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Regression with Partially Observed Ranks on a Covariate: Distribution-Guided Scores for Ranks
부분적으로 관측된 순위 공변량을 이용한 회귀분석: 순위에 대한 분포-유도 스코어 함수

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Authors
김윤응
Advisor
임요한
Major
자연과학대학 통계학과
Issue Date
2016-08
Publisher
서울대학교 대학원
Keywords
Concomitant variableinvestor attentionlinear regression modelmoments of order statisticspartially observed ranksscores of ranks.
Description
학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2016. 8. 임요한.
Abstract
This work is motivated by a hand-collected data set from one of the largest
Internet portals in Korea. This data set records the top 30 most frequently
discussed stocks on its on-line message board. The frequencies are considered
to measure the attention paid by investors to individual stocks. The empirical
goal of the data analysis is to investigate the effect of this attention on
trading behavior. For this purpose, we regress the (next day) returns and
the (partially) observed ranks of frequencies. In the regression, the ranks
are transformed into scores, for which purpose the identity or linear scores
are commonly used. In this thesis, we propose a new class of scores (a
score function) that is based on the moments of order statistics of a random
variable Z. The new scores are shown to be
flexible in modeling the desired
features (e.g., monotonicity or convexity) of the scores. In addition, if the true
covariate X is drawn from a location-scale family and Z is its standardized
distribution, then the least-squares estimator calculated using the proposed
scores consistently estimates the true correlation between the response and
the covariate and asymptotically approaches the normal distribution. We also
propose a procedure for diagnosing a given score function and selecting one
that is better suited to the data. We numerically demonstrate the advantage
of using a correctly specifed score function over that of the identity scores (or
other misspecifed scores) in estimating the correlation coefficient. Finally, we
apply our proposal to test the effects of investors' attention on their returns
using the motivating data set.
Language
English
URI
https://hdl.handle.net/10371/121163
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Ph.D. / Sc.D._통계학과)
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