Model averaging with averaging covariance matrix

Zhao, Shangwei; Zhang, Xinyu; Gao, Yichen

doi:10.1016/j.econlet.2016.06.011

Cited by 15 publications

(5 citation statements)

References 10 publications

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“…Condition (C4) mandates the non-degeneracy of the covariance matrix Ω as n → ∞. Similar assumptions can also be found in [9,10]. Similar to [15], Conditions (C5) and (C6) impose constraints on the bounds of m(•), π(•) and bandwidth, respectively.…”

Section: Methodology and Theoretical Propertymentioning

confidence: 88%

“…In the existing literature on model averaging, most estimates of variance are predominantly derived from the largest candidate model, as exemplified by works such as [6,16]. In contrast, our approach, following [10], leverages information from all candidate models for estimation rather than relying on a single model. Such an estimation method is more robust.…”

Section: Methodology and Theoretical Propertymentioning

confidence: 99%

“…Secondly, Liu and Okui [9] modified the MMA method proposed by Hensen [6] to make it suitable for heteroscedastic scenarios. Furthermore, Zhao et al [10] extended [6]'s work by estimating the covariance matrix based on the weighted average of squared residuals corresponding to all candidate models. This approach improves the model average estimator under heteroskedasticity settings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model Averaging for Accelerated Failure Time Models with Missing Censoring Indicators

Liao,

Liu

2024

Mathematics

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Model averaging has become a crucial statistical methodology, especially in situations where numerous models vie to elucidate a phenomenon. Over the past two decades, there has been substantial advancement in the theory of model averaging. However, a gap remains in the field regarding model averaging in the presence of missing censoring indicators. Therefore, in this paper, we present a new model-averaging method for accelerated failure time models with right censored data when censoring indicators are missing. The model-averaging weights are determined by minimizing the Mallows criterion. Under mild conditions, the calculated weights exhibit asymptotic optimality, leading to the model-averaging estimator achieving the lowest squared error asymptotically. Monte Carlo simulations demonstrate that the method proposed in this paper has lower mean squared errors compared to other model-selection and model-averaging methods. Finally, we conducted an empirical analysis using the real-world Acute Myeloid Leukemia (AML) dataset. The results of the empirical analysis demonstrate that the method proposed in this paper outperforms existing approaches in terms of predictive performance.

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Section: Methodology and Theoretical Propertymentioning

confidence: 88%

Section: Methodology and Theoretical Propertymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Averaging for Accelerated Failure Time Models with Missing Censoring Indicators

Liao,

Liu

2024

Mathematics

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“…The criterion in Equation 15 Therefore, we obtain the model averaging forecast of y T+h followingŷ T+h (ŵ) =ŵ ŷ T+h . Note that the H-MAHAR estimator can be considered an extension to the model averaging with averaging covariance matrix (MAACM) estimator of Zhao et al (2016) under the HAR framework, whereas the original MAACM estimator assumes no dynamic model structures.…”

Section: Model Uncertaintymentioning

confidence: 99%

“…Xie (2015) proposed the prediction model averaging (PMA) method. Zhao et al (2016) extended the PMA method to allow for heteroskedastic error terms (HPMA). Liu and Okui (2013) also proposed a heteroskedasticity-robust Mallows' C p model-averaging method (HRCP).…”

Section: Introductionmentioning

confidence: 99%

Forecast Bitcoin Volatility with Least Squares Model Averaging

Xie

2019

Econometrics

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In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in general outperform many other conventional regression models that ignore specification uncertainty.

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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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Model averaging with averaging covariance matrix

Cited by 15 publications

References 10 publications

Model Averaging for Accelerated Failure Time Models with Missing Censoring Indicators

Model Averaging for Accelerated Failure Time Models with Missing Censoring Indicators

Forecast Bitcoin Volatility with Least Squares Model Averaging

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

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