scholarly journals Improving the predictive ability of the signal-averaged electrocardiogram with a linear logistic model incorporating clinical variables.

Circulation ◽  
1990 ◽  
Vol 81 (3) ◽  
pp. 797-804 ◽  
Author(s):  
P J Vatterott ◽  
K R Bailey ◽  
S C Hammill
Keyword(s):  
Logistic Model ◽  
Predictive Ability ◽  
Clinical Variables ◽  
Linear Logistic Model

scholarly journals Business Distress Prediction Using Bayesian Logistic Model for Indian Firms

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 113 ◽  
Author(s):  
Arvind Shrivastava ◽  
Kuldeep Kumar ◽  
Nitin Kumar
Keyword(s):  
Bayesian Modeling ◽  
Logistic Model ◽  
Predictive Ability ◽  
Emerging Economy ◽  
Predictive Capability ◽  
Corporate Sector ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Distressed Firms ◽  
Corporate Distress

The objective of the study is to perform corporate distress prediction for an emerging economy, such as India, where bankruptcy details of firms are not available. Exhaustive panel dataset extracted from Capital IQ has been employed for the purpose. Foremost, the study contributes by devising novel framework to capture incipient signs of distress for Indian firms by employing a combination of firm specific parameters. The strategy not only enables enlarging the sample of distressed firms but also enables to obtain robust results. The analysis applies both standard Logistic and Bayesian modeling to predict distressed firms in Indian corporate sector. Thereby, a comparison of predictive ability of the two approaches has been carried out. Both in-sample and out of sample evaluation reveal a consistently better predictive capability employing Bayesian methodology. The study provides useful structure to indicate the early signals of failure in Indian corporate sector that is otherwise limited in literature.

scholarly journals Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

2019 ◽  
Vol 44 (4) ◽  
pp. 473-503 ◽  
Author(s):  
Peida Zhan ◽  
Hong Jiao ◽  
Kaiwen Man ◽  
Lijun Wang
Keyword(s):  
Gibbs Sampler ◽  
Logistic Model ◽  
Structural Model ◽  
Higher Order ◽  
Unified Model ◽  
Cognitive Diagnosis ◽  
Cognitive Diagnosis Models ◽  
Log Linear ◽  
Linear Logistic Model ◽  
Or Gate

In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy “and” gate model; the deterministic inputs, noisy “or” gate model; the linear logistic model; the reduced reparameterized unified model; and the log-linear CDM (LCDM). Further, we introduce the unstructured latent structural model and the higher order latent structural model. We also show how to extend these models to consider polytomous attributes, the testlet effect, and longitudinal diagnosis. Finally, we present an empirical example as a tutorial to illustrate how to use JAGS codes in R.

scholarly journals A Novel Prognostic Model of Endometrial Carcinoma Based on Clinical Variables and Oncogenomic Gene Signature

2021 ◽  
Vol 7 ◽  
Author(s):  
Fang Deng ◽  
Jing Mu ◽  
Chiwen Qu ◽  
Fang Yang ◽  
Xing Liu ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Group ◽  
High Risk Group ◽  
Clinical Variables ◽  
Prognosis Model

Due to the difficulty in predicting the prognosis of endometrial carcinoma (EC) patients by clinical variables alone, this study aims to build a new EC prognosis model integrating clinical and molecular information, so as to improve the accuracy of predicting the prognosis of EC. The clinical and gene expression data of 496 EC patients in the TCGA database were used to establish and validate this model. General Cox regression was applied to analyze clinical variables and RNAs. Elastic net-penalized Cox proportional hazard regression was employed to select the best EC prognosis-related RNAs, and ridge regression was used to construct the EC prognostic model. The predictive ability of the prognostic model was evaluated by the Kaplan–Meier curve and the area under the receiver operating characteristic curve (AUC-ROC). A clinical-RNA prognostic model integrating two clinical variables and 28 RNAs was established. The 5-year AUC of the clinical-RNA prognostic model was 0.932, which is higher than that of the clinical-alone (0.897) or RNA-alone prognostic model (0.836). This clinical-RNA prognostic model can better classify the prognosis risk of EC patients. In the training group (396 patients), the overall survival of EC patients was lower in the high-risk group than in the low-risk group [HR = 32.263, (95% CI, 7.707–135.058), P = 8e-14]. The same comparison result was also observed for the validation group. A novel EC prognosis model integrating clinical variables and RNAs was established, which can better predict the prognosis and help to improve the clinical management of EC patients.

The Rank Sum Test in the Linear Logistic Model

1985 ◽  
Vol 39 (4) ◽  
pp. 322 ◽  
Author(s):  
E. Jacquelin Dietz
Keyword(s):  
Logistic Model ◽  
Linear Logistic Model

scholarly journals Sparse Logistic Regression: Comparison of Regularization and Bayesian Implementations

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 137
Author(s):  
Mattia Zanon ◽  
Giuliano Zambonin ◽  
Gian Antonio Susto ◽  
Seán McLoone
Keyword(s):  
Prediction Accuracy ◽  
Logistic Model ◽  
Relevance Vector Machine ◽  
Sparse Logistic Regression ◽  
Knowledge Based ◽  
Comparable Performance ◽  
Bayesian Implementation ◽  
Input Variables ◽  
Selection Operator ◽  
Linear Logistic Model

In knowledge-based systems, besides obtaining good output prediction accuracy, it is crucial to understand the subset of input variables that have most influence on the output, with the goal of gaining deeper insight into the underlying process. These requirements call for logistic model estimation techniques that provide a sparse solution, i.e., where coefficients associated with non-important variables are set to zero. In this work we compare the performance of two methods: the first one is based on the well known Least Absolute Shrinkage and Selection Operator (LASSO) which involves regularization with an ℓ 1 norm; the second one is the Relevance Vector Machine (RVM) which is based on a Bayesian implementation of the linear logistic model. The two methods are extensively compared in this paper, on real and simulated datasets. Results show that, in general, the two approaches are comparable in terms of prediction performance. RVM outperforms the LASSO both in term of structure recovery (estimation of the correct non-zero model coefficients) and prediction accuracy when the dimensionality of the data tends to increase. However, LASSO shows comparable performance to RVM when the dimensionality of the data is much higher than number of samples that is p > > n .

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