A note on the dual scaling of dominance data and its relationship to correspondence analysis

Dual scaling of a subjects-by-objects table of dominance data (preferences,paired comparisons and successive categories data) has been contrasted with correspondence analysis, as if the two techniques were somehow different. In this note we show that dual scaling of dominance data is equivalent to the correspondence analysis of a table which is doubled with respect to subjects. We also show that the results of both methods can be recovered from a principal components analysis of the undoubled dominance table which is centred with respect to subject means.

R&D cooperation and spillovers: Some empirical evidence

This paper provides some first empirical evidence on the relationshipbetween R&D spillovers and R&D cooperation. The results suggest disentangling different aspects of know-how flows. Firms which rate incoming spillovers more importantly and who can limit outgoing spillovers by a more effective protection of know-how, are more likely to cooperate in R&D. Our analysis also finds that cooperating firms have higher incoming spillovers and higher protection of know-how, indicating that cooperation may serve as a vehicle to manage information flows. Our results thus suggest that on the one hand the information sharing and coordination aspects of incoming spillovers are crucial in understanding cooperation, while on the other hand, protection against outgoing spillovers is important for firms to engage in stable cooperative agreements by reducing free-rider problems. Distinguishing different types of cooperative partners reveals that while managing outgoing spillovers is less critical in alliances with non-commercial research partners than between vertically related partners, the incoming spillovers seem to be more critical in understanding the former type of R&D cooperation.

A data-dependent skeleton estimate and a scale-sensitive dimension for classification

The classical binary classification problem is investigatedwhen it is known in advance that the posterior probability function(or regression function) belongs to some class of functions. We introduceand analyze a method which effectively exploits this knowledge. The methodis based on minimizing the empirical risk over a carefully selected``skeleton'' of the class of regression functions. The skeleton is acovering of the class based on a data--dependent metric, especiallyfitted for classification. A new scale--sensitive dimension isintroduced which is more useful for the studied classification problemthan other, previously defined, dimension measures. This fact isdemonstrated by performance bounds for the skeleton estimate in termsof the new dimension.

Cigarette and Tobacco Receipts Report, December 2008

This is a report for the Iowa Department of Revenue and Finance about the receipts on cigarettes and tobacco for each month of the year. This receipt is the tax of each sale of cigarettes and tobacco.

Cigarette and Tobacco Receipts Report, January 2009

This is a report for the Iowa Department of Revenue and Finance about the receipts on cigarettes and tobacco for each month of the year. This receipt is the tax of each sale of cigarettes and tobacco.

Combining heat stress and moderate hypoxia reduces cycling time to exhaustion without modifying neuromuscular fatigue characteristics.

PURPOSE: This study investigated the isolated and combined effects of heat [temperate (22 °C/30 % rH) vs. hot (35 °C/40 % rH)] and hypoxia [sea level (FiO2 0.21) vs. moderate altitude (FiO2 0.15)] on exercise capacity and neuromuscular fatigue characteristics. METHODS: Eleven physically active subjects cycled to exhaustion at constant workload (66 % of the power output associated with their maximal oxygen uptake in temperate conditions) in four different environmental conditions [temperate/sea level (control), hot/sea level (hot), temperate/moderate altitude (hypoxia) and hot/moderate altitude (hot + hypoxia)]. Torque and electromyography (EMG) responses following electrical stimulation of the tibial nerve (plantar-flexion; soleus) were recorded before and 5 min after exercise. RESULTS: Time to exhaustion was reduced (P < 0.05) in hot (-35 ± 15 %) or hypoxia (-36 ± 14 %) compared to control (61 ± 28 min), while hot + hypoxia (-51 ± 20 %) further compromised exercise capacity (P < 0.05). However, the effect of temperature or altitude on end-exercise core temperature (P = 0.089 and P = 0.070, respectively) and rating of perceived exertion (P > 0.05) did not reach significance. Maximal voluntary contraction torque, voluntary activation (twitch interpolation) and peak twitch torque decreased from pre- to post-exercise (-9 ± 1, -4 ± 1 and -6 ± 1 % all trials compounded, respectively; P < 0.05), with no effect of the temperature or altitude. M-wave amplitude and root mean square activity were reduced (P < 0.05) in hot compared to temperate conditions, while normalized maximal EMG activity did not change. Altitude had no effect on any measured parameters. CONCLUSION: Moderate hypoxia in combination with heat stress reduces cycling time to exhaustion without modifying neuromuscular fatigue characteristics. Impaired oxygen delivery or increased cardiovascular strain, increasing relative exercise intensity, may have also contributed to earlier exercise cessation.

Design effect and interviewer effects: Evidence from the Spanish ESS3-2006 Data

In 2007 the first Quality Enhancement Meeting on sampling in the European SocialSurvey (ESS) took place. The discussion focused on design effects and inteviewereffects in face-to-face interviews. Following the recomendations of this meeting theSpanish ESS team studied the impact of interviewers as a new element in the designeffect in the response s variance using the information of the correspondent SampleDesign Data Files. Hierarchical multilevel and cross-classified multilevel analysis areconducted in order to estimate the amount of responses variation due to PSU and tointerviewers for different questions in the survey. Factor such as the age of theinterviewer, gender, workload, training and experience and respondent characteristicssuch as age, gender, renuance to participate and their possible interactions are alsoincluded in the analysis of some specific questions like trust in politicians and trustin legal system . Some recomendations related to future sampling designs and thecontents of the briefing sessions are derived from this initial research.

Trade liberalization in Uruguay: Domestic and foreign firms

The reallocation of resources is one of the main impacts of tradeliberalization processes. In the case of manufacturing industries resourceswill be reallocated from import--competing sectors to export--orientedsectors. This paper studies the effects that a more open economic environmenthas had on the entry conditions for foreign and domestic firms in Uruguayanmanufacturing industries. We find significant differences in the behaviorof foreign and domestic firms, both when they are incumbents or when theyact as potential entrants. In general, foreign firms seem to be moresuccessful in applying entry deterring strategies, due to advantages inforeign markets, deeper financial resources or better technological capabilities.They also appear to be more responsive to entry conditions when theyface the prospects of entering a given industry.

Decision making in uncertain and changing environments

We consider an agent who has to repeatedly make choices in an uncertainand changing environment, who has full information of the past, who discountsfuture payoffs, but who has no prior. We provide a learning algorithm thatperforms almost as well as the best of a given finite number of experts orbenchmark strategies and does so at any point in time, provided the agentis sufficiently patient. The key is to find the appropriate degree of forgettingdistant past. Standard learning algorithms that treat recent and distant pastequally do not have the sequential epsilon optimality property.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Inequalities for a new data-based method for selecting nonparametric density estimates

We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provideexplicit non-asymptotic density-free inequalities that relate the $L_1$ error of the selected estimate with that of the best possible estimate,and study in particular the connection between the richness of the classof density estimates and the performance bound. For example, our methodallows one to pick the bandwidth and kernel order in the kernel estimatesimultaneously and still assure that for {\it all densities}, the $L_1$error of the corresponding kernel estimate is not larger than aboutthree times the error of the estimate with the optimal smoothing factor and kernel plus a constant times $\sqrt{\log n/n}$, where $n$ is the sample size, and the constant only depends on the complexity of the family of kernels used in the estimate. Further applications include multivariate kernel estimates, transformed kernel estimates, and variablekernel estimates.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical {\sc vc} dimension, empirical {\sc vc} entropy, andmargin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Comparing and validating hypothesis test procedures: Graphical and numerical tools

When the behaviour of a specific hypothesis test statistic is studied by aMonte Carlo experiment, the usual way to describe its quality is by givingthe empirical level of the test. As an alternative to this procedure, we usethe empirical distribution of the obtained \emph{p-}values and exploit itsinformation both graphically and numerically.

On the concept of optimality interval

The approximants to regular continued fractions constitute `best approximations' to the numbers they converge to in two ways known as of the first and the second kind.This property of continued fractions provides a solution to Gosper's problem of the batting average: if the batting average of a baseball player is 0.334, what is the minimum number of times he has been at bat? In this paper, we tackle somehow the inverse question: given a rational number P/Q, what is the set of all numbers for which P/Q is a `best approximation' of one or the other kind? We prove that inboth cases these `Optimality Sets' are intervals and we give aprecise description of their endpoints.

Assessing the wellbeing of the Spanish elderly

In this paper we use a variety of data sources, both micro and macro, time series, crosssection, and panel data to provide an empirical evaluation of the current level of economicwellbeing of the Spanish elderly, and of its determinants. We focus, in particular on the role played by the pension system and its generosity in terms of minimum pension supplements and non-contributive pensions. In an IV context, we find that actual Social Security benefits contribute substantially to explain income and consumption poverty levels and trends of low income and consumption percentiles. Thus we offer support to previous evidence for Spain emphasizing the role of minimum benefit policies.