MALMEM: Model Averaging in Linear Measurement Error Models

Zhang, Xinyu; Carroll, Raymond J.

doi:10.1111/rssb.12317

Cited by 13 publications

(3 citation statements)

References 42 publications

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“…To further improve the predictive power of the transformation methods, we treat DET, DIT, and PCT as three weak learners and follow the well‐known ensemble learning approach, model averaging (Hansen, 2007; Zhang et al ., 2019), to obtain enhanced predictions. As shown in Figure 2, ensemble learning uses the base models' outputs as the input features and the predicted variable remains the same as in the original data set.…”

Section: Predictive Analytics: Nonstationary Transformation Methodsmentioning

confidence: 99%

“…Moreover, the validation analysis shows that the three methods also provide satisfying predictive performance when compared with the benchmark approaches. To further improve the fitting effect of the transformed methods, we treat DET, DIT, and PCT as three weak learners and follow the well‐known ensemble learning approach, model averaging (Hansen, 2007; Zhang et al ., 2019), to obtain enhanced predictions for the nonstationary demand. Specifically, the model averaging approach assigns a positive weight to every transformation method (the sum of three weights is 1) and outputs the weighted sum of their predictions as the enhanced predictions.…”

Section: Introductionmentioning

confidence: 99%

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Analytics for multiperiod risk‐averse newsvendor under nonstationary demands

Shi

2021

Decision Sciences

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Based on the practice of a Chinese food manufacturer, this article develops a predictive and prescriptive analytics research on a multiperiod risk‐averse newsvendor problem with nonstationary demands. We start with a predictive analysis that aims to transform the nonstationary demand series into a predictable stationary one, and then develop inventory models for deriving prescriptive decisions based on transformed stationary series. When transforming the nonstationary demand series, we consider three commonly used methods—detrending (DET), differencing (DIT), and percentage change transformations (PCT)—which all effectively convert nonstationary demand series into stationary ones. These methods not only have desired simplicity and interpretability but also provide better predictive performance than the autoregressive integrated moving (ARIMA) process. Moreover, we develop an ensemble of the three transformation methods following the model averaging approach, which provides comparable predictions as the machine learning approaches. When developing prescriptive inventory decisions, we construct dynamic risk‐averse newsvendor models for the three methods having different structures, and find that the optimal order quantities under DET and DIT monotonically change as the newsvendor becomes more risk‐averse, but the optimal order quantity under PCT may not. Similarly, we also develop a heuristic ensemble of the inventory decisions under the three methods, which can lead to better profit performance. An extensive numerical simulation based on the manufacturer's historical data set shows that the heuristic ensemble inventory decision outperforms the sole decision generated by every transformation method and achieves an average performance improvement up to 92.25%. Several extensions are also considered to confirm the robustness of our findings.

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Section: Predictive Analytics: Nonstationary Transformation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytics for multiperiod risk‐averse newsvendor under nonstationary demands

Shi

2021

Decision Sciences

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“…It can be considered as an extreme of imputation in the missing value problem, where all cases are missing for some predictors (see also Boone et al ., 2011). The measurement error model also has a similar structure, in that the true value is unobserved (see also Doppelhofer et al ., 2016; Zhang et al ., 2019).…”

Section: Economic Variable Selectionmentioning

confidence: 99%

Economic variable selection

Miyawaki

MacEachern

2021

Can J Statistics

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Regression plays a key role in many research areas and its variable selection is a classic and major problem. This study emphasizes cost of predictors to be purchased for future use, when we select a subset of them. Its economic aspect is naturally formalized by the decision-theoretic approach. In addition, two Bayesian approaches are proposed to address uncertainty about model parameters and models: the restricted and extended approaches, which lead us to rethink about model averaging. From objective, rule-based, or robust Bayes point of view, the former is preferred. Proposed method is applied to three popular datasets for illustration.

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Optimal model averaging for partially linear models with missing response variables and error‐prone covariates

Liang,

Wang,

Cai

2024

Statistics in Medicine

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We consider the problem of optimal model averaging for partially linear models when the responses are missing at random and some covariates are measured with error. A novel weight choice criterion based on the Mallows‐type criterion is proposed for the weight vector to be used in the model averaging. The resulting model averaging estimator for the partially linear models is shown to be asymptotically optimal under some regularity conditions in terms of achieving the smallest possible squared loss. In addition, the existence of a local minimizing weight vector and its convergence rate to the risk‐based optimal weight vector are established. Simulation studies suggest that the proposed model averaging method generally outperforms existing methods. As an illustration, the proposed method is applied to analyze an HIV‐CD4 dataset.

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MALMEM: Model Averaging in Linear Measurement Error Models

Cited by 13 publications

References 42 publications

Analytics for multiperiod risk‐averse newsvendor under nonstationary demands

Analytics for multiperiod risk‐averse newsvendor under nonstationary demands

Economic variable selection

Optimal model averaging for partially linear models with missing response variables and error‐prone covariates

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