When statistical observations are not based upon a controlled randomized experiment, it can be appealing to try to model their joint distribution via an exchangeable sampling distribution. However, exchangeable sampling distributions should be used with extreme caution, and do not obviously usefully model any lack of independence of the observation vectors. The two main problems concern the distributions of the test statistics, together with a lack of identifiability of the dependencies between the observation vectors. Two new asymptotic results relating to empirical processes, Dirichlet processes, and non-parametric tests for fit, are described in order to highlight these problems.Uncontrolled data Non-parametric tests of significance Cram...
Dependent nonparametric processes extend distributions over mea-sures, such as the Dirichlet process...
AbstractGiven a process X on Rd or Zd, we may form a random sequence ξ1,ξ2,… by sampling from X at s...
Let us start with a random sample X1, . . . , Xn that is independent and identically distributed and...
Exchangeability of observations corresponds to a condition shared by the vast majority of applicatio...
Paper is devoted to investigating classical normalized empirical process of independence. Processes ...
The Cramer-von Mises statistic provides a useful goodness of fit test of whether a random sample has...
Random probability measures are a cornerstone of Bayesian nonparametrics. By virtue of de Finetti's ...
The output from simulation factorial experiments can be complex and may not be amenable to standard ...
textabstractIn this paper we study stochastic processes which enable monitoring the possible changes...
Let S be the space of real cadlag functions on R with finite limits at 1, equipped with uniform dist...
Observational data are often analysed as if they had resulted from a controlled study, and yet the t...
Recent work on nonparametric identification of average partial effects (APEs) from panel data requir...
This paper investigates how data requirements can induce a non-random selection of observations from...
AbstractLet S be the space of real cadlag functions on R with finite limits at ±∞, equipped with uni...
Statistical hypothesis testing is one of the most powerful and interpretable tools for arriving at r...
Dependent nonparametric processes extend distributions over mea-sures, such as the Dirichlet process...
AbstractGiven a process X on Rd or Zd, we may form a random sequence ξ1,ξ2,… by sampling from X at s...
Let us start with a random sample X1, . . . , Xn that is independent and identically distributed and...
Exchangeability of observations corresponds to a condition shared by the vast majority of applicatio...
Paper is devoted to investigating classical normalized empirical process of independence. Processes ...
The Cramer-von Mises statistic provides a useful goodness of fit test of whether a random sample has...
Random probability measures are a cornerstone of Bayesian nonparametrics. By virtue of de Finetti's ...
The output from simulation factorial experiments can be complex and may not be amenable to standard ...
textabstractIn this paper we study stochastic processes which enable monitoring the possible changes...
Let S be the space of real cadlag functions on R with finite limits at 1, equipped with uniform dist...
Observational data are often analysed as if they had resulted from a controlled study, and yet the t...
Recent work on nonparametric identification of average partial effects (APEs) from panel data requir...
This paper investigates how data requirements can induce a non-random selection of observations from...
AbstractLet S be the space of real cadlag functions on R with finite limits at ±∞, equipped with uni...
Statistical hypothesis testing is one of the most powerful and interpretable tools for arriving at r...
Dependent nonparametric processes extend distributions over mea-sures, such as the Dirichlet process...
AbstractGiven a process X on Rd or Zd, we may form a random sequence ξ1,ξ2,… by sampling from X at s...
Let us start with a random sample X1, . . . , Xn that is independent and identically distributed and...