Modeling Burglar Incidents Data Using Generalized and Quasi Poisson Regression Models: A Case Study of Nairobi City County, Kenya

This paper examines the validity of some stylized statements that can be found in the actuarial literature about random effects models. Specifically, the actual meaning of the estimated parameters and the nature of the residual heterogeneity are discussed. A numerical illustration performed on a Belgian motor third party liability portfolio supports this discussion.

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Fixed versus Random Effects in Poisson Regression Models for Claim Counts: A Case Study with Motor Insurance

Astin Bulletin ◽

10.2143/ast.36.1.2014153 ◽

2006 ◽

Vol 36 (01) ◽

pp. 285-301 ◽

Cited By ~ 12

Author(s):

Jean-Philippe Boucher ◽

Michel Denuit

Keyword(s):

Random Effects ◽

Poisson Regression ◽

Regression Models ◽

Third Party ◽

Numerical Illustration ◽

Random Effects Models ◽

Estimated Parameters ◽

Motor Insurance ◽

Actual Meaning

This paper examines the validity of some stylized statements that can be found in the actuarial literature about random effects models. Specifically, the actual meaning of the estimated parameters and the nature of the residual heterogeneity are discussed. A numerical illustration performed on a Belgian motor third party liability portfolio supports this discussion.

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Anticipated help-seeking for cancer symptoms before and after the coronavirus pandemic: results from the Onco-barometer population survey in Spain

British Journal of Cancer ◽

10.1038/s41416-021-01382-1 ◽

2021 ◽

Author(s):

Dafina Petrova ◽

Marina Pollán ◽

Miguel Rodriguez-Barranco ◽

Dunia Garrido ◽

Josep M. Borrás ◽

...

Keyword(s):

Poisson Regression ◽

Help Seeking ◽

Regression Models ◽

Population Survey ◽

Waiting Times ◽

Perceived Barriers ◽

Older Individuals ◽

Cancer Symptoms ◽

Before And After ◽

Diagnostic Delays

Abstract Background The patient interval—the time patients wait before consulting their physician after noticing cancer symptoms—contributes to diagnostic delays. We compared anticipated help-seeking times for cancer symptoms and perceived barriers to help-seeking before and after the coronavirus pandemic. Methods Two waves (pre-Coronavirus: February 2020, N = 3269; and post-Coronavirus: August 2020, N = 1500) of the Spanish Onco-barometer population survey were compared. The international ABC instrument was administered. Pre–post comparisons were performed using multiple logistic and Poisson regression models. Results There was a consistent and significant increase in anticipated times to help-seeking for 12 of 13 cancer symptoms, with the largest increases for breast changes (OR = 1.54, 95% CI 1.22–1–96) and unexplained bleeding (OR = 1.50, 1.26–1.79). Respondents were more likely to report barriers to help-seeking in the post wave, most notably worry about what the doctor may find (OR = 1.58, 1.35–1.84) and worry about wasting the doctor’s time (OR = 1.48, 1.25–1.74). Women and older individuals were the most affected. Conclusions Participants reported longer waiting times to help-seeking for cancer symptoms after the pandemic. There is an urgent need for public interventions encouraging people to consult their physicians with symptoms suggestive of cancer and counteracting the main barriers perceived during the pandemic situation.

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Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽

10.3390/w13010037 ◽

2020 ◽

Vol 13 (1) ◽

pp. 37

Author(s):

Tomás de Figueiredo ◽

Ana Caroline Royer ◽

Felícia Fonseca ◽

Fabiana Costa de Araújo Schütz ◽

Zulimar Hernández

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Regression Models ◽

Meteorological Data ◽

Water Storage ◽

Model Performance ◽

Soil Water Balance ◽

Soil Water Storage ◽

The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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A Method for Assessing Threats to the Economic Security of a Region: A Case Study of Public Procurement in Russia

Risks ◽

10.3390/risks9010010 ◽

2021 ◽

Vol 9 (1) ◽

pp. 10

Author(s):

Valentina Kravchenko ◽

Tatiana Kudryavtseva ◽

Yuriy Kuporov

Keyword(s):

Regression Models ◽

Public Procurement ◽

Ordinary Least Squares ◽

Economic Security ◽

Government Procurement ◽

Security Monitoring ◽

Significant Relationships ◽

Monitoring Tools ◽

Russian Region

The issue of economic security is becoming an increasingly urgent one. The purpose of this article is to develop a method for assessing threats to the economic security of the Russian region. This method is based on step-by-step actions: first of all, choosing an element of the region’s economic security system and collecting its descriptive indicators; then grouping indicators by admittance-process-result categories and building hypotheses about their influence; testing hypotheses using a statistical package and choosing the most significant connections, which can pose a threat to the economic security of the region; thereafter ranking regions by the level of threats and developing further recommendations. The importance of this method is that with the help of grouping regions (territory of a country) based on proposed method, it is possible to develop individual economic security monitoring tools. As a result, the efficiency of that country’s region can be higher. In this work, the proposed method was tested in the framework of public procurement in Russia. A total of 14 indicators of procurement activity were collected for each region of the Russian Federation for the period from 2014 to 2018. Regression models were built on the basis of the grouped indicators. Ordinary Least Squares (OLS) Estimation was used. As a result of pairwise regression models analysis, we have defined four significant relationships between public procurement indicators. There are positive connections between contracts that require collateral and the percentage of tolerances, between the number of bidders and the number of regular suppliers, between the number of bidders and the average price drop, and between the number of purchases made from a single supplier and the number of contracts concluded without reduction. It was determined that the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings. Based on these groups, individual economic security monitoring tools can be developed for each region.

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Weak Signals in High‐Dimensional Poisson Regression Models

Applied Modeling Techniques and Data Analysis 1 ◽

10.1002/9781119821588.ch11 ◽

2021 ◽

pp. 203-219

Author(s):

Orawan Reangsephet ◽

Supranee Lisawadi ◽

Syed Ejaz Ahmed

Keyword(s):

Poisson Regression ◽

Regression Models ◽

High Dimensional ◽

Weak Signals

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Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

Sustainability ◽

10.3390/su13137504 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7504

Author(s):

Jie Liu ◽

Paul Schonfeld ◽

Jinqu Chen ◽

Yong Yin ◽

Qiyuan Peng

Keyword(s):

Regression Models ◽

Influential Factors ◽

Rail Transit ◽

Linear Regression Models ◽

Transit Network ◽

Time Index ◽

Trip Time ◽

Buffer Time ◽

Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

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From Community to Public Familiarity: Neighborhood, Sociability, and Belonging in the Neoliberal City

Urban Affairs Review ◽

10.1177/10780874211021512 ◽

2021 ◽

pp. 107808742110215

Author(s):

Felipe Link ◽

Andrés Señoret ◽

Felipe Valenzuela

Keyword(s):

Social Relations ◽

Sense Of Belonging ◽

Regression Models ◽

Secondary Data ◽

Critical Factor ◽

Urban Morphology ◽

Demographic Characteristics ◽

Logistic Regression Models ◽

Pedestrian Space

Current urban neoliberalism processes have shaped and changed contemporary cities, including the local scale’s built environment and social relations. This article aims to study how such transformations affect local sociability by analyzing the effects of neighborhoods’ morphology and socio-demographic characteristics on different forms of interactions and how they affect the sense of belonging. Taking the Metropolitan Area of Santiago, Chile, as a case study, we gathered secondary data on urban morphology and surveyed ten neighborhoods to measure sociability patterns. The results obtained from multilevel logistic regression models show that time living in the neighborhood and public pedestrian space is the most critical factor affecting neighborhood sociability. Moreover, instead of local ties, public familiarity is the form of sociability with the most substantial effects on a sense of belonging. We conclude that recent neighborhoods, formed by neoliberal urbanization, tend to discourage neighborhood sociability and a sense of belonging.

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