Correlation analysis with additive distortion measurement errors

Zhang, Jun; Chen, Qian; Zhou, Nanguang

doi:10.1080/00949655.2016.1222612

Cited by 25 publications

(10 citation statements)

References 40 publications

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“…= 0. Similar to the proof of theorem 3 in Zhang et al [21] or the proof of theorem 5 in Zhang et al [29], if the conditions in Theorem 2 hold, the statistiĉM ,n ( ) converge to a chi-squared distribution 2 1 with degree of freedom one. Then, we can construct confidence intervals for as I ,M = { * ∶l M,n ( * ) ≤ c }, where c denotes the quantile of 2 1 .…”

Section: Empirical Likelihood Based Confidence Intervalsmentioning

confidence: 78%

“…The exponential identifiability conditions (2.3) are different from the existing mean identifiability conditions E( (U)) = E( (U)) = 0 [5,21,22,27].…”

Section: Exponential Calibrationmentioning

confidence: 94%

“…). We also report the residual-based estimator̂R [21]. It is noted that the residual-based estimator̂D does not need the mean zero condition E[ (U)] = E[ (U)] = 0.…”

Section: Methodsmentioning

confidence: 99%

“…In the past decades, unique and desirable properties of the EL method have been studied by a number of authors. These properties include, but are not limited to, range respecting, transformation-preserving, asymmetric confidence intervals, See, for example, Cui et al [3]; Kiwitt and Neumeyer [8]; Li et al [9]; Lian [10]; Yang et al [20]; Zhang et al [21]; Zhu et al [28]. In the following, we make statistical inference based on the EL principle.…”

Section: Empirical Likelihood Based Confidence Intervalsmentioning

confidence: 99%

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Exponential calibration for correlation coefficient with additive distortion measurement errors

Zhang

2021

Statistical Analysis

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This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in an additive fashion by an observed confounding variable. We propose a new identifiability condition by using the exponential calibration to obtain calibrated variables and propose a direct-plug-in estimator for the correlation coefficient. We show that the direct-plug-in estimator is asymptotically efficient. Next, we suggest an asymptotic normal approximation and an empirical likelihood-based statistic to construct the confidence intervals. Last, we propose several test statistics for testing whether the true correlation coefficient is zero or not. The asymptotic properties of the proposed test statistics are examined. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test statistics. These methods are applied to analyze a temperature forecast data set for an illustration.

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Section: Empirical Likelihood Based Confidence Intervalsmentioning

confidence: 78%

“…The exponential identifiability conditions (2.3) are different from the existing mean identifiability conditions E( (U)) = E( (U)) = 0 [5,21,22,27].…”

Section: Exponential Calibrationmentioning

confidence: 94%

“…). We also report the residual-based estimator̂R [21]. It is noted that the residual-based estimator̂D does not need the mean zero condition E[ (U)] = E[ (U)] = 0.…”

Section: Methodsmentioning

confidence: 99%

Section: Empirical Likelihood Based Confidence Intervalsmentioning

confidence: 99%

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Exponential calibration for correlation coefficient with additive distortion measurement errors

Zhang

2021

Statistical Analysis

Self Cite

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“…There are also a large number of valuable research results, which a large number of researchers have done semantic segmentation and realistic applications based on deep learning. Besides, the deep learning techniques have been applied to various fields of image processing such as other works, making a great breakthrough in traditional image processing.…”

Section: Introductionmentioning

confidence: 99%

Research of animals image semantic segmentation based on deep learning

Liu

et al. 2018

Concurrency and Computation

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It is imperative for us to develop the technology of image semantic segmentation with the increasing demand in the image processing. Nowadays, the development of deep learning is of great significance to the improvement of image segmentation. Furthermore, the paper discussed the relationship between image semantic segmentation and animal image research based on the actual situation, and found that animal image processing technology plays a more important role in the field of protecting precious animals. The end-to-end network training of this paper is consisted of Fully Convolutional Network (FCN) for the front end and Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) for the back end via comparing a variety of research methods. The experiments achieved desired outcome for the semantic segmentation of animal images by utilizing Caffe deep learning framework and explained the implementation details from the aspects of training and testing. KEYWORDS Caffe, CRF-RNN, FCN, image semantic segmentation INTRODUCTIONTraditionally, the segmentation of the image is only directly on the surface layer, such as the appearance features based on texture, color, and shape, and they are separated after aggregating the similar features. Among them, the segmentation method based on regions, because the selection of seed points will lead to low efficiency of the algorithm, the segmented results have a certain flaw and can not reach the expected effect. However, the histogram-based threshold segmentation method ignores certain image space information, and it is very likely to segment a discontinuous and meaningless regions, making the segmentation result less satisfying. Therefore, with the demand for image segmentation technology, the semantic segmentation is indispensable.In semantic segmentation, the method of deep learning can be well escaped from the traditional processing methods, and the limitation of using only the underlying features is eliminated, making process in the level of understanding for image. The deep learning framework of Caffe 1 can handle semantic segmentation well. As the number of treasured animals continues to decline, the application of science and technology to protect them is imminent. In particular, it is necessary to collect a set of quality and distinctive animal image data for a specific animal, and the collection of a large number of data sets depends not only on human resources but also requires the support of the relevant sensors, cameras, and other hardware in the wild places it can automatically detect in the field or other places. One solution is to establish a corresponding index for the existing animal picture database. For the protected animal, it can use image processing technology to quickly identify for these animals, and then semantic segmentation is exploited to make a good recognition of animal images. This will not only increase the efficiency of the staff, but reduce the situation in which Concurrency Computat Pract Exper. 2020;32:e4892. wileyonlinelibrary.com/journal/cp...

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Bayesian jackknife empirical likelihood‐based inference for missing data and causal inference

Chen,

Wang,

Zhao

2024

Can J Statistics

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Missing data reduce the representativeness of the sample and can lead to inference problems. In this article, we apply the Bayesian jackknife empirical likelihood (BJEL) method for inference on data that are missing at random, as well as for causal inference. The semiparametric fractional imputation estimator, propensity score‐weighted estimator, and doubly robust estimator are used for constructing the jackknife pseudo values, which are needed for conducting BJEL‐based inference with missing data. Existing methods, such as normal approximation and JEL, are compared with the BJEL approach in a simulation study. The proposed approach shows better performance in many scenarios in terms of credible intervals. Furthermore, we demonstrate the application of the proposed approach for causal inference problems in a study of risk factors for impaired kidney function.

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Correlation analysis with additive distortion measurement errors

Cited by 25 publications

References 40 publications

Exponential calibration for correlation coefficient with additive distortion measurement errors

Exponential calibration for correlation coefficient with additive distortion measurement errors

Research of animals image semantic segmentation based on deep learning

Bayesian jackknife empirical likelihood‐based inference for missing data and causal inference

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