• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.

• The Bulletin of The Korean Astronomical Society
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• v.34 no.1
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• pp.76.1-76.1
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• 2009

• Bulletin of the Korean Space Science Society
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• 2009.04a
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• pp.40.1-40.1
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• 2009

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.39-55
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• 2011

People tend to overestimate their emotional reactions to events such as physical handicap and buying a new car in the future. Students overestimate their reactions to a future grade as compared to their reactions after receiving the grade. Impact bias refers to people's tendency to overestimate the intensity and the duration of emotional reactions to a future event. The present study explored whether impact bias occurs to risky objects such as nuclear energy, genetically engineered food, and mobile phone. Participants were asked to predict their emotional reactions at three time points, that is, at the present, a week after, and a year after. They predicted their reactions before and after two debiasing tasks. The present study demonstrated a different pattern of impact bias at three time points: A largest bias was observed a week after the present. A defocalism manipulation has eliminated the impact bias whereas an adaptation manipulation has not. Several points were discussed regarding the difference between the previous and the present work.

• Korean Journal of Forensic Psychology
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• v.11 no.1
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• pp.63-87
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• 2020

Judgment and decision-making studies have shown that people are easily influenced and biased by information irrelevant to the object of judgment. There is a great deal of research that indicates that bias exists in the legal judgment scene. One of them is a bias induced by defendants' character evidence. This study examined whether cognitive activities such as discussion, counterfactual thinking, and peer assessment could reduce the bias induced by the character evidece. In Experiment 1, 121 college students were asked to give the percentage they believed the defendant to be guilty. There was no cognitive activity for the control group. There were three different cognitive activities for the experimental group: discussion, counterfactual thinking and discussion, and counterfactual thinking and peer assessment. Results showed reduction in bias for all the experimental groups, and there was no difference between them. In Experiment 2, there were 125 participants from general population for the same procedure as in Experiment 1. Results showed reduction in bias only for the counterfactual thinking and discussion group. In general discussion, we speculated the implication of the results and the reason for the difference between the two experiments.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.51-56
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• 2006

Biases embedded in numerical weather precipitation forecasts by the RDAPS model was determined, quantified and corrected. The ultimate objective is to eventually enhance the reliability of reservoir operation by Korean Water Resources Corporation (KOWACO), which is based on precipitation-driven forecasts of stream flow. Statistical post-processing, so called MOS (Model Output Statistics) was applied to RDAPS to improve their performance. The Artificial Neural Nwetwork (ANN) model was applied for 4 cases of 'Probability of Precipitation (PoP) for wet and dry season' and 'Quantitative Precipitation Forecasts (QPF) for wet and dry season'. The reduction on the large systematic bias was especially remarkable. The performance of both networks may be improved by retraining, probably every month. In addition, it is expected that performance of the networks will improve once atmospheric profile data are incorporated in the analysis. The key to the optimal performance of ANN is to have a large data set relevant to the predictand variable. The more complex the process to be modeled by the ANN, the larger the data set needs to be.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.49.3-50
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• 2016

Deep Ecliptic Patrol of the Southern Sky (DEEP-South) observations have been conducted officially during the off-season for exoplanet search since October 2015. Most of the allocated time for DEEP-South is devoted to targeted photometry, Opposition Census (OC), of Near Earth Asteroids (NEAs) to increase the number of such objects with known physical properties. It is efficiently achieved by multiband, time series photometry. This Opposition Census (OC) mode target objects near their opposition, with km-sized PHAs in the early stage and goes down to sub-km objects. Continuous monitoring of the sky with KMTNet is optimized for spin characterization of various kinds of asteroids, including binaries, satellites, slow/fast- and non-principal axis-rotators, and hence is expected to facilitate the debiasing of previously reported lightcurve observations. We present the preliminary lightcurves of NEAs from year one of the DEEP-South with our long term plan.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.42.1-42.1
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• 2018

Deep Ecliptic Patrol of the Southern Sky (DEEP-South) observation is being made during the off-season for exoplanet survey, using Korea Microlensing Telescope Network (KMTNet). An optimal combination of its prime focus optics and the 0.3 billion pixel CCD provides a four square degrees field of view with 0.4 arcsec/pixel plate scale which is also best suited for small body studies. Normal operation of KMTNet started in October 2015, and a significant portion of the allocated telescope time for DEEP-South is dedicated to targeted observation, Opposition Census (OC), of near-Earth asteroids for physical and taxonomic characterization. This is effectively achieved through multiband, time series photometry using Johnson-Cousins BVRI filters. Uninterrupted monitoring of the southern sky with KMTNet is optimized for spin characterization of a broad spectrum of asteroids ranging from the near-Earth space to the main-belt, including binaries, asteroids with satellites, slow/fast- and non-principal axis-rotators, and thus is expected to facilitate the debiasing of previously reported lightcurve observations. Our software subsystem consists of an automated observation scheduler, a pipelined data processing system for differential photometry, and an easy-to-use lightcurve analysis toolkit. Lightcurves, spin periods and provisional determination of class of asteroids to which the lightcurve belongs will be presented, using the dataset from first year operation of KMTNet. Our new taxonomic classification scheme for asteroids will also be summarized.

• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.935-946
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• 2016

This study aims to explore cognitive biases relating the core competences of science and instructional strategy in reducing the level of cognitive biases. The literature review method was used to explore cognitive biases and science education experts discussed the relevance of cognitive biases to science education. Twenty nine cognitive biases were categorized into five groups (limiting rational causal inference, limiting diverse information search, limiting self-regulated learning, limiting self-directed decision making, and category-limited thinking). The cognitive biases in limiting rational causal inference group are teleological thinking, availability heuristic, illusory correlation, and clustering illusion. The cognitive biases in limiting diverse information search group are selective perception, experimenter bias, confirmation bias, mere thought effect, attentional bias, belief bias, pragmatic fallacy, functional fixedness, and framing effect. The cognitive biases in limiting self-regulated learning group are overconfidence bias, better-than-average bias, planning fallacy, fundamental attribution error, Dunning-Kruger effect, hindsight bias, and blind-spot bias. The cognitive biases in limiting self-directed decision-making group are acquiescence effect, bandwagon effect, group-think, appeal to authority bias, and information bias. Lastly, the cognitive biases in category-limited thinking group are psychological essentialism, stereotyping, anthropomorphism, and outgroup homogeneity bias. The instructional strategy to reduce the level of cognitive biases is disused based on the psychological characters of cognitive biases reviewed in this study and related science education methods.