Journal of Data Science

Abstract: This paper uses a structural time series methodology to test the notion of interconnectedness between the UK and the US credit markets. The empirical tests utilise data on premium for the Banking sector credit default swaps (CDS) and covers the recent period of financial turmoil. The methodology based on Kalman filter is robust in the presence of limited convergence. The long-term steady state convergence in CDS premium is clearly noticeable between these two markets from the results. This observation lends support for the coordinated regulatory policy initiatives to deal with the crisis and offer suggestions for sound operations of the international financial systems.

Abstract: In multivariate regression, interest lies on how the response vector depends on a set of covariates. A multivariate regression model is proposed where the covariates explain variation in the response only in the direction of the first principal component axis. This model is not only parsimonious, but it provides an easy interpretation in allometric growth studies where the first principal component of the log-transformed data corresponds to constants of allometric growth. The proposed model naturally generalizes the two–group allometric extension model to the situation where groups differ according to a set of covariates. A bootstrap test for the model is proposed and a study on plant growth in the Florida Everglades is used to illustrate the model.

Abstract: The use of contingency tables is widespread in archaeology. Cross tabulations are used in many different studies as a useful tool to syntheti cally report data, and are also useful when analyst wishes to seek for latent data structures. The latter case is when Correspondence Analysis (CA) comes into play. By graphically displaying the dependence between rows and columns, CA enables the analyst to explore the data in search of a meaningful inner structure. The article aims to show the utility of CA in archaeology in general and, in particular, for the identification of areas de voted to different activities within settlements. The application of CA to the data from a prehistoric village in north-eastern Sicily (P. Milazzese at Panarea, Aeolian Islands-Italy), taken as a case study, allows to show how CA succeeds in pinpointing different activity areas and in providing grounds to open new avenues of inquiry into other aspects of the archaeological doc umentation.

Abstract: Many nations’ defence departments use capabilitybased planning to guide their investment and divestment decisions. This planning process involves a variety of data that in its raw form is difficult for decisionmakers to use. In this paper we describe how dimensionality reduction and partition clustering are used in the Canadian Armed Forces to create visualizations that convey how important military capabilities are in planning scenarios and how much capacity the planned force structure has to provide the capabilities. Together, these visualizations give decisionmakers an overview of which capabilities may require investment or may be candidates for divestment.

This paper empirically investigates the impact of the government bailout on analysts’ forecast optimism regardingfirms in the automotive industry. We compare the results from M- and MM-robust methodologies to the results from OLS regression in an event study context and find that inferences change. When M- and MM-robust estimation methods are used to estimate the same model, the results for key control variables fall directly in line with those of similar previous studies. Furthermore, an analysis of residuals indicates that the application of M- and MM estimation methods pulls the main prediction equation towards the main sample data, suggesting a more rigorous fit. Based on robust methods, we observe changes in analyst optimism during the announcement period of the bailout, as evidenced by the significantly positive variable of interest. We support our empirical results with simulations and confirm significant improvements in estimation accuracy when robust regression methods are applied to the samples contaminated by outliers.

Abstract: For two independent random variables, X and Y, let p = P(X > Y ) + 0.5P(X = Y ), which is sometimes described as a probabilistic measure of effect size. It has been argued that for various reasons, p represents an important and useful way of characterizing how groups differ. In clinical trials, for example, an issue is the likelihood that one method of treatment will be more effective than another. The paper deals with making inferences about p when three or more groups are to be compared. When tied values can occur, the results suggest using a multiple comparison procedure based on an extension of Cliff’s method used in conjunction with Hochberg’s sequentially rejective technique. If tied values occur with probability zero, an alternative method can be argued to have a practical advantage. As for a global test, extant rank-based methods are unsatisfactory given the goal of comparing groups based on p. The one method that performed well in simulations is based in part on the distribution of the difference between each pair of random variables. A bootstrap method is used where a p-value is based on the projection depth of the null vector relative to the bootstrap cloud. The proposed methods are illustrated using data from an intervention study.

Abstract: Providing reliable estimates of the ratios of cancer incidence and mortality rates across geographic regions has been important for the National cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program as it profiles cancer risk factors as well decides cancer control planning. A fundamental difficulty, however, arises when such ratios have to be computed to compare the rate of a subregion (e.g., California) with that of a parent region (e.g., the US). Such a comparison is often made for policy-making purposes. Based on F-approximations as well as normal approximations, this paper provides new confidence intervals (CIs) for such rate ratios. Intensive simulations, which capture the real issues with the observed mortality data, reveal that these two CIs perform well. In general, for rare cancer sites, the F-intervals are often more conservative, and for moderate and common cancers, all intervals perform similarly.

Abstract: Linear regression models are often useful tools for exploring the relationship between a response and a set of explanatory (predictor) variables. When both the observed response and the predictor variables are contaminated/distorted by unknown functions of an observable confounder, inferring the underlying relationship between the latent (unobserved) variables is more challenging. Recently, S¸ent¨urk and M¨uller (2005) proposed the method of covariate-adjusted regression (CAR) analysis for this distorted data setting. In this paper, we describe graphical techniques for assessing departures from or violations of specific assumptions regarding the type and form of the data distortion. The type of data distortion consists of multiplicative, additive or no-distortion. The form of the distortion encompasses a class of general smooth distorting functions. However, common confounding adjustment methods in regression analysis implicitly make distortion assumptions, such as assuming additive or multiplicative linear distortions. We illustrate graphical detection of departures from such assumptions on the distortion. The graphical diagnostic techniques are illustrated with numeri cal and real data examples. The proposed graphical assessment of distortion assumptions is feasible due to the CAR estimation method, which utilizes a local regression technique to estimate a set of transformed distorting functions (S¸ent¨urk and Nguyen, 2006).

Abstract: Alcohol and drug uses are common in today’s society and it is well-known that they can lead to serious consequences. Studies have been conducted in order, for example, to understand short- or long-term tem poral processes of alcohol and drug uses. This paper discusses statistical modeling for joint analysis of alcohol and drug uses and several models and the corresponding estimation approaches are presented. The methods are applied to a prospective study of alcohol and drug uses on college freshmen, which motivated this investigation. The analysis results suggest that female subjects seem to have much less consequences of alcohol and drug uses than male subjects and the consequences of alcohol and drug uses decrease along with ages.