B. D. Ripley’s research while affiliated with University of Oxford and other places

This article has no abstract.

We introduce a method of estimating disease prevalence from case-control family study data. Case-control family studies are performed to investigate the familial aggregation of disease; families are sampled via either a case or a control proband, and the resulting data contain information on disease status and covariates for the probands and their relatives. Here, we introduce estimators for overall prevalence and for covariate-stratum-specific (e.g., sex-specific) prevalence. These estimators combine the proportion of affected relatives of control probands with the proportion of affected relatives of case probands and are designed to yield approximately unbiased estimates of their population counterparts under certain commonly made assumptions. We also introduce corresponding confidence intervals designed to have good coverage properties even for small prevalences. Next, we describe simulation experiments where our estimators and intervals were applied to case-control family data sampled from fictional populations with various levels of familial aggregation. At all aggregation levels, the resulting estimates varied closely and symmetrically around their population counterparts, and the resulting intervals had good coverage properties, even for small sample sizes. Finally, we discuss the assumptions required for our estimators to be approximately unbiased, highlighting situations where an alternative estimator based only on relatives of control probands may perform better.

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

Likert attitude data consist of responses to favorable and unfavorable statements about an entity, where responses fall into ordered categories ranging from disagreement to agreement. Social science and marketing researchers frequently use data of this type to measure attitudes toward an entity such as a policy or product. We focus on data on American and British attitudes toward their respective nations (”national pride”). We introduce a multidimensional unfolding model (MUM) to describe the relationship between the data and the attitudes underlying them. Unlike most existing models, the MUM allows the data to reflect not just attitudes, but also response style, which is defined as a consistent and content-independent pattern of response category selection such as a tendency to agree with all statements. The MUM can be used to model multiple attitudes, which allows researchers to expand their analysis of the data of interest to include all available Likert data so as to increase information on response style. For example, we include additional data on immigration attitudes to help distinguish the effects of response style and national pride on our data. The MUM can be used to fit linear models for the effects of background variables on attitudes. Resulting inferences about attitudes are adjusted for response style and should be less biased. Simulation results strongly suggest that, unlike Likert’s popular scoring model, the MUM yields unbiased inferences even when there are unequal proportions of favorable and unfavorable statements.

Likert attitude data consist of responses to favorable and unfavorable statements about an entity, where responses fall into ordered categories ranging from disagreement to agreement. Social science and marketing researchers frequently use data of this type to measure attitudes toward an entity such as a policy or product. We focus on data on American and British attitudes toward their respective nations ("national pride"). We introduce a multidimensional unfolding model (MUM) to describe the relationship between the data and the attitudes underlying them. Unlike most existing models, the MUM allows the data to reflect not just attitudes, but also response style, which is defined as a consistent and content-independent pattern of response category selection such as a tendency to agree with all statements. The MUM can be used to model multiple attitudes, which allows researchers to expand their analysis of the data of interest to include all available Likert data so as to increase information on response style. For example, we include additional data on immigration attitudes to help distinguish the effects of response style and national pride on our data. The MUM can be used to fit linear models for the effects of background variables on attitudes. Resulting inferences about attitudes are adjusted for response style and should be less biased. Simulation results strongly suggest that, unlike Likert's popular scoring model, the MUM yields unbiased inferences even when there are unequal proportions of favorable and unfavorable statements.

One sense of “computer-intensive” statistics is just statistical methodology that makes use of a large amount of computer time. (Examples include the bootstrap, jackknife, smoothing, image analysis, and many uses of the EM algorithm.) However, the term is usually used for methods that go beyond the minimum of calculations needed for an illuminating analysis, for example, by replacing analytic approximations by computational ones, or requiring numeric optimization or integration over high-dimensional spaces. We introduce the subject with a very simple yet useful example, and then consider some of the areas in which computer-intensive methods are used, to give a flavor of current research. Keywords: boostrap; jacknife; image analysis; optimization; Monte Carlo