TY - JOUR
T1 - An extension of play against the random past strategy. Choosing the right experts on IBM forecasts
AU - Li, Mingfei
PY - 2014
Y1 - 2014
N2 - In sequential plays with two players, the players have the opportunity to use information on opponents’ past moves in selecting a move for the current stage. Strategies for Player II are considered in our study, in particular, play against the random past (PRP) strategy. In this paper, PRP strategy will be reviewed and discussed. Hannan consistency of PRP strategy in term of regret (difference in average loss and an envelope loss) on k-extended Bayes envelope risk problem in matching binary bits game will be shown. The simulation of two-experts selection problem on real experts’ forecasting data of IBM share earnings confirms the consistency of PRP strategy.
AB - In sequential plays with two players, the players have the opportunity to use information on opponents’ past moves in selecting a move for the current stage. Strategies for Player II are considered in our study, in particular, play against the random past (PRP) strategy. In this paper, PRP strategy will be reviewed and discussed. Hannan consistency of PRP strategy in term of regret (difference in average loss and an envelope loss) on k-extended Bayes envelope risk problem in matching binary bits game will be shown. The simulation of two-experts selection problem on real experts’ forecasting data of IBM share earnings confirms the consistency of PRP strategy.
UR - https://dx.doi.org/10.1080/00949655.2013.823963
U2 - 10.1080/00949655.2013.823963
DO - 10.1080/00949655.2013.823963
M3 - Article
VL - 84
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - Issue 12
ER -