer behavior patterns and the proposed model improved the quality of customer behavior prediction [18].…”

Section: Related Workmentioning

confidence: 99%

Research on Customer Retention Prediction Model of VOD Platform Based on Machine Learning

Zhao¹,

Zhao²,

Yang³

et al. 2023

IJACSA

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Advanced wireless technology and smart mobile devices allow users to watch Internet video from almost anywhere. The major VOD platforms are competing with each other for customers, slowly shifting from a "product-centric" strategic goal to a "customer-centric" one. At present, existing research is limited to platform business model and development strategy as well as user behavior research, but there is less research on customer retention prediction. In order to effectively solve the customer retention prediction problem, this study applies machine learning methods to video-on-demand platform customer retention prediction, improves the traditional RFM model to establish the RFLH theoretical model for video-ondemand platform customer retention prediction, and uses machine learning methods to predict the number of customer retention days. The Optuna algorithm is used to determine the model hyperparameters, and the SHAP framework is integrated to analyze the important factors affecting customer retention. The experimental results show that the comprehensive performance of the LightGBM model is better than other models. The total number of user logins in the past week, the length of video playback in the same day, and the time difference between the last login and the present are important features that affect customer retention prediction. This study can help companies develop effective customer management strategies to maximize potential customer acquisition and existing customer retention for maximum market advantage.

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“…The problem of nonresponse was dealt with in mailed questionnaires (2) . The calibrated estimator of population mean was suggested in the presence of unit non-response with the help of non-response adjustment factor on using different models (inverse linear, exponential, logistic) on using auxiliary variables (3) .…”

Section: Introductionmentioning

confidence: 99%

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

Garg,

Patel,

Pachori

2024

IJST

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Objectives: In this article, we addressed the problem of estimation of the finite population proportion under the Probability Proportional to Size (PPS) sampling technique, when the complete information is unavailable due to the presence of non-response. We developed calibrated estimators of the population proportion under PPS sampling in the presence of nonresponse based on the availability of auxiliary information. Methods: The expressions for the mean squared errors of the suggested estimators were developed to the first order of approximation. The developed estimators of the population proportion are compared with the design-based Horvitz-Thompson estimator and Horvitz-Thompson type calibration estimator which were obtained on the complete response units along with the design-based Hansen and Hurwitz type estimator in the presence of non-response. A Simulation study has also been conducted to support the performance of the developed estimators of population proportion with the help of two real datasets, by computing Percentage Absolute Relative Bias (%ARB) and Percentage Relative Root Mean Squared Error (%RRMSE) using R software. Findings: The simulation study supported the performance of the developed estimators of the finite population proportion based on %ARB and %RRMSE. The proposed calibration estimators of population proportion are more efficient than the other considered estimators in the presence of non-response. Novelty: The proposed new calibrated estimators have practical implications in the estimation of finite population proportions. Keywords: Auxiliary information, Calibration Approach, Nonresponse, Population proportion, PPS sampling

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New calibration estimation procedure in the presence of unit non response

Cited by 5 publications

References 17 publications

Estimation of Hidden Logits Using Several Randomized Response Techniques

Estimation of Hidden Logits Using Several Randomized Response Techniques

Research on Customer Retention Prediction Model of VOD Platform Based on Machine Learning

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

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