Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA

In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.ANFIS, Discrete Choice Models, Error Back-propagation, Financial Crisis, Fuzzy Logic, US Economy

Estimação e diagnóstico em modelos parcialmente lineares censurados sob distribuições de cauda pesada

Orientadores: Larissa Avila Matos, Víctor Hugo Lachos DávilaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Em muitos estudos, dados limitados ou censurados são coletados. Isso ocorre em várias situações práticas, devido as limitações dos equipamentos de medição ou pelo desenho experimental. Dessa forma, as respostas podem ser censuradas à esquerda, à direita ou em um intervalo. Por outro lado, os modelos parcialmente lineares são considerados como uma extensão flexível dos modelos de regressão lineares incluindo uma componente não paramétrica em alguma covariável. Neste trabalho, estudamos procedimentos de estimação e diagnóstico em modelos de regressão parcialmente lineares com respostas censuradas sob a classe de distribuições de mistura de escala normal (SMN). Esta família de distribuições contém um grupo de distribuições com caudas mais pesadas do que a normal que costumam ser usadas para inferências robustas de dados simétricos, como a t de Student, a slash, a normal contaminada, entre outras. Um algoritmo do tipo EM é apresentado para obter iterativamente as estimativas de máxima verossimilhança penalizada dos parâmetros dos modelos. Para examinar o desempenho dos modelos propostos, técnicas de deleção de casos e de influência local são desenvolvidas para mostrar a robustez contra observações potencialmente influentes e outliers. Isto é feito através da análise de sensibilidade das estimativas de máxima verossimilhança penalizada com alguns esquemas de perturbação no modelo ou nos dados e analisando alguns gráficos de diagnóstico. A eficácia do método proposto é avaliada através da análise de conjuntos de dados simulados e reais. O pacote \verb+PartCensReg+ implementado no R dá suporte computacional para este trabalhoAbstract: In many studies, limited or censored data are collected. This occurs, in many situations in practice, for reasons such as limitations of measuring instruments or due to experimental design. So, the responses can be either left, interval or right censored. On the other hand, partially linear models are considered as a flexible generalizations of linear regression models by including a nonparametric component of some covariate in the linear predictor. In this work, we discuss estimation and diagnostic procedures in partially linear censored regression models with errors following a scale mixture of normal (SMN) distributions. This family of distributions contains a group of well-known heavy-tailed distributions that are often used for robust inference of symmetrical data, such as Student-t, slash and contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum penalized likelihood (MPL) estimates of the parameters is presented. To examine the performance of the proposed model, case-deletion and local influence techniques are developed to show its robustness against outlying and influential observations. This is performed by sensitivity analysis of the maximum penalized likelihood estimates under some usual perturbation schemes, either in the model or in the data, and by inspecting some proposed diagnostic graphs. We evaluate the finite sample performance of the algorithm and the asymptotic properties of the MPL estimates through empirical experiments. An application to a real dataset is presented to illustrate the effectiveness of the proposed methods. The package \verb+PartCensReg+ implemented for the software R give computational support to this workMestradoEstatisticaMestre em EstatísticaCAPE

Fitting the Cusp Catastrophe in R: A cusp Package Primer

Of the seven elementary catastrophes in catastrophe theory, the âÂÂcuspâ model is the most widely applied. Most applications are however qualitative. Quantitative techniques for catastrophe modeling have been developed, but so far the limited availability of flexible software has hindered quantitative assessment. We present a package that implements and extends the method of Cobb (Cobb and Watson'80; Cobb, Koppstein, and Chen'83), and makes it easy to quantitatively fit and compare different cusp catastrophe models in a statistically principled way. After a short introduction to the cusp catastrophe, we demonstrate the package with two instructive examples.

Uncertainty propagation and speculation in projective forecasts of environmental change: a lake-eutrophication example

The issue of whether models developed for current conditions can yield correct predictions when used under changed control, as is often the case in environmental management, is discussed. Two models of different complexity are compared on the basis of performance criteria, but it appears that good performance at the calibration stage does not guarantee correctly predicted behavior. A requirement for the detection of such a failure of the model is that the prediction uncertainty range is known. Two techniques to calculate uncertainty propagation are presented and compared: a stochastic first-order error propagation based on the extended Kalman filter (EKF), and a newly developed and robust Monte Carlo set-membership procedure (MCSM). The procedures are applied to a case study of water quality, generating a projective forecast of the algal dynamics in a lake (Lake Veluwe) in response to management actions that force the system into a different mode of behavior. It is found that the forecast from the more complex model falls within the prediction uncertainty range, but its informative value is low due to large uncertainty bounds. As a substitute for time-consuming revisions of the model, educated speculation about parameter shifts is offered as an alternative approach to account for expected but unmodelled changes in the system

Practical approaches to mining of clinical datasets : from frameworks to novel feature selection

Research has investigated clinical data that have embedded within them numerous complexities and uncertainties in the form of missing values, class imbalances and high dimensionality. The research in this thesis was motivated by these challenges to minimise these problems whilst, at the same time, maximising classification performance of data and also selecting the significant subset of variables. As such, this led to the proposal of a data mining framework and feature selection method. The proposed framework has a simple algorithmic framework and makes use of a modified form of existing frameworks to address a variety of different data issues, called the Handling Clinical Data Framework (HCDF). The assessment of data mining techniques reveals that missing values imputation and resampling data for class balancing can improve the performance of classification. Next, the proposed feature selection method was introduced; it involves projecting onto principal component method (FS-PPC) and draws on ideas from both feature extraction and feature selection to select a significant subset of features from the data. This method selects features that have high correlation with the principal component by applying symmetrical uncertainty (SU). However, irrelevant and redundant features are removed by using mutual information (MI). However, this method provides confidence in the selected subset of features that will yield realistic results with less time and effort. FS-PPC is able to retain classification performance and meaningful features while consisting of non-redundant features. The proposed methods have been practically applied to analysis of real clinical data and their effectiveness has been assessed. The results show that the proposed methods are enable to minimise the clinical data problems whilst, at the same time, maximising classification performance of data

The Binormal Hypothesis of Specific Learning Disabilities

The concept of specific learning disabilities has its roots in the medical literature of the nineteenth century. According to the medical model the cause of specific learning disabilities are presumed to lie in specific cognitive dysfunctions. This hypothesis predicts two qualitatively distinct types of learner and a bimodal distribution of assessment scores. Evidence for bimodality has been sought in the distribution of residuals generated from the regression of standardised measures of attainment on IQ, however this technique has been widely criticised. Recent advances in computer adaptive assessment, coupled with Rasch interval level measurement, have opened up the possibility of seeking evidence for bimodality in the distribution of assessment scores directly. In the present study the binormal distribution was developed as a model for describing bimodality. The binormal distribution is conceived as two superimposed normal distributions and is defined by five parameters. The algebraic relationship between the five parameters was first determined, and then a methodology was developed for deriving objective estimates of those parameters. The methodology was applied to a unique dataset of over 80,000 children aged between seven and eleven years of age, and across four assessment domains; picture vocabulary, reading, mathematics and arithmetic. The methodology was found to be sensitive to factors that might influence the shape of the distribution of assessment scores such as gender, number of years of schooling, and ceiling effects, and this affected its utility. Nevertheless evidence was found for the existence two qualitatively distinct groups of reader. The pattern in these results was consistent with a developmental transition from beginning to fluent reader. Evidence was also found for a developmental lag between boys and girls, which would explain the higher prevalence of dyslexia reported for boys in many studies. The methodology produced inconsistent results when applied to the other assessments, and no evidence was found to either confirm or disprove the existence of specific dysfunctions as predicted by the medical model