Establishing statistical foundations early : data modeling with young learners

This paper addresses research from a three-year longitudinal study that engaged children in data modeling experiences from the beginning school year through to third year (6-8 years). A data modeling approach to statistical development differs in several ways from what is typically done in early classroom experiences with data. In particular, data modeling immerses children in problems that evolve from their own questions and reasoning, with core statistical foundations established early. These foundations include a focus on posing and refining statistical questions within and across contexts, structuring and representing data, making informal inferences, and developing conceptual, representational, and metarepresentational competence. Examples are presented of how young learners developed and sustained informal inferential reasoning and metarepresentational competence across the study to become “sophisticated statisticians”.

Good just isn't Good enough. Best System Probabilities and the Foundations of Statistical Mechanics

Statistical physicists assume a probability distribution over micro-states to explain thermodynamic behavior. The question of this paper is whether these probabilities are part of a best system and can thus be interpreted as Humean chances. I consider two strategies, viz. a globalist as suggested by Loewer, and a localist as advocated by Frigg and Hoefer. Both strategies fail because the system they are part of have rivals that are roughly equally good, while ontic probabilities should be part of a clearly winning system. I conclude with the diagnosis that well-defined micro-probabilities under-estimate the robust character of explanations in statistical physics.

Foundations and applications of non-equilibrium statistical mechanics

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Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Prediction of frequency and amplitude of foundations at resonance

This note presents the statistical analysis carried out on some of the available experimental results to predict the resonant frequency and maximum displacement amplitude of a machine foundation – soil system under vertical vibration as a function of the size and weight of the foundation and of the excitation level. A total of 442 experimental results of Fry, Novak, and Raman have been analysed using nonlinear regression analysis. The results obtained compared well with predictions obtained from the popular theoretical models, and the coefficient of correlation obtained from the analysis was satisfactory in most of the cases.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues.

For optimal solutions in health care, decision makers inevitably must evaluate trade-offs, which call for multi-attribute valuation methods. Researchers have proposed using best-worst scaling (BWS) methods which seek to extract information from respondents by asking them to identify the best and worst items in each choice set. While a companion paper describes the different types of BWS, application and their advantages and downsides, this contribution expounds their relationships with microeconomic theory, which also have implications for statistical inference. This article devotes to the microeconomic foundations of preference measurement, also addressing issues such as scale invariance and scale heterogeneity. Furthermore the paper discusses the basics of preference measurement using rating, ranking and stated choice data in the light of the findings of the preceding section. Moreover the paper gives an introduction to the use of stated choice data and juxtaposes BWS with the microeconomic foundations.

Organizational and financial transparency in the website of the foundations: an empirical study for Spain

Transparency in nonprofit sector and foundations, as an element to enhance the confidence of stakeholders in the organization, is a fact shown by several studies in recent decades. Transparency can be considered in various fields and through different channels. In our study we focused on the analysis of the organizational and economic transparency of foundations, shown through the voluntary information on their Website. We review the theoretical previous studies published to put to the foundations within the framework of the social economy. This theoretical framework has focused on accountability that make foundations in relation to its social function and its management, especially since the most recent focus of information transparency across the Website.In this theoretical framework was made an index to quantify the voluntary information which is shown on its website. This index has been developed ad hoc for this study and applied to a group of large corporate foundations.With the application of these data are obtained two kind of results, to a descriptive level and to a inferential level.We analyzed the statistical correlation between economic transparency and organizational transparency offered in the Website through quantified variables by a multiple linear regression. This empirical analysis allows us to draw conclusions about the level of transparency offered by these organizations in relation to their organizational and financial information, as well as explain the relation between them.

Ranald Macdonald and statistical inference

Ranald Roderick Macdonald (1945-2007) was an important contributor to mathematical psychology in the UK, as a referee and action editor for British Journal of Mathematical and Statistical Psychology and as a participant and organizer at the British Psychological Society's Mathematics, statistics and computing section meetings. This appreciation argues that his most important contribution was to the foundations of significance testing, where his concern about what information was relevant in interpreting the results of significance tests led him to be a persuasive advocate for the 'Weak Fisherian' form of hypothesis testing.

Development of national statistical systems and the responsibilities of national statistical offices

Includes bibliography

A simple method to predict elastic settlements in foundations resting on two soils of differing deformability

This paper shows the analysis results obtained from more than 200 finite element method (FEM) models used to calculate the settlement of a foundation resting on two soils of differing deformability. The analysis considers such different parameters as the foundation geometry, the percentage of each soil in contact with the foundation base and the ratio of the soils’ elastic moduli. From the described analysis, it is concluded that the maximum settlement of the foundation, calculated by assuming that the foundation is completely resting on the most deformable soil, can be correlated with the settlement calculated by FEM models through a correction coefficient named “settlement reduction factor” (α). As a consequence, a novel expression is proposed for calculating the real settlement of a foundation resting on two soils of different deformability with maximum errors lower than 1.57%, as demonstrated by the statistical analysis carried out. A guide for the application of the proposed simple method is also explained in the paper. Finally, the proposed methodology has been validated using settlement data from an instrumented foundation, indicating that this is a simple, reliable and quick method which allows the computation of the maximum elastic settlement of a raft foundation, evaluates its suitability and optimises its selection process.

Theoretical foundations of neural networks

Neural networks have often been motivated by superficial analogy with biological nervous systems. Recently, however, it has become widely recognised that the effective application of neural networks requires instead a deeper understanding of the theoretical foundations of these models. Insight into neural networks comes from a number of fields including statistical pattern recognition, computational learning theory, statistics, information geometry and statistical mechanics. As an illustration of the importance of understanding the theoretical basis for neural network models, we consider their application to the solution of multi-valued inverse problems. We show how a naive application of the standard least-squares approach can lead to very poor results, and how an appreciation of the underlying statistical goals of the modelling process allows the development of a more general and more powerful formalism which can tackle the problem of multi-modality.

Micro-Foundations of Financialization. Status Anxiety and the Market for Consumer Credit in Chile

This project investigates why people in Chile acquired so much consumer debt in contexts of material prosperity, and asks what the role of inequality and commodification is in this process. The case raises an important challenge to the literature. Insofar as existing accounts assume that the financialization of consumption occurs in contexts marked by wage stagnation and a general deterioration of the middle classes, they engender two contradictory explanations: while political economists argue that people use credit in order to smooth their consumption in the face of market volatility, economists maintain that concentration of wealth at the top pushes middle income consumers to emulate the expenditures of the rich and consume beyond their means. These explanations do not necessarily fit the reality of developing countries. Triangulating in-depth interviews with middle class families, multivariate statistical analysis and secondary literature, the project shows that consumers in Chile use credit to finance “ordinary” forms of consumption that do not aim either at coping with market instability or emulating and signaling status to others. Rather, Chileans use department store credit cards in order to acquire a standard package of “inconspicuous” goods that they feel entitled to have. From this point of view, the systematic indebtedness of consumers originates in a major concern with “rank”, “achievement” and "security" that – following De Botton -- I call “status anxiety”. Status anxiety does not stem from the desire to emulate rich consumers, but from the impossibility of complying with normative expectations about what a middle class family should be (and have) that outweigh wage improvements. The project thus investigates the way in which “status anxiety” is systematically reproduced by means of two broad mechanisms that prompt people to acquire consumer debt. The first mechanism generating debt stems from an increase of real wages and high levels of inequality. It is explained by a general sociological principle known as relative deprivation, which points to the fact that general satisfaction with one´s income, possessions or status, is assessed not in absolute terms such as total income, but in relation with reference groups. In this sense, I explore the mechanisms that operate as catalyzers of relative deprivation, by making explicit social inequalities and distorting the perception of others´ wealth. Despite upward mobility and economic improvement, Chileans share the perception of “falling behind,” which materializes in an “imaginary middle class” against which people compare their status, possessions and economic independence. Finally, I show that the commodification of education, health and pension funds does not directly prompt people to acquire consumer debt, but operate as “income draining” mechanisms that demand higher shares of middle class families’ “discretionary income.” In combination with “relative deprivation,” these “income draining” mechanisms leave families with few options to perform their desired class identities, other than learning how to bring resources from the future into the present with the help of department store credit cards.