Creditor Classification Logistic Regression Ensemble Boosting And Logistic Regression In Creditor Classification With Binary Response

Credit risk is the risk that has the greatest opportunity to occur in banking. The number of bad loans will also affect bank performance. The banking sector needs to know whether a prospective creditor is classified as a risky person or not. The purpose of this study is to classify creditors and compare the classification results through logistic regression with the maximum likelihood model and the Boosting algorithm, especially the AdaBoost algorithm, and to select a model with the Boosting algorithm Credit Scoring aims to classify prospective creditor into two classes, namely good prospective creditor (Performing Loan) and bad prospective creditor (Non Performing Loan) based on certain characteristics. The method often used for classifying creditor is logistic regression, but this method is less robust and less accurate than data mining. Thus, there is a need for methods that provide greater accuracy. Among the methods that have been proposed is a method called Boosting, which operates sequentially by applying a classification algorithm to the reweighted version of the training data set. This study uses 5 datasets. The first dataset is secondary data originating from data on non-subsidized homeownership creditors of Bank X Malang City. While the other datasets are simulation data with many samples of 10, 500, and 1000. The results of this study indicate that ensemble boosting logistic regression is more suitable for describing binary response problems, especially creditor classification because it provides more accurate information. For high-dimensional data, which is represented by a sample size of 10, ensemble logistic regression is proven to be able to produce fairly accurate predictions with an accuracy rate of up to 80%, whereas in the logistic regression analysis the model raises N.A because many samples < many independent variables. The use of boosting is preferred because it focuses on problems that are misclassified and have a tendency to increase to higher accuracy.

On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

The model developed considers the uniqueness of a data-driven binary response (indicated by 0 and 1) identified as having a Bernoulli distribution with finite mixture components. In social science applications, Bernoulli’s constructs a hierarchical structure data. This study introduces the Hierarchical Bernoulli mixture model (Hibermimo), a new analytical model that combines the Bernoulli mixture with hierarchical structure data. The proposed approach uses a Hamiltonian Monte Carlo algorithm with a No-U-Turn Sampler (HMC/NUTS). The study has performed a compatible syntax program computation utilizing the HMC/NUTS to analyze the Bayesian Bernoulli mixture aggregate regression model (BBMARM) and Hibermimo. In the model estimation, Hibermimo yielded a result of ~90% compliance with the modeling of each district and a small Widely Applicable Information Criteria (WAIC) value.

Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data

In clinical trials, futility rules are widely used to monitor the study while it is in progress, with the aim of ensuring early termination if the experimental treatment is unlikely to provide the desired level of efficacy. In this paper, we focus on Bayesian strategies to perform interim analyses in single-arm trials based on a binary response variable. Designs that exploit both posterior and predictive probabilities are described and a slight modification of the futility rules is introduced when a fixed historical response rate is used, in order to add uncertainty in the efficacy probability of the standard treatment through the use of prior distributions. The stopping boundaries of the designs are compared under the same trial settings and simulation studies are performed to evaluate the operating characteristics when analogous procedures are used to calibrate the probability cut-offs of the different decision rules.

Deep Learning-Based Residual Control Chart for Binary Response

A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neural network model and deep learning model. We prove that the deep learning r control chart is relatively efficient to monitor the simulated and real binary response asymmetric data compared with r control chart of the generalized linear model (GLM) with probit and logit link functions and neural network r control chart.

Influence of oviposition-inducing hormone on spawning and mortality in the endangered Panamanian golden frog (Atelopus zeteki)

Background With Panamanian golden frogs (Atelopus zeteki; PGFs) likely extirpated from the wild, ensuring long-term sustainability of captive populations is crucial in order to conserve this critically endangered species. Unfortunately, PGFs display a unique reproductive behavior involving a prolonged period of amplexus leading to challenges in their successful captive propagation. The Maryland Zoo in Baltimore has observed high levels of mortality during the breeding season and suboptimal reproductive success leading to the use of hormone stimulation to aid in reproduction and health management. Methods This project aimed to develop induced ovulation and health management protocols by (1) evaluating different doses of gonadotropin releasing hormone analogue (GnRHa), (2) comparing the efficacy of GnRHa and GnRHa + metoclopramide, (3) determining latency periods and the effects of pulsed hormone sequences; and (4) establish if mortality is impacted by hormone therapy. Female PGFs (n = 174) were given GnRHa either in various concentrations (Experiment 1) or combined with metoclopramide (Experiment 2), and oviposition success, latency, and mortality were measured as binary response variables. Results Overall, the use of exogenous hormones significantly decreased mortality when compared to the control data of natural egg-laying females. GnRHa doses of 0.05 μg/g body weight produced similar ovulation rates compared to higher doses, and the addition of metoclopramide did not increase oviposition success compared to GnRHa alone. Lastly, results indicate the majority of female PGFs will release eggs within 48 h following the initial pulse of hormones with a small percentage ovipositing after a second pulse. Conclusion Findings from this study will benefit captive management of PGFs by documenting the increased survival of females when given hormone stimulation and defining appropriate GnRHa doses and expected latency to spawning.

RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses.