A network centrality method for the rating problem

We propose a new method for aggregating the information of multiple reviewers rating multiple products. Our approach is based on the network relations induced between products by the rating activity of the reviewers. We show that our method is algorithmically implementable even for large numbers of both products and consumers, as is the case for many online sites. Moreover, comparing it with the simple average, which is mostly used in practice, and with other methods previously proposed in the literature, it performs very well under various dimension, proving itself to be an optimal trade--off between computational efficiency, accordance with the reviewers original orderings, and robustness with respect to the inclusion of systematically biased reports.Comment: 25 pages, 8 figure

Use and Evaluation of Statistical Methods for Personalized Medicine in Oncology

The goal of personalized medicine is to give the right treatment to the right patient at the right dose using all we know about the patient. With the increasing availability of biomarkers and prediction models, there is the potential for individualized treatment based on patient specific factors. There are many statistical challenges associated with achieving this goal. One is how to develop and assess good predictions models. Another is how to define a criteria for an optimal treatment when there are multiple outcomes and then how to analyze available data to determine the optimal treatment for each future patient. In Chapter II, we consider the assessment of prediction models using data with missing biomarker values. We propose inverse probability weighted (IPW) and augmented inverse probability weighted (AIPW) estimates of the area under the ROC curve (AUC) and Brier Score to handle the missing data. AIPW is a double-robust method that is robust to the misspecification of either a model for the missingness mechanism or a model for the distribution of the missing variable. We evaluated the performance of IPW and AIPW in comparison with multiple imputation (MI) in simulation studies under missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) scenarios. We illustrate these methods using an example from prostate cancer. In Chapters III and IV we consider the setting where there is an existing dataset of patients treated with heterogeneous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. In Chapter III, we propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose-biomarker interactions. To incorporate the large number of potential biomarkers, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy. In Chapter IV, we extend the approach of Chapter III and propose to use flexible machine learning methods such as random forests and Gaussian processes to build models for efficacy and toxicity depending on the dose and biomarkers. In addition, we allow for dependence between efficacy and toxicity. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non-decreasing dose-efficacy and dose-toxicity relationships. Numerical utilities are assigned to each potential outcome pair, which allow the improvement in the utility due to a change in efficacy to depend on the level of toxicity. For each patient, the optimal dose is chosen to maximize the utility function or the posterior mean of the utility function. We further adjust the utility function with more constraints to incorporate clinical requirements, and consider the uncertainty in the estimation of the utility function in the optimal dose selection. The various models and methods are evaluated in a simulation study and illustrated using data from a lung cancer study.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163037/1/pinli_1.pd

Quantifying the Contribution of Mean Flow and Eddy Advection to the Warm SST Bias in the Southeast Tropical Atlantic Region

In current-generation climate models, the warm sea surface temperature (SST) bias problem is most commonly seen in the eastern boundary upwelling systems (EBUSs), and is most pronounced and most prevalent in the Southeast Tropical Atlantic (SETA) region. Previous studies have shown that the coastal wind pattern in this region, namely the Benguela low-level coastal jet (BLLCJ), is of great importance for the generation of such SST bias, because the coastal ocean circulation is highly sensitive to the off-shore structure of the wind forcing. Using an eddy-resolving regional ocean model, we first show that the SST bias in the region is drastically reduced when forced with simulated winds from a high-resolution regional atmospheric model. We subsequently demonstrate that the SST bias is highly sensitive to the spatial structure of the wind stress curl (WSC). We also find that when the ocean model is forced by a realistic high-resolution wind, the ocean model resolution is of second order importance in reducing the SST bias. Furthermore, we use a double-time average (DTA) method to quantify the contribution of heat budget terms, and show that the horizontal advection contributes significantly to the SST bias. We then examined the question: To what extent do ocean eddies play a role in balancing the coastal ocean heat budget and affecting the SST bias? By experimenting with a submesoscale eddy-permitting regional ocean model, we show that ocean eddies in the Southeast Tropical Atlantic region are most energetic near the Angola-Benguela Front (ABF), the Lüderitz Upwelling Cell region and the Agulhas Leakage region. In these three regions, comparisons between the two model simulations forced with the low- vs high-resolution winds suggest that the SST bias is mainly generated by mean flow advection with ocean eddies playing the role of counteracting the warming induced by the mean flow advection in this region

Domestic Bargaining in Taiwan\u27s International Agricultural Negotiations

Development literature often depicts development as a transitional process in which agriculture is marginalized while resources are transferred to growing nonfarm sectors, through either voluntary exchange or involuntary coercion. The conventional analysis of Taiwan\u27s agriculture and its role in economic development generally attests to this model.-1 Prior to the 1970s, as the backbone of Taiwan\u27s economy and the major earner of foreign currencies, agriculture accounted for more than 40% of the total employment and over 20% of the net domestic product (NDP). But a series of programs unfavorable to farmers were put in place so that the government could channel resources into industrial development. For instance, rice farmers were asked to pay for fertilizer with rice at exchange ratios consistently unfavorable to them, and the government\u27s mandatory purchase scheme bought rice from farmers at 20% to 30% below prevailing market prices. Through these mechanisms, productivity gains achieved after Taiwan\u27s land reforms (1949- 53) were taken by the government, which subsequently redirected them into the emerging industrial sector as investment capital. In addition, the abundant supply of food and labor from rural areas also provided an environment conducive to low-wage, labor-intensive industrializatio

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201