PENGARUH DANA DESA DAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) TERHADAP TINGKAT KEMISKINAN DI PROVINSI ACEH

This study aims to see the effect of Village Fund and Gross Domestic Regional Product on poverty in districts / cities in Aceh Province during the 2017-2019 period. To analyze the data, the method used is panel data regression analysis with the estimation of model parameters using a fixed effect model (FEM). The results showed that the village funds variable did not have a significant effect on poverty, this happened because most of the village funds were allocated more to the infrastructure development sector, causing village funds to still not have a direct effect on reducing poverty. The Gross Domestic Regional Product variable has a negative but significant effect on poverty in the District / City of Aceh Province, which means that with an increase in Gross Domestic Regional Product it will significantly affect the reduction of poverty levels in Aceh Province.

Sine Inverse Lomax Generated Family of Distributions with Applications

This paper introduces a new family of distributions by combining the sine produced family and the inverse Lomax generated family. The new proposed family is very interested and flexible more than some old and current families. It has many new models which have many applications in physics, engineering, and medicine. Some fundamental statistical properties of the sine inverse Lomax generated family of distributions as moments, generating function, and quantile function are calculated. Four special models as sine inverse Lomax-exponential, sine inverse Lomax-Rayleigh, sine inverse Lomax-Frèchet and sine inverse Lomax-Lomax models are proposed. Maximum likelihood estimation of model parameters is proposed in this paper. For the purpose of evaluating the performance of maximum likelihood estimates, a simulation study is conducted. Two real life datasets are analyzed by the sine inverse Lomax-Lomax model, and we show that providing flexibility and more fitting than known nine models derived from other generated families.

The ensemble algorithm for estimation of model parameters in the problem of assessing greenhouse gases fluxes

The problem of assessing the greenhouse gases fluxes from the Earth’s surface based on observations is currently very urgent. To solve it, it is customary to use data assimilation systems (or a more general concept — inverse modeling), which include the observations on the concentration of greenhouse gases and models of the transport and diffusion. Since such problems involve large volumes of satellite data and the global model of transport and diffusion, it has a huge dimension. For this reason, the development of effective algorithms to enable the practical implementation of the task is required. The paper discusses data assimilation algorithms based on the ensemble Kalman filter and ensemble Kalman smoothing, which can be used to solve the problem of estimating greenhouse gases fluxes. Economical algorithms for estimating a parameter that is constant over a given time interval are proposed.

Joint processing of images in two spectral channels for small objects detecting

The article is devoted to the problem of detecting low contrast small-sized objects in two-color images with a powerful spatially non-stationary background. An increase of the detecting reliability is achieved through a combination of three factors: attenuation of the background based on the construction of its locally stationary model; improving the estimation of model parameters by excluding statistically significant outliers from the initial data; joint processing of two-color images with a weakened background component. A method of constructing a linear boundary for detecting a useful signal in a two-dimensional space is proposed. The performance characteristics of a two-channel detector of small-sized objects are presented.

Likelihood and Its Applications

The chapter “Likelihood and Its Applications” introduces the likelihood concept and the concept of maximum likelihood estimation of model parameters. Likelihood is the link between data and models. It is used to estimate model parameters, judge the degree of precision of parameter estimates, and weight support for alternative models. Likelihood is therefore a crucial concept that underlies the ability to test multiple models. The chapter contains several worked examples that progress the reader through increasingly complex problems, ending at likelihood profiles for models with multiple parameters. Importantly, it illustrates how one can take any dynamic model and data and use likelihood to link the data (random variables) to a probability function that depends on the dynamic model.

Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data

Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.

The normal distribution, an epistemological view

The role of the normal distribution in the realm of statistical inference and science is considered from epistemological viewpoint. Quantifiable knowledge is usually embodied in mathematical models. History and emergence of the normal distribution is presented in a close relationship to those models. Furthermore, the role of the normal distribution in estimation of model parameters, starting with Laplace’s Central Limit Theorem, through maximum likelihood theory leading to Bronstein von Mises and Convolution Theorems, is discussed. The paper concludes with the claim that our knowledge on the effects of variables in models or laws of nature has a mathematical structure which is identical to the normal distribution. The epistemological consequences of the latter claim are also considered.