1,720,977 research outputs found
Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
No full textWe use reversible jump Markov chain Monte Carlo methods (Green, 1995) to develop strategies for calculating posterior probabilities of hierarchical, graphical or decomposable log-linear models for high-dimensional contingency tables. Even for tables of moderate size, these sets of models may be very large. The choice of suitable prior distributions for model parameters is also discussed in detail, and two examples are presented. For the first example, a three-way table, the model probabilities calculated using our reversible jump approach are compared with model probabilities calculated exactly or by using an alternative approximation. The second example is a six-way contingency table for which exact methods are infeasible, because of the large number of possible models. We identify the most probable hierarchical, graphical and decomposable models, and compare the results with alternatives approaches
Sample surveys: nonprobability sampling.
No full textNonprobability sampling describes any method for collecting survey data which does not utilize a full probability sampling design. Nonprobability samples are usually cheaper and easier to collect than probability samples. However, there are a number of drawbacks. Such methods can be prone to selection bias, and standard design-based methods of inference cannot be used to ensure approximately unbiased estimators of population quantities or to provide associated measures of precision. In this article, some of the more common methods of nonprobability sampling, quota sampling in particular, are introduced. Their advantages and disadvantages are discussed, and a formal framework for assessing the validity of inferences from nonprobability samples is described
Bayesian variable and link determination for generalised linear models
No full textIn this paper, we describe full Bayesian inference for generalised linear models where uncertainty exists about the structure of the linear predictor, the linear parameters and the link function. Choice of suitable prior distributions is discussed in detail and we propose an efficient reversible jump Markov chain Monte-Carlo algorithm for calculating posterior summaries. We illustrate our method with two data examples
Specification and interpretation of prior distributions for variable selection in linear models.
No full textResponse to best-frequency tone bursts in the ventral cochlear nucleus is governed by ordered inter-spike interval statistics
No full textThe spike trains generated by short constant-amplitude constant-frequency tone bursts in the ventral cochlear nucleus of the anaesthetised guinea pig are examined. Spikes are grouped according to the order in which they occur following the onset of the stimulus. It is found that successive inter-spike intervals have low statistical dependence according to information-theoretic measures. This is in contrast to previous observations with long-duration tone bursts in the cat dorsal and posteroventral cochlear nuclei and lateral superior olive, where it was found that long intervals tended to be followed by shorter ones and vice versa. The interval distributions can also be reasonably modelled by a shifted Gamma distribution parameterised by the dead-time and the mean and coefficient of variation of the dead-time corrected ISI distribution. Knowledge of those three parameters for each interval is sufficient to determine the peri-stimulus time histogram and the regularity measures used to classify these neurons
On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case‐control study of colorectal cancer
Get PDFPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93566/1/sim5358.pd
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