scholarly journals Forecasting the Basis for Corn in Western New York

1984 ◽  
Vol 13 (1) ◽  
pp. 97-102 ◽  
Author(s):  
Patricia D. Taylor ◽  
William G. Tomek
Keyword(s):  
New York ◽  
Simple Model ◽  
Specific Region ◽  
Explanatory Variables

This study develops a simple model to forecast the basis for corn in a specific region. Improved forecasts can improve hedging decisions. Basis behavior, however, depends on explanatory variables that are themselves difficult to forecast with precision. This limits the usefulness of the basis model, but it does offer some benefit over naive forecasts.

scholarly journals A Rainfall-Based Model for Predicting the Regional Incidence of Wheat Seed Infection by Stagonospora nodorum in New York

2002 ◽  
Vol 92 (5) ◽  
pp. 511-518 ◽  
Author(s):  
Denis A. Shah ◽  
Gary C. Bergstrom
Keyword(s):  
New York ◽  
Simple Model ◽  
Stagonospora Nodorum ◽  
Seed Infection ◽  
Independent Data ◽  
Wheat Seed ◽  
Binomial Probability ◽  
Actual Distribution ◽  
Central New York ◽  
Distribution Parameters

Our goal was to develop a simple model for predicting the incidence of wheat seed infection by Stagonospora nodorum across western and central New York in any given year. The distribution of the incidence of seed infection by S. nodorum across the region was well described by the beta-binomial probability distribution (parameters p and θ). Mean monthly rainfalls in May and in June across western and central New York were used to predict p. The binary power law was used to predict θ. The model was validated with independent data collected from New York. The predicted distribution of seed infection incidence was not statistically different from the actual distribution of the incidence of seed infection.

scholarly journals EXPLORING DRIVING FACTORS OF HIGHER PAID TAXI TRIPS USING ORIGIN–DESTINATION GPS DATA (CASE STUDY: GREEN TAXIS OF NEW YORK CITY)

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 745-748
Author(s):  
P. Mojtabaee ◽  
M. Molavi ◽  
M. Taleai
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Driving Forces ◽  
Influential Factors ◽  
Driving Factors ◽  
Population Number ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
The Government

Abstract. Investigating the influential factors of the areas where people use taxis is a crucial step in understanding the taxi demand dynamics. In this study, we intend to analyze higher-paying taxi trips by putting forward an approach to explore a dataset of green taxi trips in New York City in January 2015 together with some demographic, housing, social and economic data. The final goal is to find out whether the chosen factors are statistically significant to be considered as potential driving forces of demand location for trips with a higher-paid fare. Since airports are major attracting sources for taxi travels, all the steps are taken separately for three scenarios that the trip drop-offs are in 1) LaGuardia Airport, 2) John F Kennedy Airport or 3) other areas. First, the spatial pick-up distribution of these higher-paying trips is mapped to enable visual comparison of the urban movement patterns. Then, taking into account the pick-up density as the response variable, the densities of: foreign-born’s population, number of houses with no vehicles, the private wage and salary workers’ population, the government workers’ population and the self-employed workers’ population in own not incorporate business were considered as the explanatory variables. These variables were examined to find important factors affecting the demand in each neighborhood and different results in each of the three scenarios were discussed. This study gives a better insight into discovering driving factors of higher-paid taxi trips when considering airports as destinations which attract travels with potentially different characteristics.

Diffusion of Broadband Access in Latin America

2011 ◽  
pp. 711-728
Author(s):  
Arturo Robles Rovalo ◽  
Claudio Feijóo González ◽  
José Luis Gómez-Barroso
Keyword(s):  
Latin America ◽  
Digital Divide ◽  
National Level ◽  
Developed Countries ◽  
Specific Region ◽  
Broadband Access ◽  
Geographic Ranges ◽  
Explanatory Variables ◽  
Public Action ◽  
The Difference

The “geographic” digital divide is obvious when comparing more developed countries to the rest. Its first and most obvious sign is the difference in the diffusion of broadband accesses. However, it is clear that there are also lines of separation in smaller geographic ranges: between countries of a same area, inside each country and, sometimes, in each specific region. This chapter shows this situation by studying the broadband access diffusion in Latin America on a three level basis (regional, national, and local). At the national level, a few explanatory variables of the different situations presented by the countries chosen for the study are researched. Additionally, a description of the environment (market and public action) where this diffusion is occurring is also included.

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

2020 ◽  
Vol 2674 (8) ◽  
pp. 145-159 ◽  
Author(s):  
Ehsan Esmaeilzadeh ◽  
Seyedmirsajad Mokhtarimousavi
Keyword(s):  
Machine Learning ◽  
New York ◽  
Impact Analysis ◽  
Travel Demand ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Explanatory Variables ◽  
Explanatory Factors ◽  
Flight Delay ◽  
Delay Prediction

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

America and the world: (still) a work in progress?

2004 ◽  
Vol 30 (3) ◽  
pp. 443-450
Author(s):  
Linda B. Miller
Keyword(s):  
United States ◽  
New York ◽  
Foreign Policy ◽  
Soft Power ◽  
American Foreign Policy ◽  
The United States ◽  
Washington Dc ◽  
American Empire ◽  
Explanatory Variables ◽  
The World

Andrew J. Bacevich, American Empire (Cambridge, MA: Harvard University Press, 2002).Charles Kupchan, The End of the American Era (New York: Alfred Knopf, 2002).Ivo H. Daadler and James M. Lindsay, America Unbound (Washington, DC: Brookings Institution, 2003).Did 11 September 2001 change everything about the United States including its foreign policy? Have the subsequent US-led wars in Afghanistan and Iraq altered the scholarly calculus of what should be studied and how? Must authors determined to assert the continuing importance of history, geopolitics or domestic factors as explanatory variables recast or abandon their existing conclusions to highlight the newer realities after the terrorist attacks and their aftermath? If so, how? These questions lead to others. Is there a usable American past that helps illuminate the dilemmas of the present? If so, where is it found? Is there a sustainable future role for the US in the world, beyond ideology or improvisation? If so, what are its contours? Is the Bush administration truly ‘radical’ or even ‘revolutionary’ in its imperial thrusts? After Afghanistan and Iraq, is American foreign policy still largely a success story? Or is the United States en route to becoming an ordinary country, albeit one with extraordinary resources in both hard and soft power?

scholarly journals Improved prediction of new COVID-19 cases using a simple vector autoregressive model: Evidence from seven New York State counties

2022 ◽  
Author(s):  
Harutaka Takahashi ◽  
Takayoshi Kitaoka
Keyword(s):  
New York ◽  
Seasonal Influenza ◽  
Prediction Models ◽  
New York State ◽  
Epidemiological Studies ◽  
Vector Autoregressive ◽  
York State ◽  
Explanatory Variables ◽  
Rapid Spread ◽  
Model Evidence

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and SARS. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback-they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

scholarly journals STATISTICAL INFERENCE WITH F-STATISTICS WHEN FITTING SIMPLE MODELS TO HIGH-DIMENSIONAL DATA

2021 ◽  
pp. 1-24
Author(s):  
Hannes Leeb ◽  
Lukas Steinberger
Keyword(s):  
Simple Model ◽  
Full Rank ◽  
High Dimensional ◽  
Explanatory Variables ◽  
Relevant Variables ◽  
Subset Regression ◽  
Alpha Beta ◽  
Gaussian Data ◽  
Asymptotic Validity ◽  
F Statistics

Abstract We study linear subset regression in the context of the high-dimensional overall model $y = \vartheta +\theta ' z + \epsilon $ with univariate response y and a d-vector of random regressors z, independent of $\epsilon $ . Here, “high-dimensional” means that the number d of available explanatory variables is much larger than the number n of observations. We consider simple linear submodels where y is regressed on a set of p regressors given by $x = M'z$ , for some $d \times p$ matrix M of full rank $p < n$ . The corresponding simple model, that is, $y=\alpha +\beta ' x + e$ , is usually justified by imposing appropriate restrictions on the unknown parameter $\theta $ in the overall model; otherwise, this simple model can be grossly misspecified in the sense that relevant variables may have been omitted. In this paper, we establish asymptotic validity of the standard F-test on the surrogate parameter $\beta $ , in an appropriate sense, even when the simple model is misspecified, that is, without any restrictions on $\theta $ whatsoever and without assuming Gaussian data.

scholarly journals Common Factors of Stress Change under the First COVID-19 Outbreak as Observed in Four Global Cities

2021 ◽  
Vol 13 (11) ◽  
pp. 5996
Author(s):  
Misato Uehara ◽  
Makoto Fujii ◽  
Kazuki Kobayashi ◽  
Yasuto Hayashi ◽  
Yuki Arai
Keyword(s):  
New York ◽  
Family Relationships ◽  
Stress Change ◽  
Living Alone ◽  
Web Based ◽  
Explanatory Variables ◽  
Outbreak Period ◽  
Stress Changes ◽  
Before And After ◽  
Private Research

Research focusing on stress change comparing before and after being affected by the first COVID-19 outbreak is still limited. This study examined the model between the stress changes during the first COVID-19 outbreak and social attributes (age, sex, occupation, etc.) among residents of four cities around the globe. We obtained 741 valid responses from the residents of London (11.5%), New York (13.8%), Amsterdam (11.7%), and Tokyo (53.4%), through a web-based questionnaire survey conducted in collaboration with a private research firm. We identified 16 statistically significant variables out of 36 explanatory variables, which explained a significant stress change compared to the pre-outbreak period. This result showed that whether living alone or not and the number of times going out for walk or jogging during the first COVID-19 outbreak were the explanatory variables with higher significance for the reduced stress. In addition, those who lived in a place different from their hometowns, who were dissatisfied with their work or their family relationships were more stressed, with statistically significant differences.

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