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Different Approaches for Modeling Grouped Survival Data: A Mango Tree Study

GIOLO, Suely Ruiz; COLOSIMO, Enrico Antonio; DEMETRIO, Clarice Garcia Borges
Fonte: AMER STATISTICAL ASSOC & INT BIOMETRIC SOC Publicador: AMER STATISTICAL ASSOC & INT BIOMETRIC SOC
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.43%
Interval-censored survival data, in which the event of interest is not observed exactly but is only known to occur within some time interval, occur very frequently. In some situations, event times might be censored into different, possibly overlapping intervals of variable widths; however, in other situations, information is available for all units at the same observed visit time. In the latter cases, interval-censored data are termed grouped survival data. Here we present alternative approaches for analyzing interval-censored data. We illustrate these techniques using a survival data set involving mango tree lifetimes. This study is an example of grouped survival data.; Brazilian CAPES Foundation[BEX 0298/01-8]; CNPq

A Log-Linear Regression Model for the Beta-Weibull Distribution

ORTEGA, Edwin M. M.; CORDEIRO, Gauss M.; HASHIMOTO, Elizabeth M.
Fonte: TAYLOR & FRANCIS INC Publicador: TAYLOR & FRANCIS INC
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.36%
We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.; CNPq; CAPES

A bivariate regression model for matched paired survival data: local influence and residual analysis

BARRIGA, Gladys D. C.; LOUZADA-NETO, Francisco; ORTEGA, Edwin M. M.; CANCHO, Vicente G.
Fonte: SPRINGER HEIDELBERG Publicador: SPRINGER HEIDELBERG
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.17%
The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

The log-exponentiated Weibull regression model for interval-censored data

HASHIMOTO, Elizabeth M.; ORTEGA, Edwin M. M.; CANCHO, Vicente G.; CORDEIRO, Gauss M.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.47%
In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.; CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

LEMONTE, ArturJ.; FERRARI, Silvia L. P.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.43%
The two-parameter Birnbaum-Saunders distribution has been used successfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing testing inference on the two parameters of the Birnbaum-Saunders distribution under type-II right censored samples. The likelihood ratio statistic and a recently proposed statistic, the gradient statistic, provide a convenient framework for statistical inference in such a case, since they do not require to obtain, estimate or invert an information matrix, which is an advantage in problems involving censored data. An extensive Monte Carlo simulation study is carried out in order to investigate and compare the finite sample performance of the likelihood ratio and the gradient tests. Our numerical results show evidence that the gradient test should be preferred. Further, we also consider the generalized Birnbaum-Saunders distribution under type-II right censored samples and present some Monte Carlo simulations for testing the parameters in this class of models using the likelihood ratio and gradient tests. Three empirical applications are presented. (C) 2011 Elsevier B.V. All rights reserved.; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); CNPq; FAPESP; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The log-exponentiated generalized gamma regression model for censored data

Ortega, Edwin M. M.; Cordeiro, Gauss M.; Pascoa, Marcelino A. R.; Couto, Epaminondas V.
Fonte: TAYLOR & FRANCIS LTD; ABINGDON Publicador: TAYLOR & FRANCIS LTD; ABINGDON
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.38%
For the first time, we introduce a generalized form of the exponentiated generalized gamma distribution [Cordeiro et al. The exponentiated generalized gamma distribution with application to lifetime data, J. Statist. Comput. Simul. 81 (2011), pp. 827-842.] that is the baseline for the log-exponentiated generalized gamma regression model. The new distribution can accommodate increasing, decreasing, bathtub- and unimodal-shaped hazard functions. A second advantage is that it includes classical distributions reported in the lifetime literature as special cases. We obtain explicit expressions for the moments of the baseline distribution of the new regression model. The proposed model can be applied to censored data since it includes as sub-models several widely known regression models. It therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data. We show that our extended regression model is very useful by means of two applications to real data.; CNPq; CNPq; CAPES; CAPES

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

Ortega, Edwin M. M.; Cordeiro, Gauss M.; Lemonte, Artur J.
Fonte: ELSEVIER SCIENCE BV; AMSTERDAM Publicador: ELSEVIER SCIENCE BV; AMSTERDAM
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.39%
The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.; CNPq; CNPq; FAPESP (Brazil); FAPESP (Brazil)

Modelo de regressão para dados com censura intervalar e dados de sobrevivência grupados; Regression model for interval-censored data and grouped survival data

Hashimoto, Elizabeth Mie
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 04/02/2009 PT
Relevância na Pesquisa
66.3%
Neste trabalho foi proposto um modelo de regressão para dados com censura intervalar utilizando a distribuição Weibull-exponenciada, que possui como característica principal a função de taxa de falha que assume diferentes formas (unimodal, forma de banheira, crescente e decrescente). O atrativo desse modelo de regressão é a sua utilização para discriminar modelos, uma vez que o mesmo possui como casos particulares os modelos de regressão Exponencial, Weibull, Exponencial-exponenciada, entre outros. Também foi estudado um modelo de regressão para dados de sobrevivência grupados na qual a abordagem é fundamentada em modelos de tempo discreto e em tabelas de vida. A estrutura de regressão representada por uma probabilidade é modelada adotando-se diferentes funções de ligação, tais como, logito, complemento log-log, log-log e probito. Em ambas as pesquisas, métodos de validação dos modelos estatísticos propostos são descritos e fundamentados na análise de sensibilidade. Para detectar observações influentes nos modelos propostos, foram utilizadas medidas de diagnóstico baseadas na deleção de casos, denominadas de influência global e medidas baseadas em pequenas perturbações nos dados ou no modelo proposto...

Modelo de regressão log-gama generalizado exponenciado com dados censurados; The log-exponentiated generalized gamma regression model with censored data

Couto, Epaminondas de Vasconcellos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 22/02/2010 PT
Relevância na Pesquisa
66.43%
No presente trabalho, e proposto um modelo de regressão utilizando a distribuição gama generalizada exponenciada (GGE) para dados censurados, esta nova distribuição e uma extensão da distribuição gama generalizada. A distribuição GGE (CORDEIRO et al., 2009) que tem quatro parâmetros pode modelar dados de sobrevivência quando a função de risco tem forma crescente, decrescente, forma de U e unimodal. Neste trabalho apresenta-se uma expansão natural da distribuição GGE para dados censurados, esta distribuição desperta o interesse pelo fato de representar uma família paramétrica que possui como casos particulares outras distribuições amplamente utilizadas na analise de dados de tempo de vida, como as distribuições gama generalizada (STACY, 1962), Weibull, Weibull exponenciada (MUDHOLKAR et al., 1995, 1996), exponencial exponenciada (GUPTA; KUNDU, 1999, 2001), Rayleigh generalizada (KUNDU; RAKAB, 2005), dentre outras, e mostra-se útil na discriminação entre alguns modelos probabilísticos alternativos. Considerando dados censurados, e abordado o método de máxima verossimilhança para estimar os parâmetros do modelo proposto. Outra proposta deste trabalho e introduzir um modelo de regressão log-gama generalizado exponenciado com efeito aleatório. Por fim...

Confiabilidade em sistemas coerentes: um modelo bayesiano Weibull.; Reliability in coherent systems: a bayesian weibull model

Bhering, Felipe Lunardi
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/06/2013 PT
Relevância na Pesquisa
46.36%
O principal objetivo desse trabalho é introduzir um modelo geral bayesiano Weibull hierárquico para dados censurados que estima a função de confiabilidade de cada componente para sistemas de confiabilidade coerentes. São introduzidos formas de estimação mais sólidas, sem a inserção de estimativas médias nas funções de confiabilidade (estimador plug-in). Através desse modelo, são expostos e solucionados exemplos na área de confiabilidade como sistemas em série, sistemas em paralelo, sistemas k-de-n, sistemas bridge e um estudo clínico com dados censurados intervalares. As soluções consideram que as componentes tem diferentes distribuições, e nesse caso, o sistema bridge ainda não havia solução na literatura. O modelo construído é geral e pode ser utilizado para qualquer sistema coerente e não apenas para dados da área de confiabilidade, como também na área de sobrevivência, dentre outros. Diversas simulações com componentes com diferentes proporções de censura, distintas médias, três tipos de distribuições e tamanhos de amostra foram feitas em todos os sistemas para avaliar a eficácia do modelo.; The main purpose of this work is to introduce a general bayesian Weibull hierarchical model for censored data which estimates each reliability components function from coherent systems. Its introduced estimation procedures which do not consider plug-in estimators. Also...

A BIVARIATE GENERALIZED EXPONENTIAL DISTRIBUTION DERIVED FROM COPULA FUNCTIONS IN THE PRESENCE OF CENSORED DATA AND COVARIATES

Achcar,Jorge Alberto; Moala,Fernando Antônio; Tarumoto,Mario Hissamitsu; Coladello,Leandro Fernandes
Fonte: Sociedade Brasileira de Pesquisa Operacional Publicador: Sociedade Brasileira de Pesquisa Operacional
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2015 EN
Relevância na Pesquisa
66.29%
In this paper, we introduce a Bayesian analysis for a bivariate generalized exponential distribution in the presence of censored data and covariates derived from Copula functions. The generalized exponential distribution could be a good alternative to analyze lifetime data in comparison to usual existing parametric lifetime distributions as Weibull or Gamma distributions. We have being using standard existing MCMC (Markov Chain Monte Carlo) methods to simulate samples for the joint posterior of interest. Two examples are introduced to illustrate the proposed methodology: an example with simulated bivariate lifetime data and an example with a real lifetime data set.

A Bayesian MCMC approach to survival analysis with doubly-censored data

Yu, Binbing
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/08/2010 EN
Relevância na Pesquisa
46.51%
Doubly-censored data refers to time to event data for which both the originating and failure times are censored. In studies involving AIDS incubation time or survival after dementia onset, for example, data are frequently doubly-censored because the date of the originating event is interval-censored and the date of the failure event usually is right-censored. The primary interest is in the distribution of elapsed times between the originating and failure events and its relationship to exposures and risk factors. The estimating equation approach [Sun, et al. 1999. Regression analysis of doubly censored failure time data with applications to AIDS studies. Biometrics 55, 909-914] and its extensions assume the same distribution of originating event times for all subjects. This paper demonstrates the importance of utilizing additional covariates to impute originating event times, i.e., more accurate estimation of originating event times may lead to less biased parameter estimates for elapsed time. The Bayesian MCMC method is shown to be a suitable approach for analyzing doubly-censored data and allows a rich class of survival models. The performance of the proposed estimation method is compared to that of other conventional methods through simulations. Two examples...

Estimating Correlation with Multiply Censored Data Arising from the Adjustment of Singly Censored Data

Newton, Elizabeth; Rudel, Ruthann
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/01/2007 EN
Relevância na Pesquisa
46.55%
Environmental data frequently are left censored due to detection limits of laboratory assay procedures. Left censored means that some of the observations are known only to fall below a censoring point (detection limit). This presents difficulties in statistical analysis of the data. In this paper, we examine methods for estimating the correlation between variables each of which is censored at multiple points. Multiple censoring frequently arises due to adjustment of singly censored laboratory results for physical sample size. We discuss maximum likelihood (ML) estimation of the correlation and introduce a new method (cp.mle2) that, instead of using the multiply censored data directly, relies on ML estimates of the covariance of the singly censored laboratory data. We compare the ML methods with Kendall's tau-b (ck.taub) which is a modification Kendall's tau adjusted for ties, and several commonly used simple substitution methods: correlations estimated with non-detects set to the detection limit divided by two and correlations based on detects only (cs.det) with non-detects set to missing. The methods are compared based on simulations and real data. In the simulations, censoring levels are varied from 0 to 90%, ρ from -0.8 to 0.8 and ν (variance of physical sample size) is set to 0 and 0.5...

Semi-parametric modeling of excesses above high multivariate thresholds with censored data

Sabourin, Anne
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/12/2014
Relevância na Pesquisa
46.42%
How to include censored data in a statistical analysis is a recur-rent issue in statistics. In multivariate extremes, the dependence structure of large observations can be characterized in terms of a non parametric angular measure, while marginal excesses above asymptotically large thresholds have a parametric distribution. In this work, a flexible semi-parametric Dirichlet mix-ture model for angular measures is adapted to the context of censored data and missing components. One major issue is to take into account censoring intervals overlapping the extremal threshold, without knowing whether the correspond-ing hidden data is actually extreme. Further, the censored likelihood needed for Bayesian inference has no analytic expression. The first issue is tackled using a Poisson process model for extremes, whereas a data augmentation scheme avoids multivariate integration of the Poisson process intensity over both the censored intervals and the failure region above threshold. The implemented MCMC algorithm allows simultaneous estimation of marginal and dependence parameters, so that all sources of uncertainty other than model bias are cap-tured by posterior credible intervals. The method is illustrated on simulated and real data.

Identification of Outlying Observations with Quantile Regression for Censored Data

Eo, Soo-Heang; Hong, Seung-Mo; Cho, HyungJun
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/04/2014
Relevância na Pesquisa
46.38%
Outlying observations, which significantly deviate from other measurements, may distort the conclusions of data analysis. Therefore, identifying outliers is one of the important problems that should be solved to obtain reliable results. While there are many statistical outlier detection algorithms and software programs for uncensored data, few are available for censored data. In this article, we propose three outlier detection algorithms based on censored quantile regression, two of which are modified versions of existing algorithms for uncensored or censored data, while the third is a newly developed algorithm to overcome the demerits of previous approaches. The performance of the three algorithms was investigated in simulation studies. In addition, real data from SEER database, which contains a variety of data sets related to various cancers, is illustrated to show the usefulness of our methodology. The algorithms are implemented into an R package OutlierDC which can be conveniently employed in the \proglang{R} environment and freely obtained from CRAN.

MOSAIC_SSD: a new web-tool for the Species Sensitivity Distribution, allowing to include censored data by maximum likelihood

King, Guillaume Kon Kam; Veber, Philippe; Charles, Sandrine; Delignette-Muller, Marie Laure
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.44%
Censored data are seldom taken into account in Species Sensitivity Distribution (SSD) analysis. However, they are found in virtually every dataset and sometimes represent the better part of the data. Stringent recommendations on data quality often lead to discard a lot of this meaningful data, often resulting in datasets of reduced size, which lack representativeness of any realistic community. However, it is reasonably simple to include censored data into SSD by using an extension of the standard maximum likelihood method. In this paper, we detail this approach based on the use of the R-package \emph{fitdistrplus}, dedicated to the fit of parametric probability distributions. In particular, we introduce the new web-tool MOSAIC$\_$SSD to fit an SSD on datasets containing any kind of data, censored or not. MOSAIC$\_$SSD allows predicting any Hazardous Concentration (HC) and provides in addition bootstrap confidence intervals on the prediction. In the end, taking examples from published data, we illustrate the added value of including censored data in SSD.; Comment: 26 pages, 2 figures

Weighted empirical likelihood in some two-sample semiparametric models with various types of censored data

Ren, Jian-Jian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/03/2008
Relevância na Pesquisa
46.56%
In this article, the weighted empirical likelihood is applied to a general setting of two-sample semiparametric models, which includes biased sampling models and case-control logistic regression models as special cases. For various types of censored data, such as right censored data, doubly censored data, interval censored data and partly interval-censored data, the weighted empirical likelihood-based semiparametric maximum likelihood estimator $(\tilde{\theta}_n,\tilde{F}_n)$ for the underlying parameter $\theta_0$ and distribution $F_0$ is derived, and the strong consistency of $(\tilde{\theta}_n,\tilde{F}_n)$ and the asymptotic normality of $\tilde{\theta}_n$ are established. Under biased sampling models, the weighted empirical log-likelihood ratio is shown to have an asymptotic scaled chi-squared distribution for censored data aforementioned. For right censored data, doubly censored data and partly interval-censored data, it is shown that $\sqrt{n}(\tilde{F}_n-F_0)$ weakly converges to a centered Gaussian process, which leads to a consistent goodness-of-fit test for the case-control logistic regression models.; Comment: Published in at http://dx.doi.org/10.1214/009053607000000695 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Influence diagnostics in exponentiated-Weibull regression models with censored data

Ortega, Edwin M. M.; Cancho, Vicente G.; Bolfarine, Heleno
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2006 ENG
Relevância na Pesquisa
56.17%
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influential observations in data sets. In this paper, we follow the local influence approach (Cook 1986) in detecting influential observations with exponentiated-Weibull regression models. The relevance of the approach is illustrated with a real data set, where it is shown that by removing the most influential observations, there is a change in the decision about which model fits the data better.

Quantile estimation of the rejection distribution of food products integrating assessor values and interval-censored consumer data

Langohr, Klaus; Gómez, Guadalupe; Hough, Guillermo
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2013 ENG
Relevância na Pesquisa
56.28%
Fitting parametric survival models with interval-censored data is a common task in survival analysis and implemented in many statistical software packages. Here, we present a novel approach to fit such models if the values on the scale of interest are measured with error. Random effects ANOVA models are used to account for the measurement errors and the likelihood function of the parametric survival model is maximized with numerical methods. An illustration is provided with a real data set on the rejection of yogurt as a function of its acid taste.

The Tobit Kalman filter: an estimator for censored data

Allik, Bethany
Fonte: University of Delaware Publicador: University of Delaware
Tipo: Tese de Doutorado
Relevância na Pesquisa
56.5%
Zurakowski, Ryan; The Kalman Filter has become ubiquitous in tracking and estimation. Many estimation applications, especially those using low cost commercial of-the-shelf sensors (COTS), are subject to a special type of measurement nonlinearity called censoring. Censoring frequently takes the form of sensor saturation, occlusion regions, and limit-of-detection. These forms of censoring are known as Tobit model Type 1 censoring. Introduction of censored measurements into the Kalman filter results in biased estimates of the underlying states. In this dissertation, we present the first formulation of the Kalman filter capable of estimating state variables from censored data without bias. We refer to this formulation as the Tobit Kalman filter. Previous work on Kalman filtering with measurement nonlinearities or sensor faults includes a Kalman filter for intermittent measurements, the particle filter, the unscented Kalman filter (UKF) and the extended Kalman filter (EKF). Intermittent measurement nonlinearity is similar to the censored measurement model; with the exception that censored data measurements are correlated with the state values. Previous work for intermittent measurements in estimation reduces the Kalman filter to a linear predictor when the measurement is missing. Use of either this formulation or a standard Kalman filter as an estimator in a censored data example will result in a biased estimate of the state. The particle filter is able to estimate the state values when the measurements are subject to censoring under certain cases...