00558nas a2200145   4500008004100000245007700041210006900118260002200187653002300209653001700232100002100249700001900270700001900289856010400308       2016                        eng d00aEarly Detection of Placement for Success in an Online Quantitative Class0 aEarly Detection of Placement for Success in an Online Quantitati  aChicago, ILc201610aBusiness Analytics10aSupply Chain1 aHsieh, Ping-Hung1 aChang, Xiaohui1 aOlstad, Andrew  uhttps://business.oregonstate.edu/biblio/early-detection-placement-success-online-quantitative-class01461nas a2200157   4500008004100000245007300041210006900114260000900183520091500192653000801107653001701115100002201132700002101154700001801175856011001193       2015                        eng d00aEstimation and Visualization of Digital Library Content Similarities0 aEstimation and Visualization of Digital Library Content Similari  c20153 aWe 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 library‘s table of content.10aBIS10aSupply Chain1 aReitsma, Reindert1 aHsieh, Ping-Hung1 aRobson, Robby  uhttps://business.oregonstate.edu/biblio/estimation-and-visualization-digital-library-content-similarities01632nas a2200157   4500008004100000245005800041210005800099260000900157300001400166490000600180520113000186653001701316100002401333700002101357856009601378       2009                        eng d00aExtreme Value Analysis for Partitioned Insurance Loss0 aExtreme Value Analysis for Partitioned Insurance Loss  c2009  a214 - 2380 v33 aThe 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.10aSupply Chain1 aIII, John, B. Henry1 aHsieh, Ping-Hung  uhttps://business.oregonstate.edu/biblio/extreme-value-analysis-partitioned-insurance-loss-000450nas a2200121   4500008004100000245005800041210005500099260002600154653001700180100002100197700001600218856009400234       2004                        eng d00aOn Examining Asymmetric Behavior of Price Limit Moves0 aExamining Asymmetric Behavior of Price Limit Moves  aToronto, Canadac200410aSupply Chain1 aHsieh, Ping-Hung1 aYang, Jimmy  uhttps://business.oregonstate.edu/biblio/examining-asymmetric-behavior-price-limit-moves-001679nas a2200145   4500008004100000245011900041210006900160260000900229300001200238490000700250520110100257653001701358100002101375856013701396       2002                        eng d00aAn Exploratory First Step in Teletraffic Data Modeling: Evaluation of Long-run Performance of Parameter Estimators0 aExploratory First Step in Teletraffic Data Modeling Evaluation o  c2002  a263-2830 v403 aExamination 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.10aSupply Chain1 aHsieh, Ping-Hung  uhttps://business.oregonstate.edu/biblio/exploratory-first-step-teletraffic-data-modeling-evaluation-long-run-performance-parameter-0