Graphics for uncertainty

Graphical methods such as colour shading and animation, which are widely available, can be very effective in communicating uncertainty. In particular, the idea of a ‘density strip’ provides a conceptually simple representation of a distribution and this is explored in a variety of settings, including a comparison of means, regression and models for contingency tables. Animation is also a very useful device for exploring uncertainty and this is explored particularly in the context of flexible models, expressed in curves and surfaces whose structure is of particular interest. Animation can further provide a helpful mechanism for exploring data in several dimensions. This is explored in the simple but very important setting of spatiotemporal data

An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm

Extending the Lee Carter Model: a Three-way Decomposition

In this paper, we focus on a Multidimensional Data Analysis approach to the Lee-Carter (LC) model of mortality trends . In particular, we extend the bilinear LC model and specify a new model based on a three-way structure, which incorporates a further component in the decomposition of the log-mortality rates. A multi-way component analysis is performed using the Tucker 3 model. The suggested methodology allows us to obtain combined estimates for the three modes: i) time, ii) agegroups and iii) different populations. From the results obtained by the Tucker 3 decomposition, we can jointly compare, in both a numerical and graphical way, the relationships among all three modes and obtain a time series component as a leading indicator of the mortality trend for a group of populations. Further, we carry out a correlation analysis of the estimated trends in order to assess the reliability of the results of the three-way decomposition. The model’s goodness of fit is assessed using an analysis of the residuals. Finally, we discuss how the synthesised mortality index can be used to build concise projected life tables for a group of populations. An application which compares ten European countries is used to illustrate the approach and provide a deeper insight into the model and its implementation

Further Investigation of the Time Delay, Magnification Ratios, and Variability in the Gravitational Lens 0218+357

High precision VLA flux density measurements for the lensed images of 0218+357 yield a time delay of 10.1(+1.5-1.6)days (95% confidence). This is consistent with independent measurements carried out at the same epoch (Biggs et al. 1999), lending confidence in the robustness of the time delay measurement. However, since both measurements make use of the same features in the light curves, it is possible that the effects of unmodelled processes, such as scintillation or microlensing, are biasing both time delay measurements in the same way. Our time delay estimates result in confidence intervals that are somewhat larger than those of Biggs et al., probably because we adopt a more general model of the source variability, allowing for constant and variable components. When considered in relation to the lens mass model of Biggs et al., our best-fit time delay implies a Hubble constant of H_o = 71(+17-23) km/s-Mpc for Omega_o=1 and lambda_o=0 (95% confidence; filled beam). This confidence interval for H_o does not reflect systematic error, which may be substantial, due to uncertainty in the position of the lens galaxy. We also measure the flux ratio of the variable components of 0218+357, a measurement of a small region that should more closely represent the true lens magnification ratio. We find ratios of 3.2(+0.3-0.4) (95% confidence; 8 GHz) and 4.3(+0.5-0.8) (15 GHz). Unlike the reported flux ratios on scales of 0.1", these ratios are not strongly significantly different. We investigate the significance of apparent differences in the variability properties of the two images of the background active galactic nucleus. We conclude that the differences are not significant, and that time series much longer than our 100-day time series will be required to investigate propagation effects in this way.Comment: 33 pages, 9 figures. Accepted for publication in ApJ. Light curve data may be found at http://space.mit.edu/RADIO/papers.htm

Currency Unions and Trade: A PPML Re-Assessment with High-Dimensional Fixed Effects

Recent work on the effects of currency unions (CUs) on trade stresses the importance of using many countries and years in order to obtain reliable estimates. However, for large samples, computational issues associated with the three-way (exporter-time, importer-time, and country-pair) fixed effects currently recommended in the gravity literature have heretofore limited the choice of estimator, leaving an important methodological gap. To address this gap, we introduce an iterative Poisson Pseudo-Maximum Likelihood (PPML) estimation procedure that facilitates the inclusion of these fixed effects for large data sets and also allows for correlated errors across countries and time. When applied to a comprehensive sample with more than 200 countries trading over 65 years, these innovations flip the conclusions of an otherwise rigorously-specified linear model. Most importantly, our estimates for both the overall CU effect and the Euro effect specifically are economically small and statistically insignificant. We also document that linear and PPML estimates of the Euro effect increasingly diverge as the sample size grows

Finite-Size Scaling in the Energy-Entropy Plane for the 2D +- J Ising Spin Glass

For $L \times L$ square lattices with $L \le 20$ the 2D Ising spin glass with +1 and -1 bonds is found to have a strong correlation between the energy and the entropy of its ground states. A fit to the data gives the result that each additional broken bond in the ground state of a particular sample of random bonds increases the ground state degeneracy by approximately a factor of 10/3. For $x = 0.5$ (where $x$ is the fraction of negative bonds), over this range of $L$, the characteristic entropy defined by the energy-entropy correlation scales with size as $L^{1.78(2)}$. Anomalous scaling is not found for the characteristic energy, which essentially scales as $L^2$. When $x= 0.25$, a crossover to $L^2$ scaling of the entropy is seen near $L = 12$. The results found here suggest a natural mechanism for the unusual behavior of the low temperature specific heat of this model, and illustrate the dangers of extrapolating from small $L$.Comment: 9 pages, two-column format; to appear in J. Statistical Physic

Evaluation of Methods for Estimating Time to Steady State with Examples from Phase 1 Studies

An overview is provided of the methodologies used in determining the time to steady state for Phase 1 multiple dose studies. These methods include NOSTASOT (no-statistical-significance-of-trend), Helmert contrasts, spline (quadratic) regression, effective half life for accumulation, nonlinear mixed effects modeling, and Bayesian approach using Markov Chain Monte Carlo (MCMC) methods. For each methodology we describe its advantages and disadvantages. The first two methods do not require any distributional assumptions for the pharmacokinetic (PK) parameters and are limited to average assessment of steady state. Also spline regression which provides both average and individual assessment of time to steady state does not require any distributional assumptions for the PK parameters. On the other hand, nonlinear mixed effects modeling and Bayesian hierarchical modeling which allow for the estimation of both population and subject-specific estimates of time to steady state do require distributional assumptions on PK parameters. The current investigation presents eight case studies for which the time to steady state was assessed using the above mentioned methodologies. The time to steady state estimates obtained from nonlinear mixed effects modeling, Bayesian hierarchal approach, effective half life, and spline regression were generally similar

How does firm innovativeness enable supply chain resilience?:The moderating role of supply uncertainty and interdependence

Despite its potential benefits in a wide range of circumstances, firm innovativeness received scant attention in relation to managing the various risks and uncertainties in the global business environment. Likewise, there is still a limited understanding of firms’ supply chain resilience (SCR) and its related antecedents in the strategic management literature. This research focuses on exploring the relationship between firm innovativeness and SCR in an attempt to facilitate bridging the gap between two important research streams and shed some light on the contingent value of firm innovativeness against disruptions and adversities. The moderating role of supply uncertainty and interdependence in the focal relationship was also hypothesised and tested. Findings suggest that firm innovativeness is positively associated with firm SCR, and supply uncertainty negatively moderates this relationship but interdependence does not. We argue that this could be due to the dual nature of interdependence in supply networks