TY - HEAR
T1 - Early Detection of Placement for Success in an Online Quantitative Class
Y1 - 2016
A1 - Hsieh,Ping-Hung
A1 - Chang,Xiaohui
A1 - Olstad,Andrew
KW - MBA
KW - Supply Chain
JA - Joint Statistical Meetings
CY - Chicago, IL
U2 - c
U4 - 144709447680
ID - 144709447680
ER -
TY - CONF
T1 - Estimation and Visualization of Digital Library Content Similarities
T2 - Intern. Conf. on Inf. Systems (ICIS) 2015
Y1 - 2015
A1 - Reitsma,Reindert
A1 - Hsieh,Ping-Hung
A1 - Robson,Robby
KW - BIS
KW - MBA
KW - Supply Chain
AB - We report on a process for similarity estimation and two-dimensional mapping of lesson materials stored in a Web-based K12 Science, Technology, Engineering and Mathematics (STEM) digital library. The process starts with automated removal of all information which should not be included in the similarity estimations followed by automated indexing. Similarity estimation itself is conducted through a natural language processing algorithm which heavily relies on bigrams. The resulting similarities are then used to compute a Sammon-map; i.e., a projection in n dimensions, the item-to-item distances of which best reflect the input similarities. In this paper we concentrate on specification and validation of this process. The similarity results show almost 100% precision-by-rank in the top three to five ranks. Sammon mapping in two dimensions corresponds well with the digital libraryâ€˜s table of content.
JA - Intern. Conf. on Inf. Systems (ICIS) 2015
U2 - b
U4 - 105616928768
ID - 105616928768
ER -
TY - JOUR
T1 - Extreme Value Analysis for Partitioned Insurance Loss
JF - Variance
Y1 - 2009
A1 - Henry III,John B.
A1 - Hsieh,Ping-Hung
KW - MBA
KW - Supply Chain
AB - The heavy-tailed nature of insurance claims requires that special attention be put into the analysis of the tail behavior of a loss distribution. It has been demonstrated that the distribution of large claims of several lines of insurance have Pareto-type tails. As a result, estimating the tail index, which is a measure of the heavy-tailedness of a distribution, has received a great deal of attention. Although numerous tail index estimators have been proposed in the literature, many of them require detailed knowledge of individual losses and are thus inappropriate for insurance data in partitioned form. In this study we bridge this gap by developing a tail index estimator suitable for partitioned loss data. This estimator is robust in the sense that no particular global density is assumed for the loss distribution. Instead we focus only on fitting the model in the tail of the distribution where it is believed that the Pareto-type form holds. Strengths and weaknesses of the proposed estimator are explored through simulation and an application of the estimator to real world partitioned insurance data is given.
VL - 3
CP - 2
U2 - a
U4 - 22474360833
ID - 22474360833
ER -
TY - HEAR
T1 - On Examining Asymmetric Behavior of Price Limit Moves
Y1 - 2004
A1 - Hsieh,Ping-Hung
A1 - Yang,J. Jimmy
KW - MBA
KW - Supply Chain
JA - National Joint Statistical Meetings
CY - Toronto, Canada
U2 - c
U4 - 646834176
ID - 646834176
ER -
TY - JOUR
T1 - An Exploratory First Step in Teletraffic Data Modeling: Evaluation of Long-run Performance of Parameter Estimators
JF - Computational Statistics and Data Analysis
Y1 - 2002
A1 - Hsieh,Ping-Hung
KW - MBA
KW - Supply Chain
AB - Examination of the tail behavior of a distribution F that generates teletraffic measurements is an important first step toward building a network model that explains the link between heavy tails and long-range dependence exhibited in such data. When knowledge of the tail behavior of F is vague, the family of the generalized Pareto distributions (GPDs) can be used to approximate the tail probability of F, and the value of its shape parameter characterizes the tail behavior. To detect tail behavior of F between two host computers on a network, the estimation procedure must be carried out over all possible combinations of host computers, and thus, the performance of the estimator under repeated use becomes the primary concern. In this article, we evaluate the long-run performance of several existing estimation procedures and propose a Bayes estimator to overcome some of the shortcomings. The conditions in which the procedures perform well in the long run are reported, and a simple rule of thumb for choosing an appropriate estimator for the task of repeated estimation is recommended.
VL - 40
CP - 2
U2 - a
U4 - 646813696
ID - 646813696
ER -