Impact of cross-section centers estimation on the accuracy of the point cloud spatial expansion using robust M-estimation and Monte Carlo simulation

Dąbrowski, Paweł; Zienkiewicz, Marek Hubert

doi:10.1016/j.measurement.2021.110436

Cited by 8 publications

(1 citation statement)

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“…The capacity of the estimator to endure disruptions is what is meant by the crucial estimation performance index robust [10]. The article suggests a robust M-estimate approach to optimize CLV based on sampling in order to solve this issue by improving the overall state estimation accuracy by correctly changing the robustness [11].…”

Section: Introductionmentioning

confidence: 99%

An boosting business intelligent to customer lifetime value with robust M-estimation

Elveny,

Y. Syah,

M. Nasution

2024

IJ-AI

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When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.

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Section: Introductionmentioning

confidence: 99%