New York University

About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.

Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)

Abstract: This study analyzes the comparison between traditional statistical methodologies for distress classification and prediction, i.e., linear discriminant (LDA) or logit analyses, with an artificial intelligence algorithm known as neural networks (NN). Analyzing well over 1,000 healthy, vulnerable and unsound industrial Italian firms from 1982–1992, this study was carried out at the Centrale dei Bilanci in Turin, Italy and is now being tested in actual diagnostic situations. The results are part of a larger effort involving separate models for industrial, retailing/trading and construction firms. The results indicate a balanced degree of accuracy and other beneficial characteristics between LDA and NN. We are particularly careful to point out the problems of the ‘black-box’ NN systems, including illogical weightings of the indicators and overfitting in the training stage both of which negatively impacts predictive accuracy. Both types of diagnoslic techniques displayed acceptable, over 90%, classificalion and holdoul sample accuracy and the study concludes that there certainly should be further studies and tests using the two lechniques and suggests a combined approach for predictive reinforcement.

Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

Abstract: With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how diff erent factors a ffect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. Further, reviews that rate products negatively can be associated with increased product sales when the review text is informative and detailed.By using Random Forest based classi ers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: 'reviewer-related' features, 'review subjectivity' features, and 'review readability' features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.

A new algorithm in spectral analysis and band-limited extrapolation

Abstract: If only a segment of a function f (t) is given, then its Fourier spectrum F(\omega) is estimated either as the transform of the product of f(t) with a time-limited window w(t) , or by certain techniques based on various a priori assumptions. In the following, a new algorithm is proposed for computing the transform of a band-limited function. The algorithm is a simple iteration involving only the fast Fourier transform (FFT). The effect of noise and the error due to aliasing are determined and it is shown that they can be controlled by early termination of the iteration. The proposed method can also be used to extrapolate bandlimited functions.

Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women.

Abstract: Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. Background: Obesity is associated with increased breast cancer risk among postmenopausal women. We examined whether this association could be explained by the relationship of body mass index (BMI) with serum sex hormone concentrations. Methods: We analyzed individual data from eight prospective studies of postmenopausal women. Data on BMI and prediagnostic estradiol levels were available for 624 case subjects and 1669 control subjects; data on the other sex hormones were available for fewer subjects. The relative risks (RRs) with 95% confidence intervals (CIs) of breast cancer associated with increasing BMI were estimated by conditional logistic regression on case- control sets, matched within each study for age and recruitment date, and adjusted for parity. All statistical tests were two- sided. Results: Breast cancer risk increased with increasing BMI (P-trend = .002), and this increase in RR was substantially reduced by adjustment for serum estrogen concentrations. Adjusting for free estradiol reduced the RR for breast cancer associated with a 5 kg/m(2) increase in BMI from 1.19 (95% CI = 1.05 to 1.34) to 1.02 (95% CI = 0.89 to 1.17). The increased risk was also substantially reduced after adjusting for other estrogens (total estradiol, non-sex hormone-binding globulin- bound estradiol, estrone, and estrone sulfate), and moderately reduced after adjusting for sex hormone-binding globulin, whereas adjustment for the androgens (androstenedione, dehydroepiandrosterone, dehydroepiandrosterone sulfate, and testosterone) had little effect on the excess risk. Conclusion: The results are compatible with the hypothesis that the increase in breast cancer risk with increasing BMI among postmenopausal women is largely the result of the associated increase in estrogens, particularly bioavailable estradiol.

Power and Perspectives Not Taken

Abstract: Four experiments and a correlational study explored the relationship between power and perspective taking. In Experiment 1, participants primed with high power were more likely than those primed with low power to draw an E on their forehead in a self-oriented direction, demonstrating less of an inclination to spontaneously adopt another person's visual perspective. In Experiments 2a and 2b, high-power participants were less likely than low-power participants to take into account that other people did not possess their privileged knowledge, a result suggesting that power leads individuals to anchor too heavily on their own vantage point, insufficiently adjusting to others' perspectives. In Experiment 3, high-power participants were less accurate than control participants in determining other people's emotion expressions; these results suggest a power-induced impediment to experiencing empathy. An additional study found a negative relationship between individual difference measures of power and perspective taking. Across these studies, power was associated with a reduced tendency to comprehend how other people see, think, and feel.