In many situations saddlepoint approximations can replace the Monte Carlo simulation typically used to nd the bootstrap distribution of a statistic. We explain how bootstrap and permutation distributions can be expressed as conditional distributions and how methods for linear programming and for tting generalized linear models can be used to nd saddlepoint approximations to these distributions....

A structural measurement model (Adams, Wilson, & Wu, 1997) consists of an item response theory model for responses conditional on ability and a structural model that describes the distribution of ability in the population. As a rule, ability is assumed to be normally distributed in the population. However, there are situations where there is reason to assume that the distribution of ability is ...

The computational complexity of linearchain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise when dealing with morphologically rich languages or joint labelling tasks. We extend here recent proposals to consider variable order CRFs. Using an effective finitestate representation of variable-...

We present a new method for estimating the frontier of a sample. The estimator is based on a local polynomial regression on the powertransformed data. We assume that the exponent of the transformation goes to infinity while the bandwidth goes to zero. We give conditions on these two parameters to obtain almost complete convergence. The asymptotic conditional bias and variance of the estimator a...

Reasoning about ordinary human situations and activities requires the availability of diverse types of knowledge, including expectations about the probable results of actions and the lexical entailments for many predicates. We describe initial work to acquire such a collection of conditional (if–then) knowledge by exploiting presuppositional discourse patterns (such as ones involving ‘but’, ‘ye...

This work describes a Bayesian approach for model selection in Gaussian conditional autoregressive models and Gaussian simultaneous autoregressive models which are commonly used to describe spatial lattice data. The approach is aimed at situations when all competing models have the same mean structure, but differ on some aspects of their covariance structures. The proposed approach uses as sele...

In this paper, we show that the conditional transform with respect to the Gaussian process involving the first variation can be expressed in terms of the conditional transform without the first variation. We then use this result to obtain various integration formulas involving the conditional $diamond$-product and the first variation.

Our experiments investigate the extent to which traders learn from price, differentiating between situations where orders are submitted before versus after price has realized. In simultaneous markets with bids that conditional on neglect information conveyed by hypothetical value of price. sequential is known prior bid submission, react an roughly consistent benchmark theory. The difference’s r...