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11.
This study aimed to assess the effectiveness of a group-based educational training on the self-efficacy and self-acceptance of Iranian menopausal women using the PRECEDE–PROCEED model. This Randomized Controlled Trial (RCT) was conducted on 80 menopausal women in the age range of 47–55 years residing in the northeast of Iran. The participants were divided randomly into a test group (n = 40) and a control group (n = 40). We found that designing and implementation of a group-based educational training according to the PRECEDE–PROCEED model can significantly enhance the knowledge and performance of the test group with regard to self-efficacy and self-acceptance. 相似文献
12.
Vyacheslav Lyubchich Nathaniel K. Newlands Azar Ghahari Tahir Mahdi Yulia R. Gel 《Wiley Interdisciplinary Reviews: Computational Statistics》2019,11(4)
Local extreme weather events cause more insurance losses overall than large natural disasters. The evidence is provided by long‐term observations of weather and insurance records that are also a foundation for the majority of insurance products covering weather related damages. The insurers around the world are concerned, however, that the past records used to assess and price the risks underestimate the risk and incurred losses in recent years. The growing insurance risks are largely attributed to climate change that brings increasingly more alterations and permanent impact on all aspects of human life and welfare. From floods to hail to excessive wind, adverse atmospheric events are a poignant reminder of how vulnerable our society is across a broad range of threats posed by environmental extremes. Indeed, as climate change effects become more pronounced, we face a new era of risk with increasing weather related damages and losses. This in turn, coupled with challenges of massive climatic data, requires developing innovative analytic approaches that transcend traditional disciplinary boundaries of statistical, actuarial and environmental sciences. Nevertheless, the multidisciplinary nature of climate risk assessment and its impact on insurance is often overlooked and neglected. We highlight the most recent developments and interdisciplinary perspectives on diverse statistical and machine learning methodology for modeling and assessing climate risk in agricultural and home insurances, with a particular focus on noncatastrophic events. This article is categorized under:
- Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting
- Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
- Data: Types and Structure > Massive Data
13.
Mahdi Roozbeh Mohammad Arashi 《Journal of Statistical Computation and Simulation》2017,87(6):1130-1147
14.
In this paper, a generalized difference-based estimator is introduced for the vector parameter β in partially linear model when the errors are correlated. A generalized-difference-based almost unbiased two-parameter estimator is defined for the vector parameter β. Under the linear stochastic constraint r = Rβ + e, we introduce a new generalized-difference-based weighted mixed almost unbiased two-parameter estimator. The performance of this new estimator over the generalized-difference-based estimator and generalized- difference-based almost unbiased two-parameter estimator in terms of the MSEM criterion is investigated. The efficiency properties of the new estimator is illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real dataset. 相似文献
15.
Mohammad Mahdi Maghami Mohammad Bahrami Farkhondeh Alsadat Sajadi 《Journal of applied statistics》2020,47(16):3030
A particular concerns of researchers in statistical inference is bias in parameters estimation. Maximum likelihood estimators are often biased and for small sample size, the first order bias of them can be large and so it may influence the efficiency of the estimator. There are different methods for reduction of this bias. In this paper, we proposed a modified maximum likelihood estimator for the shape parameter of two popular skew distributions, namely skew-normal and skew-t, by offering a new method. We show that this estimator has lower asymptotic bias than the maximum likelihood estimator and is more efficient than those based on the existing methods. 相似文献
16.
In the context of ridge regression, the estimation of shrinkage parameter plays an important role in analyzing data. Many efforts have been put to develop the computation of risk function in different full-parametric ridge regression approaches using eigenvalues and then bringing an efficient estimator of shrinkage parameter based on them. In this respect, the estimation of shrinkage parameter is neglected for semiparametric regression model. Not restricted, but the main focus of this approach is to develop necessary tools for computing the risk function of regression coefficient based on the eigenvalues of design matrix in semiparametric regression. For this purpose the differencing methodology is applied. We also propose a new estimator for shrinkage parameter which is of harmonic type mean of ridge estimators. It is shown that this estimator performs better than all the existing ones for the regression coefficient. For our proposal, a Monte Carlo simulation study and a real dataset analysis related to housing attributes are conducted to illustrate the efficiency of shrinkage estimators based on the minimum risk and mean squared error criteria. 相似文献
17.
We consider estimation of parameters in models defined by systems of ordinary differential equations (ODEs). This problem is important because many processes in different fields of science are modelled by systems of ODEs. Various estimation methods based on smoothing have been suggested to bypass numerical integration of the ODE system. In this paper, we do not propose another method based on smoothing but show how some of the existing ones can be brought together under one unifying framework. The framework is based on generalized Tikhonov regularization and extremum estimation. We define an approximation of the ODE solution by viewing the system of ODEs as an operator equation and exploiting the connection with regularization theory. Combining the introduced regularized solution with an extremum criterion function provides a general framework for estimating parameters in ODEs, which can handle partially observed systems. If the extremum criterion function is the negative log‐likelihood, then suitable regularized solutions yield estimators that are consistent and asymptotically efficient. The well‐known generalized profiling procedure fits into the proposed framework. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
18.
Mahdi Louati 《Journal of the Korean Statistical Society》2013,42(1):83-93
Wishart natural exponential families (NEFs) characterized by Letac (1989) are extended to the Riesz NEFs on symmetric matrices. These families are characterized by their variance functions defined in Hassairi and Lajmi (2001). This work uses a particular basis of these NEFs to describe the class of the generalized multivariate gamma distributions and then to study the statistical model obtained by the mixture of this distribution with the Riesz one on the space of symmetric matrices. 相似文献
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20.
Mahdi Sohrabi-Haghighat Mohammadreza Rostami 《Journal of Combinatorial Optimization》2017,34(1):218-232
The geometric-arithmetic index was introduced in the chemical graph theory and it has shown to be applicable. The aim of this paper is to obtain the extremal graphs with respect to the geometric-arithmetic index among all graphs with minimum degree 2. Let G(2, n) be the set of connected simple graphs on n vertices with minimum degree 2. We use linear programming formulation and prove that the minimum value of the first geometric-arithmetic \((GA_{1})\) index of G(2, n) is obtained by the following formula:
相似文献
$$\begin{aligned} GA_1^* = \left\{ \begin{array}{ll} n&{}\quad n \le 24, \\ \mathrm{{24}}\mathrm{{.79}}&{}\quad n = 25, \\ \frac{{4\left( {n - 2} \right) \sqrt{2\left( {n - 2} \right) } }}{n}&{}\quad n \ge 26. \\ \end{array} \right. \end{aligned}$$