Bayesian Analysis of DSGE Models—Rejoinder

We would like to thank all the discussants for their stimulating comments. While our article to a large extent reviews current practice of Bayesian analysis of Dynamic Stochastic General Equilibrium (DSGE) models the discussants provide many ideas to improve upon the current practice, thereby outlining a research agenda for the years to come. In our rejoinder we will briefly revisit some of the issues that were raised.     

Rejoinder to discussants of “sentencing convicted felons in the United States: a Bayesian analysis using multilevel covariates”

We thank the discussants for their contributions. We think that they have greatly enhanced the value of our article by providing additional insights into our study and raising challenging questions about the choices we made during our analysis and alternative paths we could have taken. Our thanks also go to the executive editor and to the coordinating editor for organizing this discussion.Space does not allow us to fully respond to all of the points raised by the discussants, so we restrict our comments to issues of disagreement or where elaboration would seem to be useful. Since many of the issues raised by the discussants overlap, we organize our rejoinder by theme rather than by discussant.     

Bayesian Analysis of DSGE Models—Rejoinder

We would like to thank all the discussants for their stimulating comments. While our article to a large extent reviews current practice of Bayesian analysis of Dynamic Stochastic General Equilibrium (DSGE) models the discussants provide many ideas to improve upon the current practice, thereby outlining a research agenda for the years to come. In our rejoinder we will briefly revisit some of the issues that were raised.     

Discussion of “Statistical disease mapping for heterogeneous neuroimaging studies”

This is a discussion of Liu & Zhu (2021), which develops a novel statistical disease mapping framework for neuroimaging data analysis.     

Statistical disease mapping for heterogeneous neuroimaging studies

Many cancers and neuro‐related diseases display significant phenotypic and genetic heterogeneity across subjects and subpopulations. Characterizing such heterogeneity could transform our understanding of the etiology of these conditions and inspire new approaches to urgently needed prevention, diagnosis, treatment, and prognosis. However, most existing statistical methods face major challenges in delineating such heterogeneity at both the group and individual levels. The aim of this article is to propose a novel statistical disease‐mapping (SDM) framework to address some of these challenges. We develop an efficient estimation method to estimate unknown parameters in SDM and delineate individual and group disease maps. Statistical inference procedures such as hypothesis‐testing problems are also investigated for parameters of interest. Both simulation studies and real data analysis on the ADNI hippocampal surface dataset show that our SDM not only effectively detects diseased regions in each patient but also provides a group disease‐mapping analysis of Alzheimer subgroups.     

Authors’ response

In this rejoinder, we address all or almost all comments that have been provided by the discussants. In particular, we include additional references to literature related to change-point detection as well as to intrusion detection; analyze distributions of stopping times of the CUSUM and Shiryaev–Roberts detection procedures under the no change hypothesis in more detail; and provide an overview of the detection-isolation problem.     

“Weak” stochastic orderings and dependence measures

Many notions of dependence rely upon orderings of random pairs. These orderings are generally partial orders, and thus there are many pairs of random vectors which are not comparable. By using a weakened version of stochastic dominance, many new orderings, as well as corresponding dependence measures, are created. The application to stock market data is explored.     

A test for detecting etiologic heterogeneity in epidemiological studies

Current statistical methods for analyzing epidemiological data with disease subtype information allow us to acquire knowledge not only for risk factor-disease subtype association but also, on a more profound account, heterogeneity in these associations by multiple disease characteristics (so-called etiologic heterogeneity of the disease). Current interest, particularly in cancer epidemiology, lies in obtaining a valid p-value for testing the hypothesis whether a particular cancer is etiologically heterogeneous. We consider the two-stage logistic regression model along with pseudo-conditional likelihood estimation method and design a testing strategy based on Rao's score test. An extensive Monte Carlo simulation study is carried out, false discovery rate and statistical power of the suggested test are investigated. Simulation results indicate that applying the proposed testing strategy, even a small degree of true etiologic heterogeneity can be recovered with a large statistical power from the sampled data. The strategy is then applied on a breast cancer data set to illustrate its use in practice where there are multiple risk factors and multiple disease characteristics of simultaneous concern.     

ESTIMATING THE FALSE NEGATIVE FRACTION FOR A MULTIPLE SCREENING TEST FOR BOWEL CANCER WHEN NEGATIVES ARE NOT VERIFIED

This paper aims to estimate the false negative fraction of a multiple screening test for bowel cancer, where those who give negative results for six consecutive tests do not have their true disease status verified. A subset of these same individuals is given a further screening test, for the sole purpose of evaluating the accuracy of the primary test. This paper proposes a beta heterogeneity model for the probability of a diseased individual ‘testing positive’ on any single test, and it examines the consequences of this model for inference on the false negative fraction. The method can be generalized to the case where selection for further testing is informative, though this did not appear to be the case for the bowel‐cancer data.     

“Shelf-Ready” Print Serials Acquisitions

The focus of this installment of “The Balance Point” is “shelf-ready” print serials acquisitions, including functions associated with traditional consolidation services (ordering, receiving, check-in, labeling, claiming and batch shipments), and the newer capabilities of uploading check-in data automatically into library systems. Featured authors discuss traditional vendor consolidation services, pilot projects and experiments in the pursuit of what they define as genuinely “shelf-ready” periodicals. The authors view obtaining “shelf-ready” print journal issues as an effective and efficient means of managing print serials operations while coping with the demands of managing digital resource acquisitions with limited financial resources.     

Regression modelling of weighted κ by using generalized estimating equations

In many clinical studies more than one observer may be rating a characteristic measured on an ordinal scale. For example, a study may involve a group of physicians rating a feature seen on a pathology specimen or a computer tomography scan. In clinical studies of this kind, the weighted κ coefficient is a popular measure of agreement for ordinally scaled ratings. Our research stems from a study in which the severity of inflammatory skin disease was rated. The investigators wished to determine and evaluate the strength of agreement between a variable number of observers taking into account patient-specific (age and gender) as well as rater-specific (whether board certified in dermatology) characteristics. This suggested modelling κ as a function of these covariates. We propose the use of generalized estimating equations to estimate the weighted κ coefficient. This approach also accommodates unbalanced data which arise when some subjects are not judged by the same set of observers. Currently an estimate of overall κ for a simple unbalanced data set without covariates involving more than two observers is unavailable. In the inflammatory skin disease study none of the covariates were significantly associated with κ, thus enabling the calculation of an overall weighted κ for this unbalanced data set. In the second motivating example (multiple sclerosis), geographic location was significantly associated with κ. In addition we also compared the results of our method with current methods of testing for heterogeneity of weighted κ coefficients across strata (geographic location) that are available for balanced data sets.     

The “Green” and “Gold” Roads to Open Access: The Case for Mixing and Matching

Recent discussions on Open Access (OA) have tended to treat OA journals and self-archiving as two distinct routes. Some supporters of self-archiving even suggest that it alone can bring about full Open Access to the world's scientific literature. In this paper, it is argued that each route actually corresponds to a phase in the movement toward Open Access; that the mere fact of self-archiving is not enough; that providing some branding ability to the repositories is needed. However, doing so will eventually bring about the creation of overlay (or database) journals. The two roads, therefore, will merge to create a mature OA landscape.     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

Regression-based estimation of the false negative fraction when multiple negatives are unverified

Summary.  The paper describes a method of estimating the false negative fraction of a multiple-screening test when individuals who test negatively on all K tests do not have their true disease status verified. The method proposed makes no explicit assumptions about the underlying heterogeneity of the population or about serial correlation of test results within an individual. Rather, it is based on estimating false negative fractions conditionally on observed diagnostic histories and extrapolating the observed patterns in these empirical frequencies by using logistic regression. The method is illustrated on, and motivated by, data on a multiple-screening test for bowel cancer.     

Goals for Statistical Uses of Administrative Records: The Next 10 Years

United States statistical agencies use data from administrative record systems to develop program statistics, to establish statistical data bases, and to enhance and evaluate census and survey data. Such uses of administrative records are likely to increase as efforts to control costs and respondent burden of statistical programs continue. This review article proposes six goals for enhanced statistical uses of administrative records in the next 10 years and describes elements of an activist strategy to achieve them. The discussants, representing three agencies that make important statistical uses of administrative records, give their reactions to the proposed goals and strategy.     

A Bayesian latent spatial model for mapping the cortical signature of progression to Alzheimer's disease

Prior studies have shown that atrophy in vulnerable cortical regions is associated with an increased risk of progression to clinical dementia. In this work, we utilize the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the relationship between the temporally changing spatial topography of cortical thickness and conversion from mild cognitive impairment to Alzheimer's disease (AD). We develop a novel Bayesian latent spatial model that employs the spatial information underlying the thickness effects across the cortical surface. The proposed method facilitates the development of imaging markers by reliably quantifying and mapping the regional vulnerability to AD progression across the cortical surface. Simulation results showed substantial gains in statistical power and estimation performance by accounting for the spatial structure of the association. Using MRI data from ADNI, we examined the topographic patterns of anatomic regions where cortical thinning is associated with an increased risk of developing AD.     

Reply

Thanks and congratulations to all of the discussants for covering a wide array of important topics. Since space does not permit attention to all of the points that have been raise, this reply will concentrate on trying to dispell confusion on important matters that might remain.     

Comments on “bootstrapping time series models”

We take issue with the main suggestion in Li and Maddala (LiMa) that bootstrapping residuals is always the preferred approach and question some of their guidelines. We show that it can be potentially misleading to mimic the autocovariance structure of residuals, since it can be very different from that of true errors. We emphasize that the residuals are sensitive to model misspecification and generally not a part of the information set. We make constructive suggestions and propose a semiparametric method.     

Change point-cure models with application to estimating the change-point effect of age of diagnosis among prostate cancer patients

Previous research on prostate cancer survival trends in the United States National Cancer Institute's Surveillance Epidemiology and End Results database has indicated a potential change-point in the age of diagnosis of prostate cancer around age 50. Identifying a change-point value in prostate cancer survival and cure could have important policy and health care management implications. Statistical analysis of this data has to address two complicating features: (1) change-point models are not smooth functions and so present computational and theoretical difficulties; and (2) models for prostate cancer survival need to account for the fact that many men diagnosed with prostate cancer can be effectively cured of their disease with early treatment. We develop a cure survival model that allows for change-point effects in covariates to investigate a potential change-point in the age of diagnosis of prostate cancer. Our results do not indicate that age under 50 is associated with increased hazard of death from prostate cancer.