This article introduces a novel non parametric penalized likelihood hazard estimation when the censoring time is dependent on the failure time for each subject under observation. More specifically, we model this depen...
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This article introduces a novel non parametric penalized likelihood hazard estimation when the censoring time is dependent on the failure time for each subject under observation. More specifically, we model this dependence using a copula, and the method of maximum penalized likelihood (MPL) is adopted to estimate the hazard function. We do not consider covariates in this article. The non negatively constrained MPL hazard estimation is obtained using a multiplicative iterative algorithm. The consistency results and the asymptotic properties of the proposed hazard estimator are derived. The simulation studies show that our MPL estimator under dependent censoring with an assumed copula model provides a better accuracy than the MPL estimator under independent censoring if the sign of dependence is correctly specified in the copula function. The proposed method is applied to a real dataset, with a sensitivity analysis performed over various values of correlation between failure and censoring times.
This paper considers simultaneous estimation of the regression coefficients and baseline hazard in proportional hazard models using the maximum penalized likelihood (MPL) method where a penalty function is used to smo...
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This paper considers simultaneous estimation of the regression coefficients and baseline hazard in proportional hazard models using the maximum penalized likelihood (MPL) method where a penalty function is used to smooth the baseline hazard estimate. Although MPL methods exist to fit proportional hazard models, they suffer from the following deficiencies: (i) the positivity constraint on the baseline hazard estimate is either avoided or poorly treated leading to efficiency loss, (ii) the asymptotic properties of the MPL estimator are lacking, and (iii) simulation studies comparing the performance of MPL to that of the partial likelihood have not been conducted. In this paper we propose a new approach and aim to address these issues. We first model baseline hazard using basis functions, then estimate this approximate baseline hazard and the regression coefficients simultaneously. The penalty function included in the likelihood is quite general but typically assumes prior knowledge about the smoothness of the baseline hazard. A new iterative optimization algorithm, which combines Newton's method and a multiplicative iterative algorithm, is developed and its convergence properties studied. We show that if the smoothing parameter tends to zero sufficiently fast, the new estimator is consistent, asymptotically normal and retains full efficiency under independent censoring. A simulation study reveals that this method can be more efficient than the partial likelihood method, particularly for small to moderate samples. In addition, our simulation shows that the new estimator is substantially less biased under informative censoring. (C) 2014 Elsevier B.V. All rights reserved.
Although the use of blind deconvolution of image restoration is a widely known concept, only few reports have discussed in detail its application to solving problem of restoration of underwater range-gated laser image...
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Although the use of blind deconvolution of image restoration is a widely known concept, only few reports have discussed in detail its application to solving problem of restoration of underwater range-gated laser images. A comparative study of underwater image restoration using the Richardson-Lucy algorithm, the least-squares algorithm, and the multiplicative iterative algorithm for blind deconvolution is presented. All the deconvolution approaches use denoised underwater images and Wells' small angle approximation theory of derived point spread function as the initial object and degradation guess, respectively. Owing the underwater no-reference imaging environment, image quality judgment based on the blur metric method is incorporated in our comparison to determine the appropriate deconvolution iteration number for each algorithm, which objectively evaluates the image restoration results. The performance of the three algorithms applied to underwater image restoration is discussed and reported.
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