A Quasi-Likelihood Approach for Ordered Categorical Data with Overdispersion

Wang, You‐Gan

doi:10.2307/2532841

Cited by 8 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conditional approach provides a more flexible way of modelling overdispersion. For example, in the case when N is known, Wang (1996) constructed conditional QL functions for categorical data analysis.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Equations for Removal Data Analysis

Wang¹

1999

Biometrics

Self Cite

View full text Add to dashboard Cite

We consider the problem of estimating a population size from successive catches taken during a removal experiment and propose two estimating functions approaches, the traditional quasi-likelihood (TQL) approach for dependent observations and the conditional quasi-likelihood (CQL) approach using the conditional mean and conditional variance of the catch given previous catches. Asymptotic covariance of the estimates and the relationship between the two methods are derived. Simulation results and application to the catch data from smallmouth bass show that the proposed estimating functions perform better than other existing methods, especially in the presence of overdispersion.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The resulting estimating equations take the same form as the generalized estimating equations (GEE) by Liang and Zeger (1986). The second one is based on the conditional mean and conditional variance of each catch for given previous catches (Wang, 1996). The relationship between these two approaches is also established.…”

Section: Introductionmentioning

confidence: 99%

Estimating Equations for Removal Data Analysis

Wang¹

1999

Biometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The methods proposed here are based on conditioning. Using conditional expectations, we can adjust for the bias in the sampling process and even the possible correlations between the observations (Wang, 1996;Albert, Follmann, and Barnhart, 1997). The modifications to the usual estimating functions introduced in this paper may become useful examples for data analysis when biased sampling or nonignorable nonresponse occurs.…”

Section: Discussionmentioning

confidence: 99%

Estimating Equations with Nonignorably Missing Response Data

Wang

1999

Biometrics

Self Cite

View full text Add to dashboard Cite

Troxel, Lipsitz, and Brennan (1997, Biometrics 53, 857-869) considered parameter estimation from survey data with nonignorable nonresponse and proposed weighted estimating equations to remove the biases in the complete-case analysis that ignores missing observations. This paper suggests two alternative modifications for unbiased estimation of regression parameters when a binary outcome is potentially observed at successive time points. The weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) is also modified to obtain unbiased estimating functions. The suggested estimating functions are unbiased only when the missingness probability is correctly specified, and misspecification of the missingness model will result in biases in the estimates. Simulation studies are carried out to assess the performance of different methods when the covariate is binary or normal. For the simulation models used, the relative efficiency of the two new methods to the weighting methods is about 3.0 for the slope parameter and about 2.0 for the intercept parameter when the covariate is continuous and the missingness probability is correctly specified. All methods produce substantial biases in the estimates when the missingness model is misspecified or underspecified. Analysis of data from a medical survey illustrates the use and possible differences of these estimating functions.

show abstract

“…Lesaffre et al (1994Lesaffre et al ( , 1996, Lindsey et al (1997), Qu & Tan (1998), Qu et al (1995. Wang (1996), Williams et al ), Williamson & Kim (1996, and Williamson & Lee (1996).…”

Section: Other Applicationsmentioning

confidence: 99%

Modeling Clustered Ordered Categorical Data: A Survey

Agresti¹,

Natarajan²

2001

Int Statistical Rev

View full text Add to dashboard Cite

This article surveys various strategies for modeling ordered categorical (ordinal) response variables when the data have some type of clustering, extending a similar survey for binary data by Pendergast, Gange, Newton, Lindstrom, Palta & Fisher (1996). An important special case is when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. A much greater variety of models and fitting methods are available than when a similar survey for repeated ordinal response data was prepared a decade ago (Agresti, 1989). The primary emphasis of the review is on two cla.s.ses of models, marginal models for which effects are averaged over all clusters at particular levels of predictors, and cluster-specific models for which effects apply at the cluster level. We present the two types of models in the ordinal context, review the literature for each, and discuss connections between them. Then, we summarize some alternative modeling approaches and ways of estimating parameters, including a Bayesian approach. We also discuss applications and areas likely to be popular for future research, such as ways of handling missing data and ways of modeling agreement and evaluating the accuracy of diagnostic tests. Finally, we review the current availability of software for using the methods discussed in this article. 18 2 4 11 14 22 Source: Fmcom, Chunag-Skin & Landis (1989) Cluster-specific and Marginal ModelsIn the formulation of models, we refer to the sampling units as clusters. In many applications, such as Table 1, each cluster is a set of repeated measurements on a subject. In others, each cluster is a set of subjects expected to be more similar to each other than to other subjects, such as a litter of mice in a teratology study.As with binary and other forms of categorical data (Pendergast et al., 1996), two major types of model for ordinal responses differ in terms of whether they have population-averaged or clusterspecijc (sometimes called subject-specific) effects. The latter models refer to conditional distributions at the cluster (e.g., subject) level, whereas the former models refer to marginal distributions, averaged over clusters in the population. The choice of model affects whether parameter interpretations apply at the cluster or the population level (Zeger, Liang & Albert, 1988; Neuhaus, Kalbfleisch & Hauck, 1991;Ten Have, Landis & Hartzel, 1996). Population-level interpretations are more relevant in epidemiological studies that focus on overall frequency of occurrence in a population. As we will discuss in Section 3.2, approximate relationships exist between population-averaged parameters in marginal models and corresponding parameters in cluster-specific models; however, a cluster-specific model usually does not imply a marginal model of the same form, and a marginal model need not have any simple and meaningful cluster-specific model that implies it. A third type of model, called a transition model, is used in longitudinal studies to describe the distribution of a respons...

show abstract

A Quasi-Likelihood Approach for Ordered Categorical Data with Overdispersion

Cited by 8 publications

References 14 publications

Estimating Equations for Removal Data Analysis

Estimating Equations for Removal Data Analysis

Estimating Equations with Nonignorably Missing Response Data

Modeling Clustered Ordered Categorical Data: A Survey

Contact Info

Product

Resources

About