• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 한국조사연구학회지:조사연구
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• 제9권1호
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• pp.87-100
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• 2008

200여개의 지역구 선거가 동시에 치러지는 국회의원 선거에서 주요정당의 의석 수를 예상해야 할 필요가 있는데, 이제까지는 정당별로 당선확실 선거구 수에 경합 선거구 수를 적당히 더하는 상식적 수준의 셈에 의존하여 왔다. 그러나 선거 예측 조사 자료를 베이즈 추론의 틀에 넣어 활용함으로써 정당 의석 수에 대한 합리적 점 예측과 구간 예측이 가능하다. 2004년의 제 17대 국회의원 선거에 적용하여 이 방법의 실용성을 살펴보았다.