More Sources of Bias in Half-life Estimation

Biases in measurement of dynamics of time series from calculation of half- life received more attention lately. In particular, this issue amplifies the controversy surrounding the purchasing power parity doctrine. Cross-sectional and temporal aggregations along with mis-specified models were identified before as sources of this bias. We identified a few other sources of bias, namely, sampling error, wrong approximations, and structural breaks in time series. These sources should receive adequate attention for a sound measure of half-life

Maximum Eigenvalue Test for Seasonal Cointegrating Ranks

The maximum eigenvalue (ME) test for seasonal cointegrating ranks is presented using the approach of Cubadda ["Oxford Bulletin of Economics and Statistics" (2001), Vol. 63, pp. 497-511], which is computationally more efficient than that of Johansen and Schaumburg ["Journal of Econometrics" (1999), Vol. 88, pp. 301-339]. The asymptotic distributions of the ME test statistics are obtained for several cases that depend on the nature of deterministic terms. Monte Carlo experiments are conducted to evaluate the relative performances of the proposed ME test and the trace test, and we illustrate these tests using a monthly time series. Copyright 2006 Blackwell Publishing Ltd.

Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods

Abstract We compared the prediction performance of machine learning-based undiagnosed diabetes prediction models with that of traditional statistics-based prediction models. We used the 2014–2020 Korean National Health and Nutrition Examination Survey (KNHANES) (N = 32,827). The KNHANES 2014–2018 data were used as training and internal validation sets and the 2019–2020 data as external validation sets. The receiver operating characteristic curve area under the curve (AUC) was used to compare the prediction performance of the machine learning-based and the traditional statistics-based prediction models. Using sex, age, resting heart rate, and waist circumference as features, the machine learning-based model showed a higher AUC (0.788 vs. 0.740) than that of the traditional statistical-based prediction model. Using sex, age, waist circumference, family history of diabetes, hypertension, alcohol consumption, and smoking status as features, the machine learning-based prediction model showed a higher AUC (0.802 vs. 0.759) than the traditional statistical-based prediction model. The machine learning-based prediction model using features for maximum prediction performance showed a higher AUC (0.819 vs. 0.765) than the traditional statistical-based prediction model. Machine learning-based prediction models using anthropometric and lifestyle measurements may outperform the traditional statistics-based prediction models in predicting undiagnosed diabetes