Survival models have a long history in the biomedical and biostatistical literature, and are enormously popular in the analysis of time-to-event data. Very often these data will be grouped into strata, such as clinical sites, geographic regions, and so on. Such data will often be available over multiple time periods, and for multiple diseases. In this paper, we consider hierarchical spatial process models for multivariate survival data sets which are spatio-temporally arranged.

Owing to the importance of snowfall to water supplies in the western United States, government agencies regularly collect data on snow water equivalent (the amount of water in snow) over this region. Several di#erent measurement systems, of possibly di#erent levels of accuracy and reliability, are in operation: snow courses, snow telemetry, aerial markers, and airborne gamma radiation. Data are available at more than 2000 distinct sites, dating back a variable number of years (in a few cases to 1910). Historically, these data have been used primarily to generate flood forecasts and short-term (intra-annual) predictions of streamflow and water supply. However, they also have potential for addressing the possible e#ects of long-term climate change on snowpack accumulations and seasonal water supplies. We present a Bayesian spatio-temporal analysis of the combined snow water equivalent (SWE) data from all four systems that allows for systematic di#erences in accuracy and reliability. The primary objectives of our analysis are (1) to estimate the long-term temporal trend in SWE over the western U.S. and characterize how this trend varies spatially, with quantifiable estimates of variability, and (2) to investigate whether there are systematic di#erences in the accuracy and reliability of the four measurement systems. We find substantial evidence of a decreasing temporal trend in SWE in the Pacific Northwest and northern Rockies, but no evidence of a trend in the intermountain region and southern Rockies. Our analysis also indicates that some of the systems di#er significantly with respect to their accuracy and reliability.

Catch curves have been used to estimate survival and instantaneous mortality for fish and wildlife populations for many years. In order to better analyze catch curve data from the Apostle Islands population lake trout Salvelinus namaycush in Lake Superior, we develop a Bayesian approach to catch curve analysis. First, the proposed Bayesian approach is illustrated for a single catch curve and then extended to multiple years of data. We also relax the model assumption of a stable age distribution to allow random effects across years. The proposed models are compared with the traditional methods using the focused DIC. There are many potential advantages to the Bayesian approach over the traditional methods such as least squares and maximum likelihood, based on large sample theory. Bayesian estimates are valid for finite samples, and efficient numerical methods can be used to obtain estimates of instantaneous mortality. We conclude that many benefits can be obtained from the Bayesian approach to a single catch curve and to multiple years of data, such as closed-form variance estimates and the ability to both model and estimate the process variation of survival rates.

Real-world phenomena are frequently modelled by Bayesian hierarchical models. The buildingblocks in such models are the distribution of each variable conditional on parent and/or neighbour variables in the graph. The specifications of centre and spread of these conditional distributions may be well-motivated, while the tail specifications are often left to convenience. However, the posterior distribution of a parameter may depend strongly on such arbitrary tail specifications. This is not easily detected in complex models. In this paper we propose a graphical diagnostic which identifies such influential statistical modelling choices at the node level in any chain graph model. Our diagnostic, the local critique plot, examines local conflict between the information coming from the parents and neighbours (local prior) and from the children and co-parents (lifted likelihood). It identifies properties of the local prior and the lifted likelihood that are influential on the posterior density. We illustrate the use of the local critique plot with applications involving models of different levels of complexity. The local critique plot can be derived for all parameters in a chain graph model, and is easy to implement using the output of posterior sampling. 1

This paper provides a brief review of the more popular methods for comparing models in a Bayesian framework. Personal experience in implementing these methods in problems requiring mixture models is also referenced. 1

doi:10.1017/S0003055409990049 Does law exhibit a significant constraint on Supreme Court justices ’ decisions? Although proponents of the attitudinal model argue that ideology predominantly influences justices ’ choices, “hybrid models ” posit that law and ideology exhibit discrete and concurrent effects on justices’ choices. I offer a new conceptualization of legal constraint examining how legal rules permit varying degrees of ideological discretion, which establishes how strongly ideological preferences will influence justices ’ votes. In examining the levels-of-scrutiny legal doctrine, I posit theoretical models highlighting the differential constraining capacities of the strict scrutiny, intermediate scrutiny, and rational basis rules. I use a multilevel modeling framework to test the hypotheses within the context of the Grayned doctrine in free expression law. The results show that strict scrutiny, which Grayned applied to contentbased regulations of expression, significantly constrains ideological voting, whereas intermediate scrutiny (applied to content-neutral regulations) and the low scrutiny categories each promote high levels of ideological voting. In politics and government, institutions—both rules and norms—operate as important constraints that structure the decision-making processes of actors. Legislators, judges, bureaucrats, voters, and other actors make decisions within an institutional context defined by formal and informal rules that constrain individual discretion and ultimately shape actors ’ choices

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).

Abstract not found

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).