The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with cens...
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The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with censored outcomes and/or studies which institute enrollment criterion leading to left truncation. Censored outcomes arise when the event of interest is not observed but rather is known relevant to an observation time(s). Left truncated data occur in studies that exclude participants who have experienced the event prior to being enrolled in the study. If not accounted for, both of these features can lead to inaccurate inferences about the population under study. Thus, to overcome this challenge, herein we propose a novel unified PH model that can be used to accommodate both of these features. In particular, our approach can seamlessly analyze exactly observed failure times along with interval-censored observations, while aptly accounting for left truncation. To facilitate model fitting, an expectation-maximization algorithm is developed through the introduction of carefully structured latent random variables. To provide modeling flexibility, a monotone spline representation is used to approximate the cumulative baseline hazard function. The performance of our methodology is evaluated through a simulation study and is further illustrated through the analysis of two motivating data sets;one that involves child mortality in Nigeria and the other prostate cancer.
Data augmentation improves the convergence of iterative algo-rithms, such as the em algorithm and Gibbs sampler by introducing care-fully designed latent variables. In this article, we first propose a data aug-mentati...
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Data augmentation improves the convergence of iterative algo-rithms, such as the em algorithm and Gibbs sampler by introducing care-fully designed latent variables. In this article, we first propose a data aug-mentation scheme for the first-order autoregression plus noise model, where optimal values of working parameters introduced for recentering and rescal-ing of the latent states, can be derived analytically by minimizing the fraction of missing information in the em algorithm. The proposed data augmenta-tion scheme is then utilized to design efficient Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of some non-Gaussian and non-linear state space models, via a mixture of normals approximation coupled with a block-specific reparametrization strategy. Applications on simulated and benchmark real data sets indicate that the proposed MCMC sampler can yield improvements in simulation efficiency compared with centering, non -centering and even the ancillarity-sufficiency interweaving strategy.
Directional statistics deals with data that can be naturally expressed in the form of vector directions. The von Mises-Fisher distribution is one of the most fundamental parametric models to describe directional data....
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Directional statistics deals with data that can be naturally expressed in the form of vector directions. The von Mises-Fisher distribution is one of the most fundamental parametric models to describe directional data. Mixtures of von Mises-Fisher distributions represent a popular approach to handling heterogeneous populations. However, components of such models can be affected by the presence of mild outliers or cluster tails heavier than what can be accommodated by means of a von Mises-Fisher distribution. To relax these model limitations, a mixture of contaminated von Mises-Fisher distributions is proposed. The performance of the proposed methodology is tested on synthetic data and applied to text and genetics data. The obtained results demonstrate the importance of the proposed procedure and its superiority over the traditional mixture of von Mises-Fisher distributions in the presence of heavy tails.
Let a progressively type-II (PT-II) censored sample of size m is available. Under this set-up, we consider the problem of estimating unknown model parameters and two reliability characteristics of the log-logistic dis...
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Kvam and Samaniego (J Am Stat Assoc 89: 526-537, 1994) derived an estimator that they billed as the nonparametric maximum likelihood estimator (MLE) of the distribution function based on a ranked-set sample. However, ...
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Kvam and Samaniego (J Am Stat Assoc 89: 526-537, 1994) derived an estimator that they billed as the nonparametric maximum likelihood estimator (MLE) of the distribution function based on a ranked-set sample. However, we show here that the likelihood used by Kvam and Samaniego (1994) is different from the probability of seeing the observed sample under perfect rankings. By appealing to results on order statistics from a discrete distribution, we write down a likelihood that matches the probability of seeing the observed sample. We maximize this likelihood by using the em algorithm, and we show that the resulting MLE avoids certain unintuitive behavior exhibited by the Kvam and Samaniego (1994) estimator. We find that the new MLE outperforms both the Kvam and Samaniego (1994) estimator and the unbiased estimator due to Stokes and Sager (J Am Stat Assoc 83: 374- 381, 1988) in terms of integrated mean squared error under perfect rankings.
To deal with repeated data or longitudinal data, linear mixed effects models are commonly used. A classical parameter estimation method is the Expectation-Maximization (em) algorithm. In this paper, we propose three n...
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To deal with repeated data or longitudinal data, linear mixed effects models are commonly used. A classical parameter estimation method is the Expectation-Maximization (em) algorithm. In this paper, we propose three new Partial Least Square (PLS) based approaches using the em-algorithm to reduce the high-dimensional data to a lower one for fixed effects in linear mixed models. Unlike the Principal Component Regression approach, the PLS method allows to take into account the link between the outcome and the independent variables. We compare these approaches from a simulation study and a yeast cell-cycle gene expression data set. We demonstrate the performance of two of them and we recommend their use to conduct future analyses for high dimensional data in linear mixed effect models context.
Current status data arise when each subject under study is examined only once at an observation time, and one only knows the failure status of the event of interest at the observation time rather than the exact failur...
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Current status data arise when each subject under study is examined only once at an observation time, and one only knows the failure status of the event of interest at the observation time rather than the exact failure time. Moreover, the obtained failure status is frequently subject to misclassification due to imperfect tests, yielding misclassified current status data. This article conducts regression analysis of such data with the semiparametric probit model, which serves as an important alternative to existing semiparametric models and has recently received considerable attention in failure time data analysis. We consider the nonparametric maximum likelihood estimation and develop an expectation-maximization (em) algorithm by incorporating the generalized pool-adjacent-violators (PAV) algorithm to maximize the intractable likelihood function. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semiparametrically efficient. Furthermore, the numerical results in simulation studies indicate that the proposed method performs satisfactorily in finite samples and outperforms the naive method that ignores misclassification. We then apply the proposed method to a real dataset on chlamydia infection.
Electrical Tomography (ET) is an advanced visualization technique with low-cost, non-invasiveness, non-polluting, and fast-response advantages. However, inherent ill-posed problems and soft-field effects in the ET rec...
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ISBN:
(纸本)9798350380903;9798350380910
Electrical Tomography (ET) is an advanced visualization technique with low-cost, non-invasiveness, non-polluting, and fast-response advantages. However, inherent ill-posed problems and soft-field effects in the ET reconstruction process cause uncertainty in boundary measurements, thereby decreasing the quality of ET reconstruction. To solve this problem, this study employs Gaussian-type fuzzy membership functions to represent measurement uncertainty. The study constructs the objective function for ET reconstruction and performs fuzzy optimization using the Expectation-Maximization (em) algorithm, aiming to enhance the accuracy and stability of ET reconstruction. Experimental findings affirm the effectiveness of the proposed approach in improving the ET reconstruction quality, offering a novel and valuable tool for ET reconstruction.
The large bandwidth and large antenna arrays of millimeter-wave (mm-wave) systems make them an interesting technology for integrated communication and sensing, where communication hardware and waveforms are re-used fo...
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ISBN:
(纸本)9798350387414
The large bandwidth and large antenna arrays of millimeter-wave (mm-wave) systems make them an interesting technology for integrated communication and sensing, where communication hardware and waveforms are re-used for sensing applications. Existing multipath component (MPC) estimation algorithms are designed for multi-antenna systems which rely on digital beamforming, where the baseband signal of each antenna is easily accessible at the receiver. In this paper, we propose a MPC estimation algorithm tailored to typical and cost-effective mm-wave system hardware, which typically relies on analog beamforming. The proposed MPC estimation algorithm does not suffer from the presence of an antenna array sidelobes, unlike classical angular power profile estimation algorithms. The algorithm's performance is evaluated through simulations, and is validated on a 28 GHz experimental testbed. The results show that our algorithm is effectively able to identify individual MPC contributions, without suffering from the influence of antenna array sidelobes.
Interval sampling and two-phase sampling have both been advocated for studying rare failure outcomes. With few exceptions focusing on specific designs such as the case-cohort design, they are often studied separately ...
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Interval sampling and two-phase sampling have both been advocated for studying rare failure outcomes. With few exceptions focusing on specific designs such as the case-cohort design, they are often studied separately in the statistical literature and require different estimation procedures. We consider efficient estimation of interval-censored data collected in a two-phase sampling design using a localized nonparametric likelihood. An expectation maximization algorithm is proposed by exploiting multiple layers of data augmentation that handle transformation function, interval-censoring, and two-phase sampling structure simultaneously. We study the asymptotic properties of the estimators and conduct inference using profile likelihood. We illustrate the performance of the proposed estimator by simulations and an HIV vaccine trial.
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