278,396 research outputs found
Hybrid model using logit and nonparametric methods for predicting micro-entity failure
Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper
by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to
detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods
(Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as
either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and
Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method
implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic
variables complement financial ratios for bankruptcy prediction
A Comparison of classification/regression trees and logistic regression in failure models
The use of non-parametric statistical methods, the development of models geared towards the homogeneous characteristics of
corporate sub-populations, and the introduction of non-financial variables, are three main issues analysed in this paper. This
study compares the predictive performance of a non-parametric methodology, namelyClassification/Regression Trees (CART),
against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as
micro-entities,from the United Kingdom for the period 1999 to 2008 that includes financial, non-financial, and macroeconomic
variables. Our findings show that CART outperforms the standard approach in the literature, LR
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
In this paper, we study regression problems over a separable Hilbert space
with the square loss, covering non-parametric regression over a reproducing
kernel Hilbert space. We investigate a class of spectral-regularized
algorithms, including ridge regression, principal component analysis, and
gradient methods. We prove optimal, high-probability convergence results in
terms of variants of norms for the studied algorithms, considering a capacity
assumption on the hypothesis space and a general source condition on the target
function. Consequently, we obtain almost sure convergence results with optimal
rates. Our results improve and generalize previous results, filling a
theoretical gap for the non-attainable cases
Default Predictors and Credit Scoring Models for Retail Banking
This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification.credit scoring, discrimination analysis, banking sector, pattern recognition, retail loans, CART, European Union
On the spectral density of the wavelet coefficients of long memory time series with application to the log-regression estimation of the memory parameter
In the recent years, methods to estimate the memory parameter using wavelet
analysis have gained popularity in many areas of science. Despite its
widespread use, a rigorous semi-parametric asymptotic theory, comparable to the
one developed for Fourier methods, is still missing. In this contribution, we
adapt the classical semi-parametric framework introduced by Robinson and his
co-authors for estimating the memory parameter of a (possibly) non-stationary
process. As an application, we obtain minimax upper bounds for the log-scale
regression estimator of the memory parameter for a Gaussian process and we
derive an explicit expression of its variance.Comment: to appear in the Journal of Time Series Analysi
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