Agricultural drought periods analysis by using nonhomogeneous poisson models and regionalization of appropriate model parameters

AbstractWithin the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution (gfPd), which is a natural generalization of the fractional Poisson distribution (fPd), and the standard Poisson distribution. We derive some properties of gfPd and more specifically we study moments, limiting behavior and other features of fPd. The skewness suggests that fPd can be left-skewed, right-skewed or symmetric; this makes the model flexible and appealing in practice. We apply the model to real big count data and estimate the model parameters using maximum likelihood. Then, we turn to the very general class of weighted Poisson distributions (WPD’s) to allow both overdispersion and underdispersion. Similarly to Kemp’s generalized hypergeometric probability distribution, which is based on hypergeometric functions, we analyze a class of WPD’s related to a generalization of Mittag–Leffler functions. The proposed class of distributions includes the well-known COM-Poisson and the hyper-Poisson models. We characterize conditions on the parameters allowing for overdispersion and underdispersion, and analyze two special cases of interest which have not yet appeared in the literature.

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Data-driven structure selection for the grey NGMC(1,N) model

Grey Systems Theory and Application ◽

10.1108/gs-03-2021-0039 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Dang Luo ◽

Decai Sun

Keyword(s):

Correction Term ◽

Geometric Interpretation ◽

Selection Method ◽

Agricultural Drought ◽

Grey Model ◽

Model Parameters ◽

Drought Vulnerability ◽

Content Type ◽

Structure Selection ◽

Lasso Method

PurposeWith the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.Design/methodology/approachThe linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.FindingsBy adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.Practical implicationsThis paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.Originality/valueBased on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

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Non-Gaussian bifurcating models and quasi-likelihood estimation

Journal of Applied Probability ◽

10.1017/s0021900200112203 ◽

2004 ◽

Vol 41 (A) ◽

pp. 55-64 ◽

Cited By ~ 2

Author(s):

I. V. Basawa ◽

J. Zhou

Keyword(s):

General Class ◽

Cell Lineage ◽

Likelihood Estimation ◽

Random Coefficient ◽

Model Parameters ◽

Large Sample ◽

Poisson Models ◽

Bivariate Exponential ◽

Non Gaussian ◽

Quasi Likelihood Estimation

A general class of Markovian non-Gaussian bifurcating models for cell lineage data is presented. Examples include bifurcating autoregression, random coefficient autoregression, bivariate exponential, bivariate gamma, and bivariate Poisson models. Quasi-likelihood estimation for the model parameters and large-sample properties of the estimates are discussed.

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Non-Gaussian bifurcating models and quasi-likelihood estimation

Journal of Applied Probability ◽

10.1239/jap/1082552190 ◽

2004 ◽

Vol 41 (A) ◽

pp. 55-64 ◽

Cited By ~ 17

Author(s):

I. V. Basawa ◽

J. Zhou

Keyword(s):

General Class ◽

Cell Lineage ◽

Likelihood Estimation ◽

Random Coefficient ◽

Model Parameters ◽

Large Sample ◽

Poisson Models ◽

Bivariate Exponential ◽

Non Gaussian ◽

Quasi Likelihood Estimation

A general class of Markovian non-Gaussian bifurcating models for cell lineage data is presented. Examples include bifurcating autoregression, random coefficient autoregression, bivariate exponential, bivariate gamma, and bivariate Poisson models. Quasi-likelihood estimation for the model parameters and large-sample properties of the estimates are discussed.

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Instrumental and Ideological Union Commitment: Longitudinal Assessment of Construct Validity

European Journal of Psychological Assessment ◽

10.1027//1015-5759.17.2.98 ◽

2001 ◽

Vol 17 (2) ◽

pp. 98-111 ◽

Cited By ~ 1

Author(s):

Anders Sjöberg ◽

Magnus Sverke

Keyword(s):

Construct Validity ◽

Temporal Stability ◽

Strong Support ◽

Internal Consistency Reliability ◽

Measurement Model ◽

Two Dimensions ◽

Model Parameters ◽

Longitudinal Assessment ◽

Union Commitment ◽

Union Participation

Summary: Previous research has identified instrumentality and ideology as important aspects of member attachment to labor unions. The present study evaluated the construct validity of a scale designed to reflect the two dimensions of instrumental and ideological union commitment using a sample of 1170 Swedish blue-collar union members. Longitudinal data were used to test seven propositions referring to the dimensionality, internal consistency reliability, and temporal stability of the scale as well as postulated group differences in union participation to which the scale should be sensitive. Support for the hypothesized factor structure of the scale and for adequate reliabilities of the dimensions was obtained and was also replicated 18 months later. Tests for equality of measurement model parameters and test-retest correlations indicated support for the temporal stability of the scale. In addition, the results were consistent with most of the predicted differences between groups characterized by different patterns of change/stability in union participation status. The study provides strong support for the construct validity of the scale and indicates that it can be used in future theory testing on instrumental and ideological union commitment.

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Use of soil and climate data to assess the risk of agricultural drought for policy support in Europe

Agronomie ◽

10.1051/agro:2001108 ◽

2001 ◽

Vol 21 (1) ◽

pp. 45-56 ◽

Cited By ~ 4

Author(s):

Pandi Zdruli ◽

Robert J.A. Jones ◽

Luca Montanarella

Keyword(s):

Policy Support ◽

Agricultural Drought ◽

Climate Data

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Representation of bolted joints in a structure using finite element modelling and model updating

JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES ◽

10.15282/jmes.14.3.2020.15.0560 ◽

2020 ◽

Vol 14 (3) ◽

pp. 7141-7151 ◽

Cited By ~ 1

Author(s):

R. Omar ◽

M. N. Abdul Rani ◽

M. A. Yunus

Keyword(s):

Finite Element ◽

Dynamic Behaviour ◽

Model Updating ◽

Complex Structure ◽

Bolted Joints ◽

Model Parameters ◽

Fe Model ◽

Bolted Joint ◽

Fe Modelling ◽

Joint Structure

Efficient and accurate finite element (FE) modelling of bolted joints is essential for increasing confidence in the investigation of structural vibrations. However, modelling of bolted joints for the investigation is often found to be very challenging. This paper proposes an appropriate FE representation of bolted joints for the prediction of the dynamic behaviour of a bolted joint structure. Two different FE models of the bolted joint structure with two different FE element connectors, which are CBEAM and CBUSH, representing the bolted joints are developed. Modal updating is used to correlate the two FE models with the experimental model. The dynamic behaviour of the two FE models is compared with experimental modal analysis to evaluate and determine the most appropriate FE model of the bolted joint structure. The comparison reveals that the CBUSH element connectors based FE model has a greater capability in representing the bolted joints with 86 percent accuracy and greater efficiency in updating the model parameters. The proposed modelling technique will be useful in the modelling of a complex structure with a large number of bolted joints.

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A Note on Confidence Intervals and Model Specification

Marketing ZFP ◽

10.15358/0344-1369-2019-4-33 ◽

2019 ◽

Vol 41 (4) ◽

pp. 33-42

Author(s):

Thomas Otter

Keyword(s):

Confidence Intervals ◽

Generalized Linear Model ◽

Statistical Information ◽

Regression Coefficients ◽

Model Specification ◽

Model Parameters ◽

Exploratory Research ◽

Empirical Calibration ◽

Credible Intervals ◽

Model Structures

Empirical research in marketing often is, at least in parts, exploratory. The goal of exploratory research, by definition, extends beyond the empirical calibration of parameters in well established models and includes the empirical assessment of different model specifications. In this context researchers often rely on the statistical information about parameters in a given model to learn about likely model structures. An example is the search for the 'true' set of covariates in a regression model based on confidence intervals of regression coefficients. The purpose of this paper is to illustrate and compare different measures of statistical information about model parameters in the context of a generalized linear model: classical confidence intervals, bootstrapped confidence intervals, and Bayesian posterior credible intervals from a model that adapts its dimensionality as a function of the information in the data. I find that inference from the adaptive Bayesian model dominates that based on classical and bootstrapped intervals in a given model.

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