Evaluating the Prediction Bias Induced by Label Imbalance in Multi-label Classification

This paper examines forecasts developed by financial analysts before and after mergers. The study finds that forecast accuracy decreases sharply after mergers. These accuracy reductions tend to be more pronounced when financial leverage changes, when the merger does not provide earnings or industry diversification, when the purchase method of accounting is used to record the transaction, when capital intensity changes, and when the size of the target corporation is large compared to the size of the acquiring corporation. The data also show that reductions in forecast accuracy after mergers tend to be temporary. Accuracy returns to approximately the premerger level within four years after the merger. The study also finds that overprediction bias increases sharply in the year immediately following the merger. This increase in over-prediction bias, however, is also temporary. Overprediction bias returns to approximately the premerger level within the four-year postmerger study period.

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Exponential-growth prediction bias and compliance with safety measures related to COVID-19

Social Science & Medicine ◽

10.1016/j.socscimed.2020.113473 ◽

2021 ◽

Vol 268 ◽

pp. 113473 ◽

Cited By ~ 1

Author(s):

Ritwik Banerjee ◽

Joydeep Bhattacharya ◽

Priyama Majumdar

Keyword(s):

Exponential Growth ◽

Safety Measures ◽

Growth Prediction ◽

Prediction Bias

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Does exposure prediction bias health-effect estimation? The relationship between confounding adjustment and exposure prediction

Epidemiology ◽

10.1097/ede.0000000000000232 ◽

2015 ◽

Vol 26 (2) ◽

pp. e30

Author(s):

&NA;

Keyword(s):

Health Effect ◽

Prediction Bias ◽

Effect Estimation ◽

Confounding Adjustment ◽

The Relationship

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Development of an Easy-to-Use Prediction Equation for Body Fat Percentage Based on BMI in Overweight and Obese Lebanese Adults

Diagnostics ◽

10.3390/diagnostics10090728 ◽

2020 ◽

Vol 10 (9) ◽

pp. 728

Author(s):

Leila Itani ◽

Hana Tannir ◽

Dana El Masri ◽

Dima Kreidieh ◽

Marwan El Ghoch

Keyword(s):

Body Fat ◽

Model Building ◽

Clinical Importance ◽

Training Sample ◽

Prediction Equation ◽

Accurate Estimation ◽

Body Fat Percentage ◽

Validation Sample ◽

Fat Percentage ◽

Prediction Bias

An accurate estimation of body fat percentage (BF%) in patients who are overweight or obese is of clinical importance. In this study, we aimed to develop an easy-to-use BF% predictive equation based on body mass index (BMI) suitable for individuals in this population. A simplified prediction equation was developed and evaluated for validity using anthropometric measurements from 375 adults of both genders who were overweight or obese. Measurements were taken in the outpatient clinic of the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). A total of 238 participants were used for model building (training sample) and another 137 participants were used for evaluating validity (validation sample). The final predicted model included BMI and sex, with non-significant prediction bias in BF% of −0.017 ± 3.86% (p = 0.946, Cohen’s d = 0.004). Moreover, a Pearson’s correlation between measured and predicted BF% was strongly significant (r = 0.84, p < 0.05). We are presenting a model that accurately predicted BF% in 61% of the validation sample with an absolute percent error less than 10% and non-significant prediction bias (−0.028 ± 4.67%). We suggest the following equations: BF% females = 0.624 × BMI + 21.835 and BF% males = 1.050 × BMI − 4.001 for accurate BF% estimation in patients who are overweight or obese in a clinical setting in Lebanon.

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Affective Forecasting Bias: Liability or Protective Factor?

Journal of Cognitive Psychotherapy ◽

10.1891/0889-8391.32.4.263 ◽

2018 ◽

Vol 32 (4) ◽

pp. 263-271 ◽

Cited By ~ 1

Author(s):

Susan J. Wenze ◽

Kathleen C. Gunthert

Keyword(s):

Life Stress ◽

Protective Factor ◽

Negative Mood ◽

Affective Forecasting ◽

Life Change ◽

Depression And Anxiety ◽

Future Directions ◽

Prediction Bias ◽

Ecological Momentary

We examined whether affective forecasting biases prospectively predict depression and anxiety symptoms in the context of life stress. Participants (n = 72) completed– baseline measures of depression, anxiety, and mood predictions, followed by one week of ecological momentary assessments of mood. Three months later, they completed measures of depression, anxiety, and life stress. Neither positive nor negative mood prediction biases at baseline were associated with follow-up anxiety scores. Positive mood prediction biases were not associated with follow-up depression scores. However, the interaction between negative mood prediction bias and life stress predicted follow-up depression scores. Under conditions of greater life stress, stronger negative mood prediction biases predicted lower follow-up depression scores. Under conditions of positive life change, stronger negative mood prediction biases predicted higher follow-up depression scores. Negative mood prediction bias might serve as a protective or liability factor, depending on levels of stress. Clinical implications and future directions are discussed.

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