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Causal Inference with Two-Stage Logistic Regression - Accuracy, Precision, and Application

Cai, Bing - 2010

2SPS and 2SRI for logistic binary outcomes have not been thoroughly studied. We study the bias and variance properties … trial. We first present closed form expressions of asymptotic bias for the causal odds ratio from both 2SPS and 2SRI … approaches. Our closed form bias results show that the 2SPS logistic regression generates asymptotically biased estimates of this …

Persistent link: https://www.econbiz.de/10009439053

Better Safe than Sorry: Precautionary Reasoning and Implied Dominance in Risky Decisions

DeKay, Michael L.; Patiño-Echeverri, Dalia; Fischbeck, … - 2005

alternatives can seriously bias the evaluation of consequences in risky high-stakes decisions. …

Persistent link: https://www.econbiz.de/10009441242

Better Safe than Sorry: Precautionary Reasoning and Implied Dominance in Risky Decisions

DeKay, Michael L.; Patino-Echeverri, Dalia; Fischbeck, … - 2008

alternatives can seriously bias the evaluation of consequences in risky high-stakes decisions. …

Persistent link: https://www.econbiz.de/10009441300

The Impacts of Attribute Level Framing and Changing Cost Levels on Choice Experiments Value Estimates

Kragt, Marit Ellen; Bennett, Jeffrey W. - 2010

Choice Experiments (CE) are increasingly used to estimate the values of environmental goods andservices. CE questionnaires represent the environmental good under valuation by varying levels ofnon-market attributes. Inclusion of a cost attribute enables the estimation of monetary values...

Persistent link: https://www.econbiz.de/10009446025

Adaptive Inference for Multi-Stage Survey Data

Al-Zou'bi, L. M.; Clark, Robert Graham; Steel, D. G. - 2009

Two-stage sampling usually leads to higher variances for estimators of means and regression coefficients, because of intra-cluster homogeneity. One way of allowing for clustering in fitting a linear regression model is to use a linear mixed model with two levels. If the estimated intra-cluster...

Persistent link: https://www.econbiz.de/10009457371

Robustness in Latent Variable Models

Huang, Xianzheng - 2006

Statistical models involving latent variables are widely used in many areas of applications, such as biomedical science and social science. When likelihood-based parametric inferential methods are used to make statistical inference, certain distributional assumptions on the latent variables are...

Persistent link: https://www.econbiz.de/10009431306

Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost

Alonso, Andrés; Carbó, José Manuel - 2020

Informes recientes muestran la creciente adopción en el sector financiero de técnicas de aprendizaje automático o machine learning (ML) en la gestión del riesgo de crédito. En este entorno, los supervisores se encuentran ante el reto de permitir que se maximicen las oportunidades derivadas...

Persistent link: https://www.econbiz.de/10012525231

Artificial intelligence in financial services

Fernández Bedoya, Ana

Artículo de revista ; The use of artificial intelligence tools has escalated recently in all sectors of the economy owing, among other factors, to the growing volume of digital data and higher computational capacity. Major benefits may be reaped from applying these tools to the provision of...

Persistent link: https://www.econbiz.de/10012532407

Determination of the optimal number of strata for bias reduction in propensity score matching.

Akers, Allen - 2010

bias in all cases and across all sample sizes. This study investigates bias reduction across varying number of strata and … sample sizes via a large-scale simulation to determine the adequacy of quintiles for bias reduction under all conditions …. Sample sizes ranged from 100 to 50,000 and strata from 3 to 20. Both the percentage of bias reduction and the standardized …

Persistent link: https://www.econbiz.de/10009475025