Measures of fit in multiple correspondence analysis of crisp and fuzzy coded data

When continuous data are coded to categorical variables, two types of coding are possible: crisp coding in the form of indicator, or dummy, variables with values either 0 or 1; or fuzzy coding where each observation is transformed to a set of "degrees of membership" between 0 and 1, using co-called membership functions. It is well known that the correspondence analysis of crisp coded data, namely multiple correspondence analysis, yields principal inertias (eigenvalues) that considerably underestimate the quality of the solution in a low-dimensional space. Since the crisp data only code the categories to which each individual case belongs, an alternative measure of fit is simply to count how well these categories are predicted by the solution. Another approach is to consider multiple correspondence analysis equivalently as the analysis of the Burt matrix (i.e., the matrix of all two-way cross-tabulations of the categorical variables), and then perform a joint correspondence analysis to fit just the off-diagonal tables of the Burt matrix - the measure of fit is then computed as the quality of explaining these tables only. The correspondence analysis of fuzzy coded data, called "fuzzy multiple correspondence analysis", suffers from the same problem, albeit attenuated. Again, one can count how many correct predictions are made of the categories which have highest degree of membership. But here one can also defuzzify the results of the analysis to obtain estimated values of the original data, and then calculate a measure of fit in the familiar percentage form, thanks to the resultant orthogonal decomposition of variance. Furthermore, if one thinks of fuzzy multiple correspondence analysis as explaining the two-way associations between variables, a fuzzy Burt matrix can be computed and the same strategy as in the crisp case can be applied to analyse the off-diagonal part of this matrix. In this paper these alternative measures of fit are defined and applied to a data set of continuous meteorological variables, which are coded crisply and fuzzily into three categories. Measuring the fit is further discussed when the data set consists of a mixture of discrete and continuous variables.

TOPSpro Data Dictionary, July 1, 2005–June 30, 2006

The passage of the Workforce Investment Act (WIA) of 1998 [Public Law 105-220] by the 105th Congress has ushered in a new era of collaboration, coordination, cooperation and accountability. The overall goal of the Act is “to increase the employability, retention, and earnings of participants, and increase occupational skill attainment by participants, and, as a result improve the quality of the workforce, reduce welfare dependency, and enhance the productivity and competitiveness of the Nation.” The key principles inculcated in the Act are: • Streamlining services; • Empowering individuals; • Universal access; • Increased accountability; • New roles for local boards; • State and local flexibility; • Improved youth programs. The purpose of Title II, The Adult Education and Family Literacy Act (AEFLA), of the Workforce Investment Act of 1998 is to create a partnership among the federal government, states, and localities to provide, on a voluntary basis, adult education and literacy services in order to: • Assist adults become literate and obtain the knowledge and skills necessary for employment and self-sufficiency; • Assist adults who are parents obtain the educational skills necessary to become full partners in the educational development of their children; • Assist adults in the completion of a secondary school education. Adult education is an important part of the workforce investment system. Title II restructures and improves programs previously authorized by the Adult Education Act. AEFLA focuses on strengthening program quality by requiring States to give priority in awarding funds to local programs that are based on a solid foundation of research, address the diverse needs of adult learners, and utilize other effective practices and strategies. To promote continuous program involvement and to ensure optimal return on the Federal investment, AEFLA also establishes a State performance accountability system. Under this system, the Secretary and each State must reach agreement on annual levels of performance for a number of “core indicators” specified in the law: • Demonstrated improvements in literacy skill levels in reading, writing, and speaking the English language, numeracy, problem solving, English language acquisition, and other literacy skills. • Placement in, retention in, or completion of postsecondary education, training, unsubsidized employment or career advancement. • Receipt of a secondary school diploma or its recognized equivalent. Iowa’s community college based adult basic education program has implemented a series of proactive strategies in order to effectively and systematically meet the challenges posed by WIA. The Iowa TOPSpro Data Dictionary is a direct result of Iowa’s pro-active efforts in this educational arena.

Some last thoughts on compositional data analysis

One of the disadvantages of old age is that there is more past than future: this,however, may be turned into an advantage if the wealth of experience and, hopefully,wisdom gained in the past can be reflected upon and throw some light on possiblefuture trends. To an extent, then, this talk is necessarily personal, certainly nostalgic,but also self critical and inquisitive about our understanding of the discipline ofstatistics. A number of almost philosophical themes will run through the talk: searchfor appropriate modelling in relation to the real problem envisaged, emphasis onsensible balances between simplicity and complexity, the relative roles of theory andpractice, the nature of communication of inferential ideas to the statistical layman, theinter-related roles of teaching, consultation and research. A list of keywords might be:identification of sample space and its mathematical structure, choices betweentransform and stay, the role of parametric modelling, the role of a sample spacemetric, the underused hypothesis lattice, the nature of compositional change,particularly in relation to the modelling of processes. While the main theme will berelevance to compositional data analysis we shall point to substantial implications forgeneral multivariate analysis arising from experience of the development ofcompositional data analysis…

Stepwise multiple testing as formalized data snooping

It is common in econometric applications that several hypothesis tests arecarried out at the same time. The problem then becomes how to decide whichhypotheses to reject, accounting for the multitude of tests. In this paper,we suggest a stepwise multiple testing procedure which asymptoticallycontrols the familywise error rate at a desired level. Compared to relatedsingle-step methods, our procedure is more powerful in the sense that itoften will reject more false hypotheses. In addition, we advocate the useof studentization when it is feasible. Unlike some stepwise methods, ourmethod implicitly captures the joint dependence structure of the teststatistics, which results in increased ability to detect alternativehypotheses. We prove our method asymptotically controls the familywise errorrate under minimal assumptions. We present our methodology in the context ofcomparing several strategies to a common benchmark and deciding whichstrategies actually beat the benchmark. However, our ideas can easily beextended and/or modied to other contexts, such as making inference for theindividual regression coecients in a multiple regression framework. Somesimulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.

Driver scheduling problem modelling

The Drivers Scheduling Problem (DSP) consists of selecting a set of duties for vehicle drivers, for example buses, trains, plane or boat drivers or pilots, for the transportation of passengers or goods. This is a complex problem because it involves several constraints related to labour and company rules and can also present different evaluation criteria and objectives. Being able to develop an adequate model for this problem that can represent the real problem as close as possible is an important research area.The main objective of this research work is to present new mathematical models to the DSP problem that represent all the complexity of the drivers scheduling problem, and also demonstrate that the solutions of these models can be easily implemented in real situations. This issue has been recognized by several authors and as important problem in Public Transportation. The most well-known and general formulation for the DSP is a Set Partition/Set Covering Model (SPP/SCP). However, to a large extend these models simplify some of the specific business aspects and issues of real problems. This makes it difficult to use these models as automatic planning systems because the schedules obtained must be modified manually to be implemented in real situations. Based on extensive passenger transportation experience in bus companies in Portugal, we propose new alternative models to formulate the DSP problem. These models are also based on Set Partitioning/Covering Models; however, they take into account the bus operator issues and the perspective opinions and environment of the user.We follow the steps of the Operations Research Methodology which consist of: Identify the Problem; Understand the System; Formulate a Mathematical Model; Verify the Model; Select the Best Alternative; Present the Results of theAnalysis and Implement and Evaluate. All the processes are done with close participation and involvement of the final users from different transportation companies. The planner s opinion and main criticisms are used to improve the proposed model in a continuous enrichment process. The final objective is to have a model that can be incorporated into an information system to be used as an automatic tool to produce driver schedules. Therefore, the criteria for evaluating the models is the capacity to generate real and useful schedules that can be implemented without many manual adjustments or modifications. We have considered the following as measures of the quality of the model: simplicity, solution quality and applicability. We tested the alternative models with a set of real data obtained from several different transportation companies and analyzed the optimal schedules obtained with respect to the applicability of the solution to the real situation. To do this, the schedules were analyzed by the planners to determine their quality and applicability. The main result of this work is the proposition of new mathematical models for the DSP that better represent the realities of the passenger transportation operators and lead to better schedules that can be implemented directly in real situations.

The donor problem

Donors often rely on local intermediaries to deliver benefits to target beneficiaries. Each selected recipient observes if the intermediary under-delivers to them, so they serve as natural monitors. However, they may withhold complaints when feeling unentitled or grateful to the intermediary for selecting them. Furthermore, the intermediary may distort selection (e.g. by picking richer recipients who feel less entitled) to reduce complaints. We design an experimental game representing the donor s problem. In one treatment, the intermediary selects recipients. In the other, selection is random - as by an uninformed donor. In our data, random selection dominates delegation of the selection task to the intermediary. Selection distortions are similar, but intermediaries embezzle more when they have selection power and (correctly) expect fewer complaints.

Cloning of a cDNA encoding a plasma membrane-associated, uronide binding phosphoprotein with physical properties similar to viral movement proteins.

Oligogalacturonides are structural and regulatory homopolymers from the extracellular pectic matrix of plants. In vitro micromolar concentrations of oligogalacturonates and polygalacturonates were shown previously to stimulate the phosphorylation of a small plasma membrane-associated protein in potato. Immunologically cross-reactive proteins were detected in plasma membrane-enriched fractions from all angiosperm subclasses in the Cronquist system. Polygalacturonate-enhanced phosphorylation of the protein was observed in four of the six dicotyledon subclasses but not in any of the five monocotyledon subclasses. A cDNA for the protein was cloned from potato. The deduced protein is extremely hydrophilic and has a proline-rich N terminus. The C-terminal half of the protein was predicted to be a coiled coil, suggesting that the protein interacts with other macromolecules. The recombinant protein was found to bind both simple and complex galacturonides. The behavior of the protein suggests several parallels with viral proteins involved in intercellular communication.

Metaheuristics for the bus-driver scheduling problem

We present new metaheuristics for solving real crew scheduling problemsin a public transportation bus company. Since the crews of thesecompanies are drivers, we will designate the problem by the bus-driverscheduling problem. Crew scheduling problems are well known and severalmathematical programming based techniques have been proposed to solvethem, in particular using the set-covering formulation. However, inpractice, there exists the need for improvement in terms of computationalefficiency and capacity of solving large-scale instances. Moreover, thereal bus-driver scheduling problems that we consider can present variantaspects of the set covering, as for example a different objectivefunction, implying that alternative solutions methods have to bedeveloped. We propose metaheuristics based on the following approaches:GRASP (greedy randomized adaptive search procedure), tabu search andgenetic algorithms. These metaheuristics also present some innovationfeatures based on and genetic algorithms. These metaheuristics alsopresent some innovation features based on the structure of the crewscheduling problem, that guide the search efficiently and able them tofind good solutions. Some of these new features can also be applied inthe development of heuristics to other combinatorial optimizationproblems. A summary of computational results with real-data problems ispresented.

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.

A behavioral ecology of shermen: hidden stories from trajectory data in the Northern Humboldt Current System

This work proposes an original contribution to the understanding of shermen spatial behavior, based on the behavioral ecology and movement ecology paradigms. Through the analysis of Vessel Monitoring System (VMS) data, we characterized the spatial behavior of Peruvian anchovy shermen at di erent scales: (1) the behavioral modes within shing trips (i.e., searching, shing and cruising); (2) the behavioral patterns among shing trips; (3) the behavioral patterns by shing season conditioned by ecosystem scenarios; and (4) the computation of maps of anchovy presence proxy from the spatial patterns of behavioral mode positions. At the rst scale considered, we compared several Markovian (hidden Markov and semi-Markov models) and discriminative models (random forests, support vector machines and arti cial neural networks) for inferring the behavioral modes associated with VMS tracks. The models were trained under a supervised setting and validated using tracks for which behavioral modes were known (from on-board observers records). Hidden semi-Markov models performed better, and were retained for inferring the behavioral modes on the entire VMS dataset. At the second scale considered, each shing trip was characterized by several features, including the time spent within each behavioral mode. Using a clustering analysis, shing trip patterns were classi ed into groups associated to management zones, eet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped shermen behavior. By means of co-inertia analyses, we found signi cant associations between shermen, anchovy and environmental spatial dynamics, and shermen behavioral responses were characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of shermen re ected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of shermen behavior: shermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these ndings may be of importance for sheries management, collective behavior analyses and end-to-end models.

Note on the blocking flow shop problem

We present some results attained with different algorithms for the Fm|block|Cmax problem using as experimental data the well-known Taillard instances.

Effect of tibial tray inclination on the femoro-tibial contact pattern of a mobile total knee arthroplasty

Introduction: The posterior inclination of the tibial component is an important factor that can affect the success of total knee arthroplasty. It can reduce the posterior impingement and thus increase the range of flexion, but it may also induce instability in flexion, anterior impingement between the polyethylene of postero-stabilizing knee prosthesis, and anterior conflict with the cortical bone and the stem. Although the problem is identified, there is still a debate on the ideal inclination angle and the surgical technique to avoid an excessive posterior inclination. The aim of this study was to predict the effect of a posterior inclination of the tibial component on the contact pattern on the tibial insert, using a numerical musculoskeletal model of the knee joint. Methods: A 3D finite element model of the knee joint was developed to simulate an active and loaded squat movement after total knee arthroplasty. Flexion was actively controlled by the quadriceps muscle and muscle activations were estimated from EMG data and were synchronized by a feedback algorithm. Two inclinations of the tibial tray were considered: a posterior inclination of 0° or 10°. During the entire range of flexion, the following quantities were calculated: the tibiofemoral and patello-femoral contact force, and the contact pattern on polyethylene insert. The antero-posterior displacement of the contact pattern was also measured. Abaqus 6.7 was used for all analyses. Results: The tibio-femoral and patello-femoral contact forces increased during flexion and reached respectively 4 and 7 BW (bodyweight) at 90° of flexion. They were slightly affected by the inclination of the tibial tray. Without posterior inclination, the contact pattern on the tibial insert remained centered. The contact pressure was lower than 5 MPa below 60° of flexion, but exceeded 20 MPa at 90° of flexion. The posterior inclination displaced the contact point posteriorly by 2 to 4 mm. Conclusion: The inclination of the tibial tray displaced the contactpattern towards the posterior border of the tibial insert. However, even for 10° of inclination, the contact center remained far from the posterior border (12 mm). There was no instability predicted for this movement.

Mass conservative three-dimensional water tracer distribution from Markov chain Monte Carlo inversion of time-lapse ground-penetrating radar data

Time-lapse geophysical measurements are widely used to monitor the movement of water and solutes through the subsurface. Yet commonly used deterministic least squares inversions typically suffer from relatively poor mass recovery, spread overestimation, and limited ability to appropriately estimate nonlinear model uncertainty. We describe herein a novel inversion methodology designed to reconstruct the three-dimensional distribution of a tracer anomaly from geophysical data and provide consistent uncertainty estimates using Markov chain Monte Carlo simulation. Posterior sampling is made tractable by using a lower-dimensional model space related both to the Legendre moments of the plume and to predefined morphological constraints. Benchmark results using cross-hole ground-penetrating radar travel times measurements during two synthetic water tracer application experiments involving increasingly complex plume geometries show that the proposed method not only conserves mass but also provides better estimates of plume morphology and posterior model uncertainty than deterministic inversion results.

Underfunding of stroke research: a Europe-wide problem.

BACKGROUND AND PURPOSE: Previous studies in the United States and the United Kingdom have shown that stroke research is underfunded compared with coronary heart disease (CHD) and cancer research despite the high clinical and financial burden of stroke. We aimed to determine whether underfunding of stroke research is a Europe-wide problem. METHODS: Data for the financial year 2000 to 2001 were collected from 9 different European countries. Information on stroke, CHD, and cancer research funding awarded by disease-specific charities and nondisease-specific charity or government- funded organizations was obtained from annual reports, web sites, and by direct communication with organizations. RESULTS: There was marked and consistent underfunding of stroke research in all the countries studied. Stroke funding as a percentage of the total funding for stroke, CHD, and cancer was uniformly low, ranging from 2% to 11%. Funding for stroke was less than funding for cancer, usually by a factor of > or =10. In every country except Turkey, funding for stroke research was less than that for CHD. CONCLUSIONS: This study confirms that stroke research is grossly underfunded, compared with CHD and cancer, throughout Europe. Similar data have been obtained from the United States suggesting that relative underfunding of stroke research is likely to be a worldwide phenomenon.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.