Forecasting Patent Filings at the European Patent Office (EPO) with a Dynamic Log Linear Regression Model: Applications and Extensions

2016 ◽  
pp. 63-83
Author(s):  
Peter Hingley ◽  
Walter Park
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
European Patent Office ◽  
European Patent ◽  
Patent Office ◽  
Log Linear

A new bounded log-linear regression model

Metrika ◽  
2013 ◽  
Vol 77 (5) ◽  
pp. 695-720 ◽  
Author(s):  
HaiYing Wang ◽  
Nancy Flournoy ◽  
Eloi Kpamegan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Log Linear

Poisson Log-Linear Regression Model for Rural Signalized Intersection

2012 ◽  
Vol 594-597 ◽  
pp. 1391-1394
Author(s):  
Zhi Ping Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Traffic Safety ◽  
Linear Regression Model ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Left Turn ◽  
Traffic Volumes ◽  
Input Variables ◽  
Log Linear

Safety performance of rural signalized intersections is critical for identifying high-risk sites and predicting the hazardousness. This paper aims to develop a predictive model that will describe the safety of rural signalized intersections based on various input variables. Data are examined from 124 rural signalized intersections over three states, and Poisson log-linear regression model is presented, which connected traffic number and the average traffic volumes, geometric characteristics and signalization characteristics variables together. The model and associated data analysis reveal that average daily traffic, media width, speed limit, degree of horizontal curvature and left-turn lane are the factors that have greatest overall effect on safety. The results show that the Poisson log-linear regression model is able to describe the rural signalized intersection safety accurately. Using this model, effective countermeasures can be applied for improving traffic safety.

scholarly journals ANALISIS JUMLAH KENDARAAN BERMOTOR DI DAERAH ISTIMEWA YOGYAKARTA (1990–2012)

KINERJA ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 68
Author(s):  
I Agus Wantara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Traffic Congestion ◽  
Ordinary Least Squares ◽  
T Test ◽  
Estimation Technique ◽  
The People ◽  
Log Linear

In the last few years, traffic congestions are often occurred in Yogyakarta. This situation is caused by the increasing number of vehicles in Yogyakarta.This study evaluates the effect of the gross regional domesticproduct (PDRB), the people of Daerah Istimewa Yogyakarta (JP), and region income (PD) to the number of vehicles in Daerah Istimewa Yogyakarta (JKB). The model consists of one behavioral equation: the number of vehicles equation. The estimation technique uses Ordinary Least Squares (OLS). MacKinnon, White, and Davidson test (MWD test) is used to choose between the two models: linear regression model or log-linearregression model.The sample covers observations for 23 years (1990 - 2012). The data are obtained from (1) Bank Indonesia (2) Badan Pusat Statistik DIY and various other sources. It is found that individually lnJP andlnPD are statistically significant (positive) except ln PDRB on the basis of (separate) t test. It is also found that on the basis of the F test collectively all the regressors have a significant effect on the regressand lnJKB.Keywords: the number of vehicles, traffic congestion, linear regression model, log-linear regression model.

scholarly journals A statistical analysis of the novel coronavirus (COVID-19) in Italy and Spain

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249037
Author(s):  
Jeffrey Chu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Basic Reproduction Number ◽  
Linear Regression Model ◽  
Reproduction Number ◽  
Early Stage ◽  
The Novel ◽  
Novel Coronavirus ◽  
Log Linear ◽  
The Basic Reproduction Number

The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of life as we know it. This paper focuses on the incidence of the disease in Italy and Spain—two of the first and most affected European countries. Using two simple mathematical epidemiological models—the Susceptible-Infectious-Recovered model and the log-linear regression model, we model the daily and cumulative incidence of COVID-19 in the two countries during the early stage of the outbreak, and compute estimates for basic measures of the infectiousness of the disease including the basic reproduction number, growth rate, and doubling time. Estimates of the basic reproduction number were found to be larger than 1 in both countries, with values being between 2 and 3 for Italy, and 2.5 and 4 for Spain. Estimates were also computed for the more dynamic effective reproduction number, which showed that since the first cases were confirmed in the respective countries the severity has generally been decreasing. The predictive ability of the log-linear regression model was found to give a better fit and simple estimates of the daily incidence for both countries were computed.

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