6,058 research outputs found
APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING
Recursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.finance, credit scoring, misclassification, recursive partitioning algorithm, Agricultural Finance,
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
Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables
The use of non-parametric methodologies, the introduction of non-financial variables,
and the development of models geared towards the homogeneous characteristics of
corporate sub-populations have recently experienced a surge of interest in the bankruptcy
literature. However, no research on default prediction has yet focused on micro-entities
(MEs), despite such firms’ importance in the global economy. This paper builds the first
bankruptcy model especially designed for MEs by using a wide set of accounts from 1999
to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs
outperform the traditional logistic regression (LR) models. In addition, we also report
that, thanks to the introduction of non-financial predictors related to age, the delay
in filing accounts, legal action by creditors to recover unpaid debts, and the ownership
features of the company, the improvement with respect to the use of solely financial
information is 3.6%, which is even higher than the improvement that involves the use
of the best ANN (2.6%)
Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
The authors are doing the readers of Statistical Science a true service with
a well-written and up-to-date overview of boosting that originated with the
seminal algorithms of Freund and Schapire. Equally, we are grateful for
high-level software that will permit a larger readership to experiment with, or
simply apply, boosting-inspired model fitting. The authors show us a world of
methodology that illustrates how a fundamental innovation can penetrate every
nook and cranny of statistical thinking and practice. They introduce the reader
to one particular interpretation of boosting and then give a display of its
potential with extensions from classification (where it all started) to least
squares, exponential family models, survival analysis, to base-learners other
than trees such as smoothing splines, to degrees of freedom and regularization,
and to fascinating recent work in model selection. The uninitiated reader will
find that the authors did a nice job of presenting a certain coherent and
useful interpretation of boosting. The other reader, though, who has watched
the business of boosting for a while, may have quibbles with the authors over
details of the historic record and, more importantly, over their optimism about
the current state of theoretical knowledge. In fact, as much as ``the
statistical view'' has proven fruitful, it has also resulted in some ideas
about why boosting works that may be misconceived, and in some recommendations
that may be misguided. [arXiv:0804.2752]Comment: Published in at http://dx.doi.org/10.1214/07-STS242B the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Random Forests for Big Data
Big Data is one of the major challenges of statistical science and has
numerous consequences from algorithmic and theoretical viewpoints. Big Data
always involve massive data but they also often include online data and data
heterogeneity. Recently some statistical methods have been adapted to process
Big Data, like linear regression models, clustering methods and bootstrapping
schemes. Based on decision trees combined with aggregation and bootstrap ideas,
random forests were introduced by Breiman in 2001. They are a powerful
nonparametric statistical method allowing to consider in a single and versatile
framework regression problems, as well as two-class and multi-class
classification problems. Focusing on classification problems, this paper
proposes a selective review of available proposals that deal with scaling
random forests to Big Data problems. These proposals rely on parallel
environments or on online adaptations of random forests. We also describe how
related quantities -- such as out-of-bag error and variable importance -- are
addressed in these methods. Then, we formulate various remarks for random
forests in the Big Data context. Finally, we experiment five variants on two
massive datasets (15 and 120 millions of observations), a simulated one as well
as real world data. One variant relies on subsampling while three others are
related to parallel implementations of random forests and involve either
various adaptations of bootstrap to Big Data or to "divide-and-conquer"
approaches. The fifth variant relates on online learning of random forests.
These numerical experiments lead to highlight the relative performance of the
different variants, as well as some of their limitations
Evaluating Misclassification Probability Using Empirical Risk
* The work is supported by RFBR, grant 04-01-00858-aThe goal of the paper is to estimate misclassification probability for decision function by training
sample. Here are presented results of investigation an empirical risk bias for nearest neighbours, linear and
decision tree classifier in comparison with exact bias estimations for a discrete (multinomial) case. This allows to
find out how far Vapnik–Chervonenkis risk estimations are off for considered decision function classes and to
choose optimal complexity parameters for constructed decision functions. Comparison of linear classifier and
decision trees capacities is also performed
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