Data augmentation based estimation for the censored quantile regression neural network model

Hao, Ruiting; Weng, Chengwei; Liu, Xinyu; Yang, Xiangzhong

doi:10.1016/j.eswa.2022.119097

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…After the table is completely filled, we can proceed to assess the demand for attendance. Since we are evaluating the attendance of each movie, first of all we need to take the logarithm of the percentage of attendance of each movie [16].…”

Section: Research and Resultsmentioning

confidence: 99%

Methods of data analysis in the problem of optimizing the rental schedule

et al. 2023

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In this paper, the main problems of scheduling screenings for a cinema were considered. The activity of one cinema “X” is described and the restrictions that must be observed and taken into account when planning the schedule of sessions are analyzed. The solution to this problem is due to the complexity and time-consuming planning of the cinema schedule. To achieve this goal, the article analyzes the statistical data obtained in the cinema “X”. An analysis was carried out using the methods of statistical data analysis and predictive factors of movie attendance were identified, regression models of attendance were developed. Based on the developed regression models, statistical estimates of attendance predictive factors were obtained. Taking into account the results obtained, an optimization mathematical model for the formation of a rental schedule has been developed, which makes it possible to increase the box office of a cinema.

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Section: Research and Resultsmentioning

confidence: 99%

Methods of data analysis in the problem of optimizing the rental schedule

et al. 2023

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“…Additionally, assuming η 2 1 ∼ IG(e 0 , f 0 ), η 2 2 ∼ IG(g 0 , h 0 ). From Equations ( 8), ( 11) and (12), the posterior distribution of the fixed-effects β and random effects α i can be derived as:…”

Section: Bayesian Double Lasso Penalized Quantile Regression Methods ...mentioning

confidence: 99%

“…In Tobit quantile regression models, although mixed-effects models can comprehensively consider the covariates affecting the response variables [12], they are computationally intensive and may affect the accuracy of parameter estimation [13]. Parameter estimation and variable selection of mixed-effects models with censored data using different penalization methods can effectively make up for the shortcomings of traditional methods [14].…”

Section: Introductionmentioning

confidence: 99%

Research on Quantile Regression Method for Longitudinal Interval-Censored Data Based on Bayesian Double Penalty

Zhao,

Shu,

et al. 2024

Mathematics

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The increasing prominence of the problem of censored data in various fields has made studying how to perform parameter estimation and variable selection in censored mixed-effects models one of the hotspots of current research. In this paper, considering the situation that the response variable is restricted by the bilateral limit, a double-penalty Bayesian Tobit quantile regression model was constructed to carry out parameter estimation and variable selection in the interval-censored mixed-effects model, and at the same time, the fixed-effects and random effects coefficients are compressed in Tobit’s mixed-effects model, so as to reduce the estimation error of the model at the same time as the variable selection of the model is carried out. The posterior distribution of each unknown parameter was derived using the conditional Laplace prior and the mixed truncated normal distribution of interval-censored data, and then the Gibbs sampling algorithm for unknown parameter estimation was constructed. Through Monte Carlo simulation, it was found that the new method is more advantageous than the classical method in terms of variable selection and parameter estimation accuracy in various situations, such as different model sparsity, different data censoring ratios and different random error distributions, and the model is able to realize automatic variable selection. Finally, the new method is used to analyze the correlation between the crime rate and various economic indicators in China.

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Weighted Expectile Regression Neural Networks for Right Censored Data

Zhang,

Chen,

Liu

et al. 2024

Statistics in Medicine

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As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression (WER) method assumes that the censoring variable and covariates are independent, and that the covariates effects has a global linear structure. However, these two assumptions are too restrictive to capture the complex and nonlinear pattern of the underlying covariates effects. In this article, we developed a novel weighted expectile regression neural networks (WERNN) method by incorporating the deep neural network structure into the censored expectile regression framework. To handle the random censoring, we employ the inverse probability of censoring weighting (IPCW) technique in the expectile loss function. The proposed WERNN method is flexible enough to fit nonlinear patterns and therefore achieves more accurate prediction performance than the existing WER method for right censored data. Our findings are supported by extensive Monte Carlo simulation studies and a real data application.

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Data augmentation based estimation for the censored quantile regression neural network model

Cited by 6 publications

References 34 publications

Methods of data analysis in the problem of optimizing the rental schedule

Methods of data analysis in the problem of optimizing the rental schedule

Research on Quantile Regression Method for Longitudinal Interval-Censored Data Based on Bayesian Double Penalty

Weighted Expectile Regression Neural Networks for Right Censored Data

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