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Binary models for marginal independence
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B
, 2005
"... A number of authors have considered multivariate Gaussian models for marginal independence. In this paper we develop models for binary data with the same independence structure. The models can be parameterized based on Möbius inversion and maximum likelihood estimation can be performed using a versi ..."
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Cited by 16 (2 self)
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A number of authors have considered multivariate Gaussian models for marginal independence. In this paper we develop models for binary data with the same independence structure. The models can be parameterized based on Möbius inversion and maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. The approach is illustrated on a simple example. Relations to multivariate logistic and dependence ratio models are discussed.
Algebraic Descriptions of Nominal Multivariate Discrete Data
 J. Multivariate Anal
, 1995
"... Traditionally, multivariate discrete data are analyzed by means of loglinear models. In this paper we show how an algebraic approach leads naturally to alternative models, parametrized in terms of the moments of the distribution. Moreover we derive a complete characterization of all meaningful tran ..."
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Cited by 1 (0 self)
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Traditionally, multivariate discrete data are analyzed by means of loglinear models. In this paper we show how an algebraic approach leads naturally to alternative models, parametrized in terms of the moments of the distribution. Moreover we derive a complete characterization of all meaningful transformations of the components and show how transformations affect the moments of a distribution. It turns out that our models provide the necessary formal description of longitudinal data; moreover in the classical case, they can be considered as an analysis tool, complementary to loglinear models. 1 Introduction We start with a given multivariate discrete nominal variable X. Questions of interest about X can be roughly divided into two groups. One group is related to conditional characteristics such as conditional independencies or questions concerning the sign and/or magnitude of logodds ratios. The other group focuses on marginal characteristics such as marginal independencies or multiv...
Analyzing Incomplete Discrete Longitudinal Clinical Trial Data
, 2006
"... Abstract. Commonly used methods to analyze incomplete longitudinal clinical trial data include complete case analysis (CC) and last observation carried forward (LOCF). However, such methods rest on strong assumptions, including missing completely at random (MCAR) for CC and unchanging profile after ..."
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Cited by 1 (1 self)
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Abstract. Commonly used methods to analyze incomplete longitudinal clinical trial data include complete case analysis (CC) and last observation carried forward (LOCF). However, such methods rest on strong assumptions, including missing completely at random (MCAR) for CC and unchanging profile after dropout for LOCF. Such assumptions are too strong to generally hold. Over the last decades, a number of full longitudinal data analysis methods have become available, such as the linear mixed model for Gaussian outcomes, that are valid under the much weaker missing at random (MAR) assumption. Such a method is useful, even if the scientific question is in terms of a single time point, for example, the last planned measurement occasion, and it is generally consistent with the intentiontotreat principle. The validity of such a method rests on the use of maximum likelihood, under which the missing data mechanism is ignorable as soon as it is MAR. In this paper, we will focus on nonGaussian outcomes, such as
• Espressi tipicamente nella forma
"... Analisi di dati categorici con modelli marginali epressi tramite vincoli di uguaglianza e disuguaglianza ..."
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Analisi di dati categorici con modelli marginali epressi tramite vincoli di uguaglianza e disuguaglianza
A CovariateBased Coefficient of Source Dependence for CaptureRecapture Models
"... In capturerecapture modelling, it is necessary to account for possible dependence betwee sources. Along with nominal information, epidemiological capturerecapture studies often involve the collection of a variety of individual covariates. Under the assumption that we can estimate the covariat ..."
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In capturerecapture modelling, it is necessary to account for possible dependence betwee sources. Along with nominal information, epidemiological capturerecapture studies often involve the collection of a variety of individual covariates. Under the assumption that we can estimate the covariate distribution in the population of interest and that sources are conditionally independent given the covariates, we can estimate a coefficient of source dependence (CSD) for every set of sources. The CSD estimates are based on the estimated conditional covariate distribution given the sources. Such CSD estimates can be used as guides to fit joint loglinear models and can also be introduced in a marginal loglinear model to produce a conditional population estimate. We illustrate these applications on data from the Auckland Leg Ulcer Study, 19971998. Keywords: capturerecapture, source dependence, conditional independence, loglinear models, marginal models, leg ulcers 1 Int...
SOCIOf()gy,
, 2001
"... author Abstract::Jt:lt!stlCaJ methods have had a successful in contributing to a greatly irnr)l'(lVc:d standard of scientific rigor in the discipline. I identify three overlapping postwar,YP1'IPl',ltlnTls of statistical methods in sociology, on the kinds data address. The generation, which started ..."
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author Abstract::Jt:lt!stlCaJ methods have had a successful in contributing to a greatly irnr)l'(lVc:d standard of scientific rigor in the discipline. I identify three overlapping postwar,YP1'IPl',ltlnTls of statistical methods in sociology, on the kinds data address. The generation, which started in the late 1940s, deals with crosstabulations, and focuses on measures of association and loglinear models, perhaps the area of statistics to which SOICIC)]Ogy has contributed the most. The second generation, which began in the 1960s, deals with unitlevel survey data, and focuses on LISRELtype causal models and event history The third generation, starting to emerge in the late 1980s, deals with data that do not fall easily into either of these categories, either because they have a different form, such as texts or narratives, or because dependence is a crucial aspect, as with spatial or social nplhvlH'k data. There are many new challenges and the area is ripe for statistical research;
COMPLIANCE MEASUREMENT ERROR FOR SOME PROBLEMS IN INHALATION TOXICOLOGY
, 1997
"... under Compliance Measurement Error for ..."
ℓEM: A general program for the analysis of categorical data 1
, 1997
"... When you report results obtained with ℓEM, you should refer to this manual as “Vermunt, J.K. ..."
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When you report results obtained with ℓEM, you should refer to this manual as “Vermunt, J.K.
with application to safety studies for drugs
, 2003
"... Multivariate tests comparing binomial probabilities, ..."
Biometrics DOI: 10.1111/j.15410420.2006.00525.x Multivariate Extensions of McNemar’s Test
"... Summary. This article considers global tests of differences between paired vectors of binomial probabilities, based on data from two dependent multivariate binary samples. Difference is defined as either an inhomogeneity in the marginal distributions or asymmetry in the joint distribution. For detec ..."
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Summary. This article considers global tests of differences between paired vectors of binomial probabilities, based on data from two dependent multivariate binary samples. Difference is defined as either an inhomogeneity in the marginal distributions or asymmetry in the joint distribution. For detecting the first type of difference, we propose a multivariate extension of McNemar’s test and show that it is a generalized score test under a GEE approach. Univariate features such as the relationship between the Wald and score test and the dropout of pairs with the same response carry over to the multivariate case and the test does not depend on the working correlation assumption among the components of the multivariate response. For sparse or imbalanced data, such as occurs when the number of variables is large or the proportions are close to zero, the test is best implemented using a bootstrap, and if this is computationally too complex, a permutation distribution. We apply the test to safety data for a drug, in which two doses are evaluated by comparing multiple responses by the same subjects to each one of them.