Closed-Form Bayesian Inferences for the Logit Model via Polynomial Expansions

Articles in Marketing and choice literatures have demonstrated the need for incorporating person-level heterogeneity into behavioral models (e.g., logit models for multiple binary outcomes as studied here). However, the logit likelihood extended with a population distribution of heterogeneity doesn't yield closed-form inferences, and therefore numerical integration techniques are relied upon (e.g., MCMC methods). We present here an alternative, closed-form Bayesian inferences for the logit model, which we obtain by approximating the logit likelihood via a polynomial expansion, and then positing a distribution of heterogeneity from a flexible family that is now conjugate and integrable. For problems where the response coefficients are independent, choosing the Gamma distribution leads to rapidly convergent closed-form expansions; if there are correlations among the coefficients one can still obtain rapidly convergent closed-form expansions by positing a distribution of heterogeneity from a Multivariate Gamma distribution. The solution then comes from the moment generating function of the Multivariate Gamma distribution or in general from the multivariate heterogeneity distribution assumed. Closed-form Bayesian inferences, derivatives (useful for elasticity calculations), population distribution parameter estimates (useful for summarization) and starting values (useful for complicated algorithms) are hence directly available. Two simulation studies demonstrate the efficacy of our approach.Comment: 30 pages, 2 figures, corrected some typos. Appears in Quantitative Marketing and Economics vol 4 (2006), no. 2, 173--20

Reproducibility of the computational fluid dynamic analysis of a cerebral aneurysm monitored over a decade

Computational fluid dynamics (CFD) simulations are increasingly utilised to evaluate intracranial aneurysm (IA) haemodynamics to aid in the prediction of morphological changes and rupture risk. However, these models vary and differences in published results warrant the investigation of IA-CFD reproducibility. This study aims to explore sources of intra-team variability and determine its impact on the aneurysm morphology and CFD parameters. A team of four operators were given six sets of magnetic resonance angiography data spanning a decade from one patient with a middle cerebral aneurysm. All operators were given the same protocol and software for model reconstruction and numerical analysis. The morphology and haemodynamics of the operator models were then compared. The segmentation, smoothing factor, inlet and outflow branch lengths were found to cause intra-team variability. There was 80% reproducibility in the time-averaged wall shear stress distribution among operators with the major difference attributed to the level of smoothing. Based on these findings, it was concluded that the clinical applicability of CFD simulations may be feasible if a standardised segmentation protocol is developed. Moreover, when analysing the aneurysm shape change over a decade, it was noted that the co-existence of positive and negative values of the wall shear stress divergence (WSSD) contributed to the growth of a daughter sac

Choice Models in Marketing: Economic Assumptions, Challenges and Trends

Direct utility models of consumer choice are reviewed and developed for understanding consumer preferences. We begin with a review of statistical models of choice, posing a series of modeling challenges that are resolved by considering economic foundations based on con-strained utility maximization. Direct utility models differ from other choice models by directly modeling the consumer utility function used to derive the likelihood of the data through Kuhn-Tucker con-ditions. Recent advances in Bayesian estimation make the estimation of these models computationally feasible, offering advantages in model interpretation over models based on indirect utility, and descriptive models that tend to be highly parameterized. Future trends are dis-cussed in terms of the antecedents and enhancements of utility function specification.

Emotional distress in cancer patients: the Edinburgh Cancer Centre symptom study

To: (1) estimate the prevalence of clinically significant emotional distress in patients attending a cancer outpatient department and (2) determine the associations between distress and demographic and clinical variables, we conducted a survey of outpatients attending selected clinics of a regional cancer centre in Edinburgh, UK. Patients completed the Hospital Anxiety and Depression Scale (HADS) on touch-screen computers and the scores were linked to clinical variables on the hospital database. Nearly one quarter of the cancer outpatients 674 out of 3071 (22%; 95% confidence interval (CI) 20–23%) met our criterion for clinically significant emotional distress (total HADS score 15 or more). Univariate analysis identified the following statistically significant associations: age <65, female gender, cancer type and extent of disease. Multivariate analysis indicated that age <65 (odds ratio 1.41; 95% CI 1.18–1.69), female gender (odds ratio 1.58; 95% CI 1.31–1.92) and active disease (odds ratio 1.72; 95% CI 1.43–2.05) but not cancer diagnosis, were the independent predictors of clinically significant emotional distress. Services to treat distress in cancer patients should be organised to target patients by characteristics other than their cancer diagnosis

Prospectively predicting Pseudomonas aeruginosa infection/s using routine data from the UK cystic fibrosis register

Rationale and aims Lung health of people with cystic fibrosis (PwCF) can be preserved by daily use of inhaled therapy. Adherence to inhaled therapy, therefore, provides an important process measure to understand the success of care and can be used as a quality indicator. Defining adherence is problematic, however, since the number of prescribed treatments varies considerably between PwCF. The problem is less pronounced among those with Pseudomonas aeruginosa (PA), for whom at least three daily doses of nebulized therapy should be prescribed and who thus constitute a more homogeneous group. The UK CF Registry provides routine data on PA status, but data are only available 12 months after collection. In this study, we aim to prospectively identify contemporary PA status from historic registry data. Method UK CF Registry data from 2011 to 2015 for PwCF aged ≥16 was used to determine a pragmatic prediction rule for identifying contemporary PA status using historic registry data. Accuracy of three different prediction rules was assessed using the positive predictive value (PPV). The number and proportion of adults predicted to have PA infection were determined overall and per center for the selected prediction rule. Known characteristics linked to PA status were explored to ensure the robustness of the prediction rule. Results Having CF Registry defined chronic PA status in the two previous years is the selected definition to predict a patient will have PA infection within the current year (population-level PPV = 96%-97%, centre level PPV = 85%-100%). This approach provides a subset of data between 1852 and 1872 patients overall and a range of 8 to 279 patients per center. Conclusion Historic registry data can be used to contemporaneously identify a subgroup of patients with chronic PA. Since this patient group has a narrower treatment schedule, this can facilitate a better benchmarking of adherence across centers

Filtering and Tracking with Trinion-Valued Adaptive Algorithms

A new model for three-dimensional processes based on the trinion algebra is introduced for the first time. Compared with the pure quaternion model, the trinion model is more compact and computationally more efficient, while having similar or comparable performance in terms of adaptive linear filtering. Moreover, the trinion model can effectively represent the general relationship of state evolution in Kalman filtering, where the pure quaternion model fails. Simulations on real-world wind recordings and synthetic data sets are provided to demonstrate the potentials of this new modeling method

Interdependent Infrastructure as Linked Social, Ecological, and Technological Systems (SETSs) to Address Lock‐in and Enhance Resilience

Traditional infrastructure adaptation to extreme weather events (and now climate change) has typically been techno‐centric and heavily grounded in robustness—the capacity to prevent or minimize disruptions via a risk‐based approach that emphasizes control, armoring, and strengthening (e.g., raising the height of levees). However, climate and nonclimate challenges facing infrastructure are not purely technological. Ecological and social systems also warrant consideration to manage issues of overconfidence, inflexibility, interdependence, and resource utilization—among others. As a result, techno‐centric adaptation strategies can result in unwanted tradeoffs, unintended consequences, and underaddressed vulnerabilities. Techno‐centric strategies that lock‐in today\u27s infrastructure systems to vulnerable future design, management, and regulatory practices may be particularly problematic by exacerbating these ecological and social issues rather than ameliorating them. Given these challenges, we develop a conceptual model and infrastructure adaptation case studies to argue the following: (1) infrastructure systems are not simply technological and should be understood as complex and interconnected social, ecological, and technological systems (SETSs); (2) infrastructure challenges, like lock‐in, stem from SETS interactions that are often overlooked and underappreciated; (3) framing infrastructure with a SETS lens can help identify and prevent maladaptive issues like lock‐in; and (4) a SETS lens can also highlight effective infrastructure adaptation strategies that may not traditionally be considered. Ultimately, we find that treating infrastructure as SETS shows promise for increasing the adaptive capacity of infrastructure systems by highlighting how lock‐in and vulnerabilities evolve and how multidisciplinary strategies can be deployed to address these challenges by broadening the options for adaptation