Bayesian analysis of dynamic magnetic resonance breast images

Summary.  We describe an integrated methodology for analysing dynamic magnetic resonance images of the breast. The problems that motivate this methodology arise from a collaborative study with a tumour institute. The methods are developed within the Bayesian framework and comprise image restoration and classification steps. Two different approaches are proposed for the restoration. Bayesian inference is performed by means of Markov chain Monte Carlo algorithms. We make use of a Metropolis algorithm with a specially chosen proposal distribution that performs better than more commonly used proposals. The classification step is based on a few attribute images yielded by the restoration step that describe the essential features of the contrast agent variation over time. Procedures for hyperparameter estimation are provided, so making our method automatic. The results show the potential of the methodology to extract useful information from acquired dynamic magnetic resonance imaging data about tumour morphology and internal pathophysiological features.     

Gaussian proposal density using moment matching in SMC methods

In this article we introduce a new Gaussian proposal distribution to be used in conjunction with the sequential Monte Carlo (SMC) method for solving non-linear filtering problems. The proposal, in line with the recent trend, incorporates the current observation. The introduced proposal is characterized by the exact moments obtained from the dynamical system. This is in contrast with recent works where the moments are approximated either numerically or by linearizing the observation model. We show further that the newly introduced proposal performs better than other similar proposal functions which also incorporate both state and observations. This work was supported by a research grant from THALES Nederland BV.     

Statistical science and effective scientific communication*

Modern science increasingly uses sophisticated statistical techniques, yet papers in scientific journals are often defective in identification of methods used and in clear presentation of quantitative conclusions. The omission of essential information, ambiguities and misleading conclusions from ill-chosen methods often escape editorial challenge: referees, usually chosen for expertise in the substantive subject matter, may lack the statistical skill and experience needed for critical assessment. This paper makes proposals that might enable an editor and his referees more readily to detect the need for major revision before publication. Some matters have ethical components in addition to their implications for the advancement of knowledge in many disciplines.     

Configural Frequency Analysis: the Search for Extreme Cells

Configural Frequency Analysis (CFA) asks whether a cell in a cross-classification contains more or fewer cases than expected with respect to some base model. This base model is specified such that cells with more cases than expected (also called types) can be interpreted from a substantive perspective. The same applies to cells with fewer cases than expected (antitypes). This article gives an introduction to both frequentist and Bayesian approaches to CFA. Specification of base models, testing, and protection are discussed. In an example, Prediction CFA and two-sample CFA are illustrated. The discussion focuses on the differences between CFA and modelling.     

FDR- and FWE-controlling methods using data-driven weights

Weighted methods are an important feature of multiplicity control methods. The weights must usually be chosen a priori, on the basis of experimental hypotheses. Under some conditions, however, they can be chosen making use of information from the data (therefore a posteriori) while maintaining multiplicity control. In this paper we provide: (1) a review of weighted methods for familywise type I error rate (FWE) (both parametric and nonparametric) and false discovery rate (FDR) control; (2) a review of data-driven weighted methods for FWE control; (3) a new proposal for weighted FDR control (data-driven weights) under independence among variables; (4) under any type of dependence; (5) a simulation study that assesses the performance of procedure of point 4 under various conditions.     

Markov Chain Monte Carlo in small worlds

As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get trapped within one mode of a multi-modal space. Moreover, it may be difficult to determine if a chain is only sampling a certain area of the space or if it has indeed reached stationarity. In this paper, we show how a simple modification of the proposal mechanism results in faster convergence of the chain and helps to circumvent the problems described above. This mechanism, which is based on an idea from the field of “small-world” networks, amounts to adding occasional “wild” proposals to any local proposal scheme. We demonstrate through both theory and extensive simulations, that these new proposal distributions can greatly outperform the traditional local proposals when it comes to exploring complex heterogenous spaces and multi-modal distributions. Our method can easily be applied to most, if not all, problems involving MCMC and unlike many other remedies which improve the performance of MCMC it preserves the simplicity of the underlying algorithm.     

Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling

The Multiple-Try Metropolis is a recent extension of the Metropolis algorithm in which the next state of the chain is selected among a set of proposals. We propose a modification of the Multiple-Try Metropolis algorithm which allows for the use of correlated proposals, particularly antithetic and stratified proposals. The method is particularly useful for random walk Metropolis in high dimensional spaces and can be used easily when the proposal distribution is Gaussian. We explore the use of quasi Monte Carlo (QMC) methods to generate highly stratified samples. A series of examples is presented to evaluate the potential of the method.     

SCOAP3 and Open Access

SCOAP3 is an innovative Open Access initiative for publishing in high-energy physics. The model is viewed by many as a potential solution to multiple issues related to the financial crisis, the peer review system, scholarly communication, and the need to support institutional repositories. This installment of “The Balance Point” presents articles written by three Open Access advocates, outlining the SCOAP3 proposal, benefits of participation, and some of the roles libraries, publishers and scientists can play in making important changes to scholarly communication. Contributors discuss scalability and transferability issues of SCOAP3, as well as other matters of concern.     

Improving the contributions of technology assessment to the health care system of the U.S.A.

Summary Based on 14 case studies of highly effective therapies and the reasons they succeeded less frequently than they could, we propose a variety of steps to improve the health care system of the U.S.A. Whatever proposal emerges from current national debates until innovations are shown to be safe and effective, they should not be supported; when slightly better technologies are much more expensive than other good ones we need to consider appropriate choices carefully; simplified billing and bookkeping would reduce our costs; when a technology is rapidly introduced cautionnary measures may be needed; tracking immunization and repairing their omissions requires a new system; educational programs such as seen effective in hypertension should be applied in other areas such as vaccination; in organ transplantation the nation should consider “presumed consent”; our payment system sometimes creates perverse incentives and therefore needs review; and the preferences of the public in allocation of health resources need to be discovered once the public is informed about the issues. Research supported by Andrew W. Mellon Foundation.     

SCOAP3 and Open Access

Abstract

SCOAP3 is an innovative Open Access initiative for publishing in high-energy physics. The model is viewed by many as a potential solution to multiple issues related to the financial crisis, the peer review system, scholarly communication, and the need to support institutional repositories. This installment of “The Balance Point” presents articles written by three Open Access advocates, outlining the SCOAP3 proposal, benefits of participation, and some of the roles libraries, publishers and scientists can play in making important changes to scholarly communication. Contributors discuss scalability and transferability issues of SCOAP3, as well as other matters of concern.     

On the use of local optimizations within Metropolis–Hastings updates

Summary.  We propose new Metropolis–Hastings algorithms for sampling from multimodal dis- tributions on ℜ ⁿ . Tjelmeland and Hegstad have obtained direct mode jumping proposals by optimization within Metropolis–Hastings updates and different proposals for 'forward' and 'backward' steps. We generalize their scheme by allowing the probability distribution for forward and backward kernels to depend on the current state. We use the new setting to combine mode jumping proposals and proposals from a prior approximation. We obtain that the frequency of proposals from the different proposal kernels is automatically adjusted to their quality. Mode jumping proposals include local optimizations. When combining this with a prior approximation it is tempting to use local optimization results not only for mode jumping proposals but also to improve the prior approximation. We show how this idea can be implemented. The resulting algorithm is adaptive but has a Markov structure. We evaluate the effectiveness of the proposed algorithms in two simulation examples.     

Interacting multiple try algorithms with different proposal distributions

We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed to increase the efficiency of a modified multiple-try Metropolis (MTM) sampler. The extension with respect to the existing MCMC literature is twofold. First, the sampler proposed extends the basic MTM algorithm by allowing for different proposal distributions in the multiple-try generation step. Second, we exploit the different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. The numerical studies show that the interaction mechanism allows the IMTM to efficiently explore the state space leading to higher efficiency than other competing algorithms.     

Open Access: A Review of an Emerging Phenomenon

Discussion about Open Access (OA) has dominated industry news for the past two years. Librarians and publishers alike are attempting to fully grasp the implications of different business models on various issues, including costs, peer review, funding mechanisms, value, and archives. While there is general agreement about the importance of broadening access to scientific literature, there is disagreement on how this is best achieved in a financially responsible fashion. This article looks at some of the questions surrounding Open Access journals as well as the role publishing plays in the continuum of science in general, particularly with regard to membership organizations.     

Open Access: A Review of an Emerging Phenomenon

Abstract

Discussion about Open Access (OA) has dominated industry news for the past two years. Librarians and publishers alike are attempting to fully grasp the implications of different business models on various issues, including costs, peer review, funding mechanisms, value, and archives. While there is general agreement about the importance of broadening access to scientific literature, there is disagreement on how this is best achieved in a financially responsible fashion. This article looks at some of the questions surrounding Open Access journals as well as the role publishing plays in the continuum of science in general, particularly with regard to membership organizations.     

Multivariate regression analysis of panel data with binary outcomes applied to unemployment data

Summary  In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11 waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution (see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification which fit the data well. C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada.     

Sequential Monte Carlo on large binary sampling spaces

A Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current proposal distribution using past simulations. The choice of the parametric family that defines the set of proposal distributions is critical for good performance. In this paper, we present such a parametric family for adaptive sampling on high dimensional binary spaces. A practical motivation for this problem is variable selection in a linear regression context. We want to sample from a Bayesian posterior distribution on the model space using an appropriate version of Sequential Monte Carlo. Raw versions of Sequential Monte Carlo are easily implemented using binary vectors with independent components. For high dimensional problems, however, these simple proposals do not yield satisfactory results. The key to an efficient adaptive algorithm are binary parametric families which take correlations into account, analogously to the multivariate normal distribution on continuous spaces. We provide a review of models for binary data and make one of them work in the context of Sequential Monte Carlo sampling. Computational studies on real life data with about a hundred covariates suggest that, on difficult instances, our Sequential Monte Carlo approach clearly outperforms standard techniques based on Markov chain exploration.     

A question of identity

The weight to be attached to DNA profile evidence was the centre of a huge scientific controversy in the early-mid-1990s, with scientists, including statistical scientists, criticising each other over both science and conduct in the editorials of prestigious journals, on television and radio, and in newspapers. Today, only the occasional shot rings out on the DNA evidence front. David Balding looks back and reflects on the causes of the dispute, its evolution and resolution, and the role in it of statistics and statisticians.     

Minimum variance unbiased estimation based on bootstrap iterations

Practical computation of the minimum variance unbiased estimator (MVUE) is often a difficult, if not impossible, task, even though general theory assures its existence under regularity conditions. We propose a new approach based on iterative bootstrap bias correction of the maximum likelihood estimator to accurately approximate the MVUE. Viewing bootstrap iteration as a Markov process, we develop a computational algorithm for bias correction based on arbitrarily many bootstrap iterations. The algorithm, when applied parametrically to finite sample spaces, does not involve Monte Carlo simulation. For infinite sample spaces, a nonparametric version of the algorithm is combined with a preliminary round of Monte Carlo simulation to yield an approximate MVUE. Both algorithms are computationally more efficient and stable than conventional simulation-based bootstrap iterations. Examples are given of both finite and infinite sample spaces to illustrate the effectiveness of our new approach. Supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7026/97P).     

Challenges and Opportunities for Statistics and Statistical Education: Looking Back,Looking Forward

The 175th anniversary of the ASA provides an opportunity to look back into the past and peer into the future. What led our forebears to found the association? What commonalities do we still see? What insights might we glean from their experiences and observations? I will use the anniversary as a chance to reflect on where we are now and where we are headed in terms of statistical education amidst the growth of data science. Statistics is the science of learning from data. By fostering more multivariable thinking, building data-related skills, and developing simulation-based problem solving, we can help to ensure that statisticians are fully engaged in data science and the analysis of the abundance of data now available to us.