SIMEX estimation for single-index model with covariate measurement error

Yang, Yiping; Tong, Tiejun; Li, Gaorong

doi:10.1007/s10182-018-0327-6

Cited by 56 publications

(16 citation statements)

References 48 publications

(61 reference statements)

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“…Although RF and GBDT also output feature importance, the calculation are too rough for further causal interpretation. Advanced statistical methods such as single index model that combines flexibility of modeling with interpretability of (linear) coefficients [91] , [92] may provide a potential solution for balancing the interpretability and performance. Meanwhile, host heterogeneity on age, gender, diet, life style and other factors [93] , as well as the sparsity, variance, and high-dimensionality [94] of microbiome data can also confound the disease detection and interpretation, which should be evaluated and considered in experiment design and ML analysis.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Towards multi-label classification: Next step of machine learning for microbiome research

Chen

et al. 2021

Computational and Structural Biotechnology Journal

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Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Towards multi-label classification: Next step of machine learning for microbiome research

Chen

et al. 2021

Computational and Structural Biotechnology Journal

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“…Step 1: Compute the estimated projection direction̂[ ] by fitting a single-index model with "synthesis" data Cui, Härdle, and Zhu (2011);Li, Lai, and Lian (2015); Li, Peng, Dong, and Tong (2014); Lian, Liang, and Carroll (2015); Liang, Liu, Li, and Tsai (2010); Peng and Huang (2011);Yang, Tong, and Li (2019); Zhang, Feng, and Xu (2015); Zhang, He, Lu, and Wen (2018).…”

Section: (B) Under the Null Hypothesismentioning

confidence: 99%

Model checking for multiplicative linear regression models with mixed estimators

Zhang

2021

Statistica Neerlandica

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In this paper, we introduce the mixed estimators based on product least relative error estimation and least squares estimation in a multiplicative linear regression model. The asymptotic properties for the mixed estimators are established. We present some explicit expressions of the optimal estimator of the mixed estimators, and we also suggest some numerical solutions in the simulation studies and real data analysis. Studying model checking problems for multiplicative linear regression models, we propose four test statistics. One is the score-type test statistic, the second one is the residual-based empirical process test statistic marked by proper functions of the covariates. The third one is the integrated conditional moment test statistic by using linear projection weighting function, and the fourth one is the adaptive model test statistic. These test statistics are all related to the mixed estimators.The asymptotic properties of these test statistics are established, and some bootstrap procedures for calculating the critical values are also proposed. Simulation studies are conducted to demonstrate the performance of the proposed estimation procedures, and a real example is analyzed to illustrate its practical usage.

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“…Proof: From Lemma 2, and using the law of total probability, we obtain (48), as shown at the top of this page.…”

Section: Appendix B Proof Of Theoremmentioning

confidence: 99%

“…Carroll et al [47] further proved the superior performance of SIMEX method in nonlinear models. Yang et al [48] considered the single-index measurement errors model with mismeasured covariates in the nonparametric part, and adopted a SIMEX method based on local linear smoother and the estimating equation to solve it. Similarly, many scholars have conducted extensive and in-depth research on measurement errors in performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Step-Stress Accelerated Degradation Modeling and Remaining Useful Life Estimation Considering Multiple Sources of Variability

Pang

et al. 2019

IEEE Access

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In the absence of sufficient degradation data of long-lifetime and highly reliable products, a step-stress accelerated degradation model based on nonlinear diffusion process is proposed to estimate the remaining useful life (RUL), with the advantage of requiring small sample size and short test time. For multiple uncertainties caused by inherent properties, individual differences, measurement equipment performance and artificial deviations in the degradation process, this model considers the temporal variability, unit-to-unit variability and measurement errors in both performance degradation and covariates. To estimate the RUL, we derive an analytical approximation to the first hitting time (FHT) of the nonlinear diffusion process crossing a predetermined threshold with the consideration of measurement errors. Based on maximum likelihood estimation (MLE), a modified simulation and extrapolation (SIMEX) method, called MLE-SIMEX, is developed for estimating unknown parameters in the established model. The usefulness of the proposed model is demonstrated by a simulation case and a real-world example. The results reveal that considering nonlinearity and multiple sources of variability in the step-stress accelerated degradation process can improve the model fitting and the accuracy of RUL estimation.

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SIMEX estimation for single-index model with covariate measurement error

Cited by 56 publications

References 48 publications

Towards multi-label classification: Next step of machine learning for microbiome research

Towards multi-label classification: Next step of machine learning for microbiome research

Model checking for multiplicative linear regression models with mixed estimators

Nonlinear Step-Stress Accelerated Degradation Modeling and Remaining Useful Life Estimation Considering Multiple Sources of Variability

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