scholarly journals Effects of location of Norway spruce (Picea abies) stumps on their colonisation by insects in the mountains

2019 ◽  
Vol 61 (1) ◽  
pp. 64-77
Author(s):  
Iwona Skrzecz ◽  
Maria Bulka ◽  
Joanna Ukalska
Keyword(s):  
Picea Abies ◽  
Sea Level ◽  
Linear Models ◽  
Negative Binomial ◽  
Sun Exposure ◽  
Limited Information ◽  
Generalised Linear Models ◽  
The Family ◽  
Microclimatic Conditions ◽  
Physical And Chemical

Abstract Tree stumps provide habitat for insect assemblages, which are influenced by various factors. Among these factors, physical and chemical changes of the stumps, fungi developing in the dead wood and stump size are most often reported. However there is limited information about the abundance of insects in stumps that are located on mountains where there are different microclimatic conditions. The studies pointed at the determination whether the location of Picea abies stumps in mountains at different altitudes above sea level and on mountainsides with different sun exposure has an impact on the frequency of insects colonising them. The study was carried out in the Eastern Sudety Mountains situated in south-western Poland. The stumps were in clearcuts located at the altitudes 600–700 m and 900–1000 m above sea level and on southern and northern mountainsides. The insects were collected from 0.05 m2 of bark from each stump and identified to the family, order or species level. The numbers of insects in the stumps were modelled with the use of the Poisson distribution or the negative binomial distribution and the generalised linear models. Picea abies stumps were colonised by insects from 16 families in 3 orders (Coleoptera, Diptera, Hymenoptera) in which the Coleoptera was most frequently represented by the families Cerambycidae, Curculionidae (with the sub-family Scolytinae). In the stumps located at the elevation of 900–1000 m there were 28% more insects than in the stumps at 600–700 m. The stumps located on mountainsides with northern exposure were colonised more abundantly by Cerambycidae. Numbers of Curculionidae in the stumps were affected by altitude. Most Curculionidae were found in the stumps located at the elevation 900–1000 m above sea level. The interaction of altitude and mountainside exposure showed more insects in the stumps at higher altitude, regardless of the mountainside exposure. The results showed that the total number of insects in the stumps was influenced by their location in mountains.

scholarly journals Effect of month of birth on the risk of suicide

2006 ◽  
Vol 188 (5) ◽  
pp. 416-422 ◽  
Author(s):  
Emad Salib ◽  
Mario Cortina-Borja
Keyword(s):  
Suicide Risk ◽  
Linear Models ◽  
Negative Binomial ◽  
Early Summer ◽  
The Other ◽  
Month Of Birth ◽  
Generalised Linear Models ◽  
England And Wales ◽  
Risk Increase ◽  
New Strategies

BackgroundMonth of birth as a suicide risk factor has not been adequately explored. The findings of published studies are contradictory and inconclusive.AimsTo examine the association between suicide and month of birth using suicide data for a 22-year period in England and Wales. The sample size of 26915 suicides greatly exceeds all previous studies.MethodWe analysed all suicides (ICD-9 codes E950-959) and deaths from undetermined injury (E980-989) reported between 1979 and 2001 in England and Wales for persons born between 1955 and 1966, using Poisson and negative binomial generalised linear models with seasonal components.ResultsBirthrates of people who later kill themselves show disproportionate excess for April, May and June compared with the other months. Overall, we found an increase of 17% in the risk of suicide for people born in the peak month (spring -early summer) compared with those born in the trough month (autumn-early winter); this risk increase was larger for women (29.6%) than for men (13.7%).ConclusionsThe ‘month of birth’ factor in suicide can be interpreted in terms of the foetal origins hypothesis. Our findings might have implications for our understanding of the multifaceted aetiology of suicide and may eventually offer new strategies for research and prevention.

scholarly journals Bivariate Mixed Poisson and Normal Generalised Linear Models with Sarmanov Dependence—An Application to Model Claim Frequency and Optimal Transformed Average Severity

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 73
Author(s):  
Ramon Alemany ◽  
Catalina Bolancé ◽  
Roberto Rodrigo ◽  
Raluca Vernic
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Bivariate Distribution ◽  
Generalised Linear Models ◽  
Natural Logarithm ◽  
Insurance Portfolio ◽  
Different Types ◽  
Claim Frequency ◽  
Mixed Poisson Distribution ◽  
Box Cox Transformation

The aim of this paper is to introduce dependence between the claim frequency and the average severity of a policyholder or of an insurance portfolio using a bivariate Sarmanov distribution, that allows to join variables of different types and with different distributions, thus being a good candidate for modeling the dependence between the two previously mentioned random variables. To model the claim frequency, a generalized linear model based on a mixed Poisson distribution -like for example, the Negative Binomial (NB), usually works. However, finding a distribution for the claim severity is not that easy. In practice, the Lognormal distribution fits well in many cases. Since the natural logarithm of a Lognormal variable is Normal distributed, this relation is generalised using the Box-Cox transformation to model the average claim severity. Therefore, we propose a bivariate Sarmanov model having as marginals a Negative Binomial and a Normal Generalized Linear Models (GLMs), also depending on the parameters of the Box-Cox transformation. We apply this model to the analysis of the frequency-severity bivariate distribution associated to a pay-as-you-drive motor insurance portfolio with explanatory telematic variables.

Log-Linear Modelling and Spatial Analysis

1985 ◽  
Vol 17 (7) ◽  
pp. 931-951 ◽  
Author(s):  
E Aufhauser ◽  
M M Fischer
Keyword(s):  
Spatial Analysis ◽  
Linear Models ◽  
Generalised Linear Models ◽  
Migration Patterns ◽  
Poisson Sampling ◽  
The Past ◽  
The Social ◽  
The Family ◽  
Conventional Models ◽  
Log Linear

In the past decade the social sciences have seen an upsurge of interest in analysing multidimensional contingency tables using log-linear models. Two broad families of log-linear models may be distinguished: the family of conventional models and the family of unconventional models (that is, quasi-log-linear and hybrid models). In this paper a brief review of such models is presented and some linkage to the class of generalised linear models suggested by Nelder and Wedderburn is provided. The great potential of log-linear models for spatial analysis is illustrated in applying conventional and unconventional models in a migration context to identify intertemporal stability of migration patterns. The problem that the effective units migrating are households rather than individuals is coped with by postulating a compound Poisson sampling scheme.

scholarly journals Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)

2021 ◽  
Author(s):  
◽  
Joel E. Bancolita
Keyword(s):  
Linear Models ◽  
Descriptive Analysis ◽  
The Philippines ◽  
Family Characteristics ◽  
Intervention Programs ◽  
Categorical Data Analysis ◽  
Generalised Linear Models ◽  
Explanatory Variables ◽  
The Family ◽  
Poor Population

<p>The Philippines is a country where a quarter to one-third of the population is poor. Although the nation has managed to lower poverty incidence in some years, its booming population increases the poor population dramatically. This is why alleviating poverty is a pinnacle program in the country. In aid of poverty alleviation endeavor, this study focuses on assessing which programs had been effective in alleviating poverty given other family characteristics. Aside from descriptive methods, employing Generalised Linear Models (GLMs) and categorical data analysis are the focus in analysing the effects of existing intervention programs on status of improvement and income of families. In addition, varying effects of programs depending on values of other covariates are also analysed. Descriptive analysis and modeling are applied on the panel data of families. Intervention programs namely scholarship, Comprehensive Agrarian Reform Program (CARP) and government housing or other housing financing program (GHFP) have been run together with other family characteristics to describe improvement in welfare and income. Interaction effects, between access to intervention programs and other aspects of the family, have been derived to give a richer picture of the phenomenon. The study has come to conclude that the programs are indeed effective in improving lives of families, with some effects varying on some levels of other explanatory variables.</p>

scholarly journals Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)

2021 ◽  
Author(s):  
◽  
Joel E. Bancolita
Keyword(s):  
Linear Models ◽  
Descriptive Analysis ◽  
The Philippines ◽  
Family Characteristics ◽  
Intervention Programs ◽  
Categorical Data Analysis ◽  
Generalised Linear Models ◽  
Explanatory Variables ◽  
The Family ◽  
Poor Population

<p>The Philippines is a country where a quarter to one-third of the population is poor. Although the nation has managed to lower poverty incidence in some years, its booming population increases the poor population dramatically. This is why alleviating poverty is a pinnacle program in the country. In aid of poverty alleviation endeavor, this study focuses on assessing which programs had been effective in alleviating poverty given other family characteristics. Aside from descriptive methods, employing Generalised Linear Models (GLMs) and categorical data analysis are the focus in analysing the effects of existing intervention programs on status of improvement and income of families. In addition, varying effects of programs depending on values of other covariates are also analysed. Descriptive analysis and modeling are applied on the panel data of families. Intervention programs namely scholarship, Comprehensive Agrarian Reform Program (CARP) and government housing or other housing financing program (GHFP) have been run together with other family characteristics to describe improvement in welfare and income. Interaction effects, between access to intervention programs and other aspects of the family, have been derived to give a richer picture of the phenomenon. The study has come to conclude that the programs are indeed effective in improving lives of families, with some effects varying on some levels of other explanatory variables.</p>

scholarly journals Comparison of Approaches to Testing Equality of Expectations Among Samples from Poisson and Negative Binomial Distribution

2018 ◽  
Vol 66 (4) ◽  
pp. 1025-1034
Author(s):  
Martin Tejkal ◽  
Zuzana Hübnerová
Keyword(s):  
Maximum Likelihood ◽  
Sample Size ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Linear Models ◽  
Negative Binomial ◽  
Test Statistics ◽  
Power Functions ◽  
Generalised Linear Models ◽  
The Relationship

The paper deals with testing of the hypothesis of equality of expectations among p samples from Poisson or negative binomial distribution. a comparison of two main approaches is carried out. The first approach is based on transforming the samples from either Poisson or negative binomial distribution in order to achieve normality or variance stability, and then testing the hypothesis of equality of expectations via the F‑test. In the second approach, test statistics coming from the theory of maximum likelihood appearing in generalised linear models framework, specially designed for testing the hypothesis among samples from the respective distributions (Poisson or negative binomial), are used. The comparison is done graphically, by plotting the simulated power functions of the test of the hypothesis of equality of expectations, when first or second approach was used. Additionally, the relationship between the power functions obtained via the respective approaches and sample sizes is studied by evaluating the respective power functions as functions of a sample size numerically.

scholarly journals The Impact of Alzheimer’s Disease and Related Dementias on Dental Care Utilization and Costs in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 800-800
Author(s):  
Sam Li ◽  
Isaac Donkor ◽  
Liang Hong ◽  
Kevin Lu ◽  
Bei Wu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dental Care ◽  
Negative Binomial ◽  
Self Report ◽  
Care Utilization ◽  
Limited Information ◽  
Dental Care Utilization ◽  
The Impact ◽  
Care Costs

Abstract There is limited information on the impact of cognition function on dental care utilization and costs. This study used the Medicare current beneficiaries survey in 2016 and included 4,268 participants 65+. Dental care utilization and costs were measured by self-report and included preventive and treatment events. Negative binomial regression and generalized linear regression were used to examine the impact of Alzheimer’s disease (AD) and related dementia (RD) on dental care utilization and costs. We found that AD was not associated with dental care utilization, but RD was associated with a lower number of total treatment dental care visits (IRR: 0.60; 95% CI: 0.37~0.98). RD was not associated with dental care costs, but AD was associated with higher total dental care costs (estimate: 1.08; 95% CI: 0.14~2.01) and higher out-of-pocket costs (estimate: 1.25; 95% CI: 0.17~2.32). AD and RD had different impacts on different types of dental care utilization and costs. Part of a symposium sponsored by the Oral Health Interest Group.

Nonnull asymptotic distributions of three classic criteria in generalised linear models

Biometrika ◽  
1994 ◽  
Vol 81 (4) ◽  
pp. 709-720 ◽  
Author(s):  
GAUSS M. CORDEIRO ◽  
DENISE A. BOTTER ◽  
SILVIA L. DE PAULA FERRARI
Keyword(s):  
Linear Models ◽  
Asymptotic Distributions ◽  
Generalised Linear Models

Mixed H2/H∞ Observer-Based LPV Control of a Hydraulic Engine Cam Phasing Actuator

2011 ◽  
Author(s):  
Andrew White ◽  
Zhen Ren ◽  
Guoming Zhu ◽  
Jongeun Choi
Keyword(s):  
Engine Speed ◽  
Test Bench ◽  
Linear Models ◽  
Linear Matrix ◽  
Closed Loop System ◽  
Linear Parameter Varying ◽  
The Family ◽  
Lpv Control ◽  
Lpv System ◽  
Variable Valve

In this paper, a series of closed-loop system identification tests was performed for a variable valve timing cam phaser system on a test bench to obtain a family of linear models for an array of engine speeds and oil pressures. Using engine speed and oil pressure as the system parameters, the family of linear models was translated into a linear parameter varying (LPV) system. The engine speed and oil pressure can be measured in real-time by these sensors equipped on the engine, thus allowing their use as scheduling parameters. An observer-based gain-scheduling controller for the obtained LPV system is then designed based on the numerically efficient convex optimization or linear matrix inequality (LMI) technique. Test bench results show the effectiveness of the proposed scheme.

scholarly journals Temporal analysis of the relationship between dengue and meteorological variables in the city of Rio de Janeiro, Brazil, 2001-2009

2012 ◽  
Vol 28 (11) ◽  
pp. 2189-2197 ◽  
Author(s):  
Adriana Fagundes Gomes ◽  
Aline Araújo Nobre ◽  
Oswaldo Gonçalves Cruz
Keyword(s):  
Minimum Temperature ◽  
Rio De Janeiro ◽  
Linear Models ◽  
Negative Binomial ◽  
Critical Factor ◽  
Temporal Analysis ◽  
Temporal And Spatial Distribution ◽  
Temperature And Precipitation ◽  
The Relationship ◽  
The City

Dengue, a reemerging disease, is one of the most important viral diseases transmitted by mosquitoes. Climate is considered an important factor in the temporal and spatial distribution of vector-transmitted diseases. This study examined the effect of seasonal factors and the relationship between climatic variables and dengue risk in the city of Rio de Janeiro, Brazil, from 2001 to 2009. Generalized linear models were used, with Poisson and negative binomial distributions. The best fitted model was the one with "minimum temperature" and "precipitation", both lagged by one month, controlled for "year". In that model, a 1°C increase in a month's minimum temperature led to a 45% increase in dengue cases in the following month, while a 10-millimeter rise in precipitation led to a 6% increase in dengue cases in the following month. Dengue transmission involves many factors: although still not fully understood, climate is a critical factor, since it facilitates analysis of the risk of epidemics.

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