@Book{brazzale07,
  author =	 {A R Brazzale and A C Davison and N Reid},
  title = 	 {Applied Asymptotics---case studies in small-sample
  statistics} ,
  publisher = 	 {Cambridge University Press},
  year = 	 2007}


@Book{pawitan01,
  author =	 {Yudi Pawitan},
  title = 	 {{In All Likelihood---Statistical Modelling and
  Inference Using Likelihood}},
  publisher = 	 {Oxford University Press},
  year = 	 2001
}

@Manual{R11,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Development Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2011},
  note = {{ISBN} 3-900051-07-0},
  url = {http://www.R-project.org/},
}

@Article{tutz96,
  author = 	 {Gerhard Tutz and Wolfgang Hennevogl},
  title = 	 {Random effects in ordinal regression models},
  journal = 	 {Computational Statistics \& Data Analysis},
  year = 	 1996,
  volume =	 22,
  pages =	 {537-557}
}

@Article{efron78,
  author = 	 {Bradley Efron and David V Hinkley},
  title = 	 {{Assessing the accuracy of the maximum likelihood
                  estimator: Observed versus expected Fisher information}},
  journal = 	 {Biometrika},
  year = 	 1978,
  volume = 	 65,
  number = 	 3,
  pages = 	 {457-487}}

@article{bauer09,
   author = {Bauer, Daniel},
   affiliation = {University of North Carolina Department of
                  Psychology Chapel Hill NC 27599-3270 USA}, 
   title = {A Note on Comparing the Estimates of Models
                  for†Cluster-Correlated or Longitudinal Data with 
                  Binary or Ordinal†Outcomes},  
   journal = {Psychometrika},
   publisher = {Springer New York},
   issn = {0033-3123},
   keyword = {Humanities, Social Sciences and Law},
   pages = {97-105},
   volume = {74},
   issue = {1},
   url = {http://dx.doi.org/10.1007/s11336-008-9080-1},
   year = {2009}
}

@article{fielding04,
   author = {Fielding, Antony},
   title = {Scaling for Residual Variance Components of Ordered
                  Category Responses in Generalised Linear Mixed
                  Multilevel Models}, 
   journal = {Quality \& Quantity},
   publisher = {Springer Netherlands},
   issn = {0033-5177},
   keyword = {Humanities, Social Sciences and Law},
   pages = {425-433},
   volume = {38},
   issue = {4},
   url = {http://dx.doi.org/10.1023/B:QUQU.0000043118.19835.6c},
   year = {2004}
}

@article{winship84,
 jstor_articletype = {research-article},
 title = {Regression Models with Ordinal Variables},
 author = {Winship, Christopher and Mare, Robert D.},
 journal = {American Sociological Review},
 jstor_issuetitle = {},
 volume = {49},
 number = {4},
 jstor_formatteddate = {Aug., 1984},
 pages = {512-525},
 url = {http://www.jstor.org/stable/2095465},
 ISSN = {00031224},
 abstract = {Most discussions of ordinal variables in the
              sociological literature debate the suitability of
              linear regression and structural equation methods
              when some variables are ordinal. Largely ignored in
              these discussions are methods for ordinal variables
              that are natural extensions of probit and logit
              models for dichotomous variables. If ordinal
              variables are discrete realizations of unmeasured
              continuous variables, these methods allow one to
              include ordinal dependent and independent variables
              into structural equation models in a way that (1)
              explicitly recognizes their ordinality, (2) avoids
              arbitrary assumptions about their scale, and (3)
              allows for analysis of continuous, dichotomous, and
              ordinal variables within a common statistical
              framework. These models rely on assumed probability
              distributions of the continuous variables that
              underly the observed ordinal variables, but these
              assumptions are testable. The models can be
              estimated using a number of commonly used
              statistical programs. As is illustrated by an
              empirical example, ordered probit and logit models,
              like their dichotomous counterparts, take account of
              the ceiling and floor restrictions on models that
              include ordinal variables, whereas the linear
              regression model does not.}, 
 language = {English},
 year = {1984},
 publisher = {American Sociological Association},
 copyright = {Copyright © 1984 American Sociological Association},
}

@article{thompson81,
 jstor_articletype = {research-article},
 title = {Composite Link Functions in Generalized Linear Models},
 author = {Thompson, R. and Baker, R. J.},
 journal = {Journal of the Royal Statistical Society. Series C
              (Applied Statistics)}, 
 jstor_issuetitle = {},
 volume = {30},
 number = {2},
 jstor_formatteddate = {1981},
 pages = {125-131},
 url = {http://www.jstor.org/stable/2346381},
 ISSN = {00359254},
 abstract = {In generalized linear models each observation is
              linked with a predicted value based on a linear
              function of some systematic effects. We sometimes
              require to link each observation with a linear
              function of more than one predicted value. We embed
              such models into the generalized linear model
              framework using composite link functions. The
              computer program GLIM-3 can be used to fit these
              models. Illustrative examples are given including a
              mixed-up contingency table and grouped normal
              data.}, 
 language = {English},
 year = {1981},
 publisher = {Blackwell Publishing for the Royal Statistical Society},
 copyright = {Copyright © 1981 Royal Statistical Society},
}

@article{burridge81,
 jstor_articletype = {research-article},
 title = {A Note on Maximum Likelihood Estimation for Regression
              Models Using Grouped Data}, 
 author = {Burridge, J.},
 journal = {Journal of the Royal Statistical Society. Series B
              (Methodological)}, 
 jstor_issuetitle = {},
 volume = {43},
 number = {1},
 jstor_formatteddate = {1981},
 pages = {41-45},
 url = {http://www.jstor.org/stable/2985147},
 ISSN = {00359246},
 abstract = {The estimation of parameters for a class of
              regression models using grouped or censored data is
              considered. It is shown that with a simple
              reparameterization some commonly used distributions,
              such as the normal and extreme value, result in a
              log-likelihood which is concave with respect to the
              transformed parameters. Apart from its theoretical
              implications for the existence and uniqueness of
              maximum likelihood estimates, this result suggests
              minor changes to some commonly used algorithms for
              maximum likelihood estimation from grouped data. Two
              simple examples are given.}, 
 language = {English},
 year = {1981},
 publisher = {Blackwell Publishing for the Royal Statistical Society},
 copyright = {Copyright © 1981 Royal Statistical Society},
}

@article{pratt81,
 jstor_articletype = {research-article},
 title = {Concavity of the Log Likelihood},
 author = {Pratt, John W.},
 journal = {Journal of the American Statistical Association},
 jstor_issuetitle = {},
 volume = {76},
 number = {373},
 jstor_formatteddate = {Mar., 1981},
 pages = {103-106},
 url = {http://www.jstor.org/stable/2287052},
 ISSN = {01621459},
 abstract = {For a very general regression model with an ordinal
              dependent variable, the log likelihood is proved
              concave if the derivative of the underlying response
              function has concave logarithm. For a binary
              dependent variable, a weaker condition suffices,
              namely, that the response function and its
              complement each have concave logarithm. The normal,
              logistic, sine, and extreme-value distributions,
              among others, satisfy the stronger condition, the t
              (including Cauchy) distributions only the
              weaker. Some converses and generalizations are also
              given. The model is that which arises from an
              ordinary linear regression model with a continuous
              dependent variable that is partly unobservable,
              being either grouped into intervals with unknown
              endpoints, or censored, or, more generally, grouped
              in some regions, censored in others, and observed
              exactly elsewhere.}, 
 language = {English},
 year = {1981},
 publisher = {American Statistical Association},
 copyright = {Copyright © 1981 American Statistical Association},
}


@Manual{christensen11,
  title = 	 {Analysis of ordinal data with cumulative link models
                  --- estimation with the \textsf{ordinal} package},
  author = 	 {Rune Haubo Bojesen Christensen},
  note = 	 {R-package version 2011.09-13},
  year = 	 2011}

@Book{agresti10,
  author = 	 {Alan Agresti},
  title = 	 {Analysis of ordinal categorical data},
  publisher = 	 {Wiley},
  year = 	 2010,
  edition = 	 {2nd}}

@Book{agresti02,
  author =	 {Alan Agresti},
  title = 	 {Categorical Data Analysis},
  publisher = 	 {Wiley},
  year = 	 2002,
  edition =	 {2nd}
}

@Article{mccullagh80,
  author = 	 {Peter McCullagh},
  title = 	 {Regression Models for Ordinal Data},
  journal = 	 {Journal of the Royal Statistical Society, Series B},
  year = 	 1980,
  volume =	 42,
  pages =	 {109-142}
}

@Article{randall89,
  author = 	 {J.H. Randall},
  title = 	 {The Analysis of Sensory Data by Generalised Linear Model},
  journal = 	 {Biometrical journal},
  year = 	 1989,
  volume =	 7,
  pages =	 {781-793}
}


@Book{fahrmeir01,
  author =	 {Ludwig Fahrmeir and Gerhard Tutz},
  title = 	 {Multivariate Statistical Modelling Based on
  Generalized Linear Models},
  publisher = 	 {Springer-Verlag New York, Inc.},
  year = 	 2001,
  series =	 {Springer series in statistics},
  edition =	 {2nd}
}

@Book{greene10,
  author = 	 {William H Greene and David A Hensher},
  title = 	 {Modeling Ordered Choices: A Primer},
  publisher = 	 {Cambridge University Press},
  year = 	 2010}


@Book{mccullagh89,
  author =	 {Peter McCullagh and John Nelder},
  title = 	 {Generalized Linear Models},
  publisher = 	 {Chapman \& Hall/CRC},
  year = 	 1989,
  edition =	 {Second}
}
@Book{collett02,
  author =	 {David Collett},
  title = 	 {Modelling binary data},
  publisher = 	 {London: Chapman \& Hall/CRC},
  year = 	 2002,
  edition =	 {2nd}
}