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Institution

University of Nebraska Omaha

EducationOmaha, Nebraska, United States
About: University of Nebraska Omaha is a education organization based out in Omaha, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4526 authors who have published 8905 publications receiving 213914 citations. The organization is also known as: UNO & University of Omaha.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors employ quantile impulse-response functions obtained from multivariate quantile models to analyze the impact of US policy and US equity market uncertainties on not only domestic stock returns, but also stock returns of mature and emerging markets.

86 citations

Journal ArticleDOI
TL;DR: In this article, the authors re-examine the predictability of real stock prices based on price-dividend and price-earnings ratios and find significant evidence of increased long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at short horizons but can be rejected based on bootstrapped critical values constructed from linear representations of the data.
Abstract: Using annual data for 1872-1997, this paper re-examines the predictability of real stock prices based on price-dividend and price-earnings ratios. In line with the extant literature, we find significant evidence of increased long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at short horizons but can be rejected at longer horizons based on bootstrapped critical values constructed from linear representations of the data. While increased statistical power at long horizons in finite samples provides a possible explanation for the pattern of predictability in the data, we find via Monte Carlo simulations that the power to detect predictability in finite samples does not increase at long horizons in a linear framework. An alternative explanation for the pattern of predictability in the data is nonlinearities in the underlying data-generating process. We consider exponential smooth-transition autoregressive models of the price-dividend and priceearnings ratios and their ability to explain the pattern of stock price predictability in the data.

86 citations

Journal ArticleDOI
TL;DR: Data support the idea that cell migration can be monitored in vivo and provides an opportunity to assess monocyte mobility in brain and its affects on neurodegenerative processes and notably HAD.
Abstract: Inflammatory cells, most notably mononuclear phagocytes (MP; macrophages and microglia), play a critical role in brain homeostasis, repair and disease. One important event in cellular biodynamics is how MP move in and throughout the nervous system. Prior studies have focused principally on cell migration across the blood-brain barrier during neuroinflammatory processes with little work done on cell movement within the brain. During the past decade our laboratories have studied the role of MP in HIV-1-associated dementia (HAD). In HAD MP incite sustained glial inflammatory reactions causing significant neuronal damage. To extend these works we investigated cell movement in brain and its influence for disease in a novel co-registration system integrating neuropathology with high-field magnetic resonance imaging (MRI). Human monocytes labeled with superparamagnetic iron oxide particles were injected into the brain of severe combined immunodeficient (SCID) mice. MRI was recorded 1, 7, and 14 days after cell injection. MRI co-registered with histology verified that the MRI signal modification was due to the labeled cells. MRI showed human monocyte-derived macrophages along the injection site, the corpus callosum, the ventricular system and in other brain sites. These data support the idea that cell migration can be monitored in vivo and provides an opportunity to assess monocyte mobility in brain and its affects on neurodegenerative processes and notably HAD.

85 citations

Journal ArticleDOI
TL;DR: The EHC provided higher quality retrospective reports for cohabitation, employment, unemployment, and smoking histories; the CQ provided better data quality for marriage history, although what variable was being measured, instead of which method was being used, had the biggest impact on differences in data quality.
Abstract: Six hundred and twenty-six participants from the Panel Study of Income Dynamics (PSID) were interviewed via telephone ret- rospectively about life course events by either computer-assisted (CATI) event history calendar (EHC) or standardized CATI conventional ques- tionnaire (CQ) methods, randomly assigned. Experimental retrospective reports, for a reference period up to 30 years, were validated against reports provided annually from the PSID core interviews. Data quality outcome measures included variables associated with marriage, cohab- itation, employment, unemployment, residential changes, and cigarette smoking. The EHC provided higher quality retrospective reports for co- habitation, employment, unemployment, and smoking histories; the CQ provided better data quality for marriage history, although what variable was being measured, instead of which method was being used, had the biggest impact on differences in data quality. Both EHC and CQ inter- views lasted on average around one hour, with the EHC interviews being on average 10 percent longer. Interviewers preferred the EHC interviews. In both EHC and CQ conditions, respondents generally enjoyed the in- terviews, and did not find questions difficult. The costs and benefits of

85 citations

Journal ArticleDOI
TL;DR: The goal of this paper is to present a multi-criteria mathematical programming (MCMP) model for multi-class classification, and the concept of e-support vector to facilitate computation of large-scale applications.

85 citations


Authors

Showing all 4588 results

NameH-indexPapersCitations
Darell D. Bigner13081990558
Dan L. Longo12569756085
William B. Dobyns10543038956
Eamonn Martin Quigley10368539585
Howard E. Gendelman10156739460
Alexander V. Kabanov9944734519
Douglas T. Fearon9427835140
Dapeng Yu9474533613
John E. Wagner9448835586
Zbigniew K. Wszolek9357639943
Surinder K. Batra8756430653
Frank L. Graham8525539619
Jing Zhou8453337101
Manish Sharma82140733361
Peter F. Wright7725221498
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202323
2022108
2021585
2020537
2019492
2018421