parameter orthogonality and approximate conditional inference on propose une statistique du rapport de vraisemblance construite a partir de la distribution conditionnelle des observations etant donne les estimateurs du maximum de vraisemblance pour les parametres de nuisance

multivariate mean parameter estimation by using a partly exponential model summary a class of partly exponential models is proposed for the regression analysis of multivariate response data the class is parameterized in terms of the response mean and a general shape parameter it includes the generalized linear error model and exponential dispersion models as special cases maximum likelihood equations for mean parameters are shown to be of the same form as certain generalized estimating equations and maximum likelihood estimates of mean and shape parameters are asymptotically independent some results are given on the efficiency of the estimating equation procedure under misspecification of the response covariance matrix

a correlated probit model for joint modeling of clustered binary and continuous responses a difficulty in joint modeling of continuous and discrete response variables is the lack of a natural multivariate distribution for joint modeling of clustered observations on binary and continuous responses we study a correlated probit model that has an underlying normal latent variable for the binary responses catalano and ryan have factored the model into a marginal and a conditional component and used generalized estimating equations methodology to estimate the effects we propose a monte carlo expectation conditional maximization algorithm for finding maximum likelihood estimates of the mixed model itself extending and accelerating an algorithm for models with binary responses we demonstrate the methodology with a developmental toxicity study measuring fetal weight and a binary malformation status for several litters of mice a simulation study suggests that efficiency gains of joint fittings over separate fittings of the response variables occur mainly for small datasets with strong correlation

copula based regression models for a bivariate mixed discrete and continuous outcome this paper is concerned with regression models for correlated mixed discrete and continuous outcomes constructed using copulas our approach entails specifying marginal regression models for the outcomes and combining them via a copula to form a joint model specifically we propose marginal regression models e g generalized linear models to link the outcomes marginal means to covariates to account for associations between outcomes we adopt the gaussian copula to indirectly specify their joint distributions our approach has two advantages over current methods one regression parameters in models for both outcomes are marginally meaningful and two the association is margin free in the sense that it is characterized by the copula alone by assuming a latent variable framework to describe discrete outcomes the copula used still uniquely determines the joint distribution in addition association measures between outcomes can be interpreted in the usual way we report results of simulations concerning the bias and efficiency of two likelihood based estimation methods for the model finally we illustrate the model using data on burn injuries

regression models for a bivariate discrete and continuous outcome with clustering abstract developmental toxicity studies of laboratory animals play a crucial role in the testing and regulation of chemicals and pharmaceutical compounds exposure to developmental toxicants typically causes a variety of adverse effects such as fetal malformations and reduced fetal weight at term in this article we discuss regression methods for jointly analyzing bivariate discrete and continuous outcomes that are motivated by the statistical problems that arise in analyzing data from developmental toxicity studies we focus on marginal regression models that is models in which the marginal expectation of the bivariate response vector is related to a set of covariates by some known link functions in these models the regression parameters for the marginal expectation are of primary scientific interest whereas the association between the binary and continuous response is considered to be a nuisance characteristic of the data we describe a likelihood based approach based on the general location mode

regression models for mixed discrete and continuous responses with potentially missing values in this paper a likelihood based method for analyzing mixed discrete and continuous regression models is proposed we focus on marginal regression models that is models in which the marginal expectation of the response vector is related to covariates by known link functions the proposed model is based on an extension of the general location model of olkin and tate 1961 annals of mathematical statistics 32 448 465 and can accommodate missing responses when there are no missing data our particular choice of parameterization yields maximum likelihood estimates of the marginal mean parameters that are robust to misspecification of the association between the responses this robustness property does not in general hold for the case of incomplete data there are a number of potential benefits of a multivariate approach over separate analyses of the distinct responses first a multivariate analysis can exploit the correlation structure of the response vector to address intrinsically multivariate questions second multivariate test statistics allow for control over the inflation of the type i error that results when separate analyses of the distinct responses are performed without accounting for multiple comparisons third it is generally possible to obtain more precise parameter estimates by accounting for the association between the responses finally separate analyses of the distinct responses may be difficult to interpret when there is nonresponse because different sets of individuals contribute to each analysis furthermore separate analyses can introduce bias when the missing responses are missing at random mar a multivariate analysis can circumvent both of these problems the proposed methods are applied to two biomedical datasets

erratum to modular properties of nodal curves on k3 surfaces recall that l a d is a nodal curvewith nodesn x1 x on the polarized k3 surface s a such that a pic s is not divisible a 2 2 n 1 note that the article 3 deals only with k3 surfaces with cyclic picard group let s s be the blow up of s at n and denote by ea a 1 the exceptional divisors and e e1 e the normalization c of fits into

correlated bivariate continuous and binary outcomes issues and applications increasingly multiple outcomes are collected in order to characterize treatment effectiveness or to evaluate the impact of large policy initiatives often the multiple outcomes are non commensurate e g measured on different scales the common approach to inference is to model each outcome separately ignoring the potential correlation among the responses we describe and contrast several full likelihood and quasi likelihood multivariate methods for non commensurate outcomes we present a new multivariate model to analyze binary and continuous correlated outcomes using a latent variable we study the efficiency gains of the multivariate methods relative to the univariate approach for complete data all approaches yield consistent parameter estimates when the mean structure of all outcomes depends on the same set of covariates efficiency gains by adopting a multivariate approach are negligible in contrast when the mean outcomes depend on different covariate sets large efficiency gains are realized three real examples illustrate the different approaches

a likelihood based method for analysing longitudinal binary responses summary in this paper we discuss a likelihood based method for analysing correlated binary responses based on a multivariate model it is related to the pseudo maximum likelihood approach suggested recently by zhao prentice 1990 their parameterization results in a simple pairwise model in which the association between responses is modelled in terms of correlations while the present paper uses conditional log odds ratios with this approach higher order associations can be incorporated in a natural way one important advantage of this parameterization is that the maximum likelihood estimates of the marginal mean parameters are robust to misspecification of the time dependence we describe an iterative two stage procedure for obtaining the maximum likelihood estimates two examples are presented to illustrate this methodology

modular properties of nodal curves on k3 surfaces in this article we are going to address the following issues 1 the first is a rigidity property for pairs s c consisting of a general projective k3 surface s and a curve c obtained as the normalization of a nodal hyperplane section hat c hookrightarrow s we prove that a non trivial deformation of a pair s c induces a non trivial deformation of c 2 the second question concerns the wahl map of curves c obtained as above we prove that the wahl map of the normalization of a nodal curve contained in a general projective k3 surface is non surjective in both cases we impose upper bounds on the number of nodes of hat c

on universal severi varieties of low genus k3 surfaces we prove the irreducibility of universal severi varieties parametrizing irreducible reduced nodal hyperplane sections of primitive k3 surfaces of genus g with 3 g 11 g 10

classification of varieties with canonical curve section via gaussian maps on canonical curves the purpose of this article is to develop further a method to classify varieties x inline graphic xmlns xlink http www w3 org 1999 xlink xlink href 01i having canonical curve section using gaussian map computations in a previous article we applied these techniques to classify prime fano threefolds that is fano threefolds whose picard group is generated by the hyperplane bundle in this article we extend this method and classify fano threefolds of higher index and mukai varieties i e varieties of dimension four or more with canonical curve sections first we determine when the hilbert scheme inline graphic xmlns xlink http www w3 org 1999 xlink xlink href 02i of such varieties x is nonempty moreover in the case of picard number one we prove that inline graphic xmlns xlink http www w3 org 1999 xlink xlink href 03i is irreducible and that the examples of fano iskovskih and mukai form a dense open subset of smooth points of inline graphic xmlns xlink http www w3 org 1999 xlink xlink href 04i

response models for mixed binary and quantitative variables summary a number of special representations are considered for the joint distribution of qualitative mostly binary and quantitative variables in addition to the conditional gaussian models and to conditional gaussian regression chain models some emphasis is placed on models derived from an underlying multivariate normal distribution and on models in which discrete probabilities are specified linearly in terms of unknown parameters the possibilities for choosing between the models empirically are examined as well as the testing of independence and conditional independence and the estimation of parameters often the testing of independence is exactly or nearly the same for a number of different models

efficient encoding algorithm for third order spectral null codes an efficient algorithm is presented for encoding unconstrained information sequences into a third order spectral null code of length n and redundancy 9log sub 2 n o log log n the encoding can be implemented using o n integer additions and o nlog n counter increments

rational curves on k3 surfaces we show that projective k3 surfaces with odd picard rank contain infinitely many rational curves our proof extends the bogomolov hassett tschinkel approach i e uses moduli spaces of stable maps and reduction to positive characteristic

maximum likelihood estimation for mixed continuous and categorical data with missing values summary maximum likelihood procedures for analysing mixed continuous and categorical data with missing values are presented the general location model of olkin tate 1961 and extensions introduced by krzanowski 1980 1982 form the basis for our methods maximum likelihood estimation with incomplete data is achieved by an application of the em algorithm dempster laird rubin 1977 special cases of the algorithm include orchard woodbury s 1972 algorithm for incomplete normal samples fuchs s 1982 algorithms for log linear modelling of partially classified contingency tables and day s 1969 algorithm for multivariate normal mixtures applications include a imputation of missing values b logistic regression and discriminant analysis with missing predictors and unclassified observations c linear regression with missing continuous and categorical predictors and d parametric cluster analysis with incomplete data methods are illustrated using data from the st louis risk research project some key word8 cluster analysis discriminant analysis em algorithm incomplete data linear regression logistic regression log linear model mixture model