Regression Analysis Of Count Data Research Articles

ANALISIS REGRESI COUNT DATA UNTUK PEMODELAN JUMLAH KASUS PENYAKIT TUBERKULOSIS DI KABUPATEN BANYUMAS

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Banyumas Regency is one of the districts with quite high Tuberculosis cases in Central Java. This study aims to analyze the factors that affect the number of tuberculosis cases in Banyumas Regency using regression analysis of count data. Poisson regression is the simplest count data regression model that has the assumption of equidispersion, that is, the mean value equal to the variance. However, in its application, these assumption is often not fulfilled, for example, there are cases of overdispersion (variance value is greater than the mean). In this study, to overcome the case of overdispersion, an approach was used using Generalized Poisson Regression (GPR) and negative binomial regression. The results showed that the data on the number of tuberculosis cases in Banyumas Regency in 2019 was overdispersion. The data modeling of the number of tuberculosis cases in Banyumas Regency with the negative binomial regression model is better than the GPR model. Meanwhile, the only predictor variable that affects the number of tuberculosis cases in Banyumas Regency is the sex ratio of productive age (15-49 years).

Count Data Regression Analysis: Concepts, Overdispersion Detection, Zero-Inflation Identification, and Applications with R.

Count Data Regression Analysis: Concepts, Overdispersion Detection, Zero-Inflation Identification, and Applications with R.

The effect of intermodal competition on the pricing behaviour of a railway company: Evidence from the German case

The market for public intercity railway services still belongs to monopolistic companies in many European countries without considerable intramodal competition. Therefore, intermodal competition is of particular importance for this market. In this paper, we analyse the intermodal competition between intercity buses and railways. In particular, we analyse the price-setting behaviour of the German railway company DB Fernverkehr AG in different competitive situations. Starting from the set of all long-distance connections between the larger German cities with daily train services, the ones with bus services are identified and frequencies are counted. In our analysis, we combine bus and railway data as well as general transport and socioeconomic data. A two-step cluster analysis is used to create homogenous transport markets. Within these clusters, a count data regression analysis is applied in order to analyse the number of special prices offered by DB Fernverkehr AG, as these are the most flexible price instruments of DB. Our results show, among others, that the railway budget prices are lower on routes with intermodal competition compared to monopolistic routes.

Corrigendum to "Transmuted geometric distribution with applications in modelling and regression analysis of count data

Corrigendum to "Transmuted geometric distribution with applications in modelling and regression analysis of count data

Tax attractiveness and the location of German-controlled subsidiaries

This paper analyzes whether taxation has an influence on the location decisions of multinational enterprises. We employ a novel set of 22 tax variables, such as the taxation of dividends and capital gains, withholding taxes, the existence of a group taxation regime, and thin capitalization rules. Furthermore, we use the Tax Attractiveness Index, a new aggregate measure containing the 22 tax variables. Our count data regression analysis is based on a novel hand-collected dataset consisting of the subsidiaries of German DAX30 companies in 97 countries. Controlling for non-tax effects, we find that a country’s tax environment has a significantly positive effect on the number of German-controlled subsidiaries and, therefore, on the location decisions of German multinational enterprises. Specifically, our analysis reveals that German multinational firms place affiliates in countries that offer favorable statutory tax rates, withholding taxes, double tax treaty networks, and holding incentives. Additionally, we find that the Tax Attractiveness Index has explanatory power in subsidiary location decisions and, therefore, it can be used as alternative composite measure, for example, when 22 single tax variables are not at disposal.

多年生作物の経済性評価に関する一考察

This study discusses the roles of perennial crops in uplands where depletive agriculture is occurring, and analyzes desirable policies for developing sustainable agroforestry.Economic analysis indicates that a lower discount rate provides economic incentives with small-scale farmers in uplands to reduce food crops and increase perennial crops for cash income. Although the results of the household survey show that the profitability of clove production is much higher than that of food crops such as maize and cassava, upland farmers remain reluctant to invest in clove production and tend to keep food crop production.The count data regression analysis indicates that the number of clove trees is positively related to land area, land tenancy, education level, food availability, and total number of cattle. Therefore, small land size, food insecurity, lacks of education and financial assets are key constraints of a clove-based agroforestry system.

Tax Attractiveness and the Location of German-Controlled Subsidiaries

This paper analyzes whether taxation has an influence on the location decisions of multinational enterprises. As a tax measure, we employ the Tax Attractiveness Index (see Keller and Schanz 2013). This index covers 18 different tax factors, such as the taxation of dividends and capital gains, withholding taxes, the existence of a group taxation regime, and thin capitalization rules. Our count data regression analysis is based on a novel hand-collected data set consisting of the subsidiaries of German DAX30 companies. Controlling for non-tax effects, we find that a country's tax environment as measured by the Tax Attractiveness Index has a significantly positive effect on the number of Germancontrolled subsidiaries located there. Hence, our study implies that location decisions depend on a bundle of tax factors as captured by the index. In a second step, we show that the location decisions of German DAX30 companies cannot be explained by the statutory tax rate alone. In contrast, withholding taxes, double treaty networks, and special holding regimes seem to play a decisive role in location decisions. Previous studies examining only the influence of statutory tax rates may thus have underestimated the effects of taxation on the activities of multinational companies.

Tax Attractiveness and the Location of German-Controlled Subsidiaries

This paper analyzes whether taxation has an influence on the location decisions of multinational enterprises. As a tax measure, we employ the Tax Attractiveness Index (see Keller and Schanz 2013). This index covers 18 different tax factors, such as the taxation of dividends and capital gains, withholding taxes, the existence of a group taxation regime, and thin capitalization rules. Our count data regression analysis is based on a novel hand-collected data set consisting of the subsidiaries of German DAX30 companies. Controlling for non-tax effects, we find that a country’s tax environment as measured by the Tax Attractiveness Index has a significantly positive effect on the number of German controlled subsidiaries located there. Hence, our study implies that location decisions depend on a bundle of tax factors as captured by the index. In a second step, we show that the location decisions of German DAX30 companies cannot be explained by the statutory tax rate alone. In contrast, withholding taxes, double treaty networks, and special holding regimes seem to play a decisive role in location decisions. Previous studies examining only the influence of statutory tax rates may thus have underestimated the effects of taxation on the activities of multinational companies.

Application of zero-inflated models on regression analysis of count data: a study of sub-health symptoms

To explore the goodness of fit about the zero-inflated (ZI) models in analyzing data related to sub-health symptoms in which the counts are non-negative integers. ZI models are conducted with Stata 11.0. The coefficient of a, Vuong test, O test and likelihood test are used to compare the goodness of fit for ZI models with the common used models such as passion model, negative binomial model. When α is 0.939, and the Z statistic of Vuong test is 32.08, P < 0.0001, which shows that there are too many zeros. The mean number of sub-health symptoms is 2.90, s = 3.85, O = 308.011, P < 0.001, s(2)>x, indicating that the data are over-dispersed. In addition, the optimum goodness of fit is found in zero-inflated negative binomial (ZINB) model with the largest log likelihood and the smallest AIC. ZINB seems the optimal model to study those over-dispersed count data with too many zeros.

A Spline-Based Lack-of-Fit Test for Independent Variable Effect

In regression analysis of count data, independent variables are often modeled by their linear effects under the assumption of log-linearity. In reality, the validity of such an assumption is rarely tested, and its use is at times unjustifiable. A lack-of-fit test is proposed for the adequacy of a postulated functional form of an independent variable within the framework of semiparametric Poisson regression models based on penalized splines. It offers added flexibility in accommodating the potentially non-loglinear effect of the independent variable. A likelihood ratio test is constructed for the adequacy of the postulated parametric form, for example log-linearity, of the independent variable effect. Simulations indicate that the proposed model performs well, and misspecified parametric model has much reduced power. An example is given.

Zero-inflated models for regression analysis of count data: a study of growth and development.

Poisson regression is widely used in medical studies, and can be extended to negative binomial regression to allow for heterogeneity. When there is an excess number of zero counts, a useful approach is to used a mixture model with a proportion P of subjects not at risk, and a proportion of 1--P at-risk subjects who take on outcome values following a Poisson or negative binomial distribution. Covariate effects can be incorporated into both components of the models. In child assessment, fine motor development is often measured by test items that involve a process of imitation and a process of fine motor exercise. One such developmental milestone is 'building a tower of cubes'. This study analyses the impact of foetal growth and postnatal somatic growth on this milestone, operationalized as the number of cubes and measured around the age of 22 months. It is shown that the two aspects of early growth may have different implications for imitation and fine motor dexterity. The usual approach of recording and analysing the milestone as a binary outcome, such as whether the child can build a tower of three cubes, may leave out important information.

Regression Analysis of Count Data

Regression Analysis of Count Data

Book Review: Regression Analysis of Count Data

Book Review: Regression Analysis of Count Data

Strategic Investments in the Pulp and Paper Industry: A Count Data Regression Analysis

The effects of price and market size variables on the investment propensities in the pulp and paper industry are analyzed. A panel of 15 European countries for the time period 1984 - 1997 is used in the regression analysis. We find that the wages, the $US/ECU$ exchange rate, the price of paper and the installed production capacity are the main determinants of strategic investments in this industry. There are no - or only very small - effects from our measures of market size.

Regression Analysis of Count Data

Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.

Regression Analysis of Count Data

Regression Analysis of Count Data

Generalized poisson regression model

The generalized Poisson distribution has been found useful in fitting over-dispersed as well as under-dispersed count data. Since a number of models and methods have been proposed for the regression analysis of count data either with under-dispersion or with over-dispersion, we define and study a generalized Poisson regression (GPR) model which is useful in predicting a response variable affected by one or more covariates. This regression model is suitable for both types of dispersions. The methods of maximum likelihood and moments are given for the estimation of parameters. Approximate tests for the adequacy of the model are considered. Asymptotic tests are given for the significance of regression parameters. The GPR model has been applied to four observed data sets to which other regression models were applied earlier.