Empirical likelihood for generalized linear models with missing responses

Xue, Dong; Xue, Liugen; Cheng, Weihu

doi:10.1016/j.jspi.2011.01.005

Cited by 10 publications

(11 citation statements)

References 17 publications

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“…For model (1) with missing at random responses, Xue et al (2011) constructed an empirical loglikelihood ratio for θ 0 relying on the following observation: Xue et al (2011) shares a very good property of having a chi-squared distribution asymptotically. However, since their ELR concerns the nonparametric regression estimation of Δ(•), the well-known "curse of dimensionality" problem probably hinders its applications when the dimension of the covariates is high.…”

Section: Methodology and Main Resultsmentioning

confidence: 99%

“…Similar to Xue et al (2011), we resolve this problem by simply replacing these unknowns with their estimators, respectively.…”

Section: Methodology and Main Resultsmentioning

confidence: 99%

“…Then, similar to Xue et al (2011), one can obtain an approximate 1 − α confidence interval for θ 0 as…”

Section: Methodology and Main Resultsmentioning

confidence: 99%

“…Unfortunately, the CCA method of Wang and Rao (2002b) has to pay a price of losing information as mentioned in the earlier paragraph, whereas their imputation method fails to produce an ELR with a standard chi-squared distribution asymptotically; see also Wang and Dai (2008). As an improvement, Xue et al (2011) developed a new imputation method for the response mean of generalized linear models based on the inverse selection probability weighted technique (Horvitz and Thompson, 1952). Nevertheless, their method may suffer the so-called "curse of dimensionality" problem.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…To resolve this problem, an additional "Working model" has to be employed when the dimension of covariates is higher. This probably places considerable computational burdens, and in turn hinders somewhat the prevalence of the method of 2 Xue et al (2011). This motivates us to further consider the ELR proposed in the sequel.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Empirical likelihood for the response mean of generalized linear models with missing at random responses

Liu

2014

Communications in Statistics - Simulation and Computation

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In this paper, we present a new empirical likelihood ratio for constructing the confidence interval of the response mean of generalized linear models with missing at random responses. Compared with the existing methods, the proposal can avoid the so-called "curse of dimensionality" problem when the dimension of covariates is high, and is still chi-squared distributed asymptotically, nevertheless. Simulation studies are also provided to illustrate the performance of the developed method.

show abstract

Section: Methodology and Main Resultsmentioning

confidence: 99%

“…Similar to Xue et al (2011), we resolve this problem by simply replacing these unknowns with their estimators, respectively.…”

Section: Methodology and Main Resultsmentioning

confidence: 99%

“…Then, similar to Xue et al (2011), one can obtain an approximate 1 − α confidence interval for θ 0 as…”

Section: Methodology and Main Resultsmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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Empirical likelihood for the response mean of generalized linear models with missing at random responses

Liu

2014

Communications in Statistics - Simulation and Computation

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P‐2.9: A review of image generation methods based on deep learning

Wang

Song

Wang

2023

Symp Digest of Tech Papers

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Image as a medium of visual information transmission have the advantages of vividness, intuition and easy understanding. They play an important role in information transmission and utilization. In recent years, due to the rapid development of deep learning technology in the field of image processing, image generative model based on neural network has become one of the current research hotspots. In the field of deep learning, unsupervised learning model has received more and more attentions, especially in the field of deep generative models, which has made breakthrough progress [1] . Among them, Variational Auto-Encoder (VAE), Generative Adversarial Network (GAN), and Diffusion Model are the three most representative research methods in the field of unsupervised learning. They have been applied more and more in the field of deep generative models. Especially, the high-quality image generative models based on the generative adversarial network continue to be hot. The diffusion model is a rising star, which is favored by more and more researchers. This paper first summarizes the main research work, improvement mechanism and features of image generation methods based on VAE and GAN, then introduces the principle of the rising diffusion model and its representative models. Finally, the advantages and limitations of the above methods are compared and analyzed, and prospects for future research are put forward.

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Highway Abandoned Object Detection Based on Foreground Extraction

Wang,

Zhai

2023

Lecture Notes in Electrical Engineering

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Empirical likelihood for generalized linear models with missing responses

Cited by 10 publications

References 17 publications

Empirical likelihood for the response mean of generalized linear models with missing at random responses

Empirical likelihood for the response mean of generalized linear models with missing at random responses

P‐2.9: A review of image generation methods based on deep learning

Highway Abandoned Object Detection Based on Foreground Extraction

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