Statistical Linear Regression Method Research Articles

Robust recursive sigma point Kalman filtering for Huber-based generalized M-estimation

For nonlinear state estimation driven by non-Gaussian noise, the estimator is required to be updated iteratively. Since the iterative update approximates a linear process, it fails to capture the nonlinearity of observation models, and this further degrades filtering accuracy and consistency. Given the flaws of nonlinear iteration, this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy. The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method. The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction. Considering the correlated state and observation noise induced by recursions, a separately reweighting strategy is adopted to build a robust nonlinear system. Analogous to the nonlinear recursion, a robust nonlinear recursive update strategy is proposed, where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics, thereby completing the nonlinear solution of the robust system. Compared with the iterative update strategies under non-Gaussian observation noise, the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy, stronger robustness, and better consistency. Therefore, the proposed strategy is more suitable for the robust nonlinear filtering framework.

PENGARUH PERUBAHAN LAHAN PERTANIAN MENJADI NON PERTANIAN TERHADAP ASPEK SOSIAL EKONOMI MASYARAKAT

The transformation that occurs in the peri-urban area will cause changes in the morphological peri-urban area and affect changes in the form of agricultural land use and have an impact on changes in the socio-economic conditions of the people in the area. The purpose of this study was to determine the influence of changes in agricultural land to non-agriculture on the socio-economic aspects of the community in Jati Agung. This research uses a deductive approach. The data collection method used primary data from questionnaires and interviews, the secondary data collected from Landsat 8 satellite image and BPS South Lampung Regency in 2013-2020. The method of analysis uses the predominant peri-urban zone classification with urban or predominantly urban characteristics divided into Urban Frame Zones and Urban-rural Frame Zones, which have a percentage of agricultural land use less than 50%. Statistical linear regression method is used to see the effect of the transformation of agricultural land use into non-agriculture on the socio-economic aspects of the peoples. The results of statistical tests show that there is a disproportionate effect with a large effect of 29.5% on the total population, 7.6% on population density, 8.6% on Livelihoods and 20.2% on income.

EFFECTS OF DIRECT FOREIGN INVESTMENTS ON THE ECONOMY OF THE REPUBLIC OF CROATIA

The aim of the paper is to investigate and provide an argumentative explanation of the effects that direct foreign investments have on selected macroeconomic indicators in the Republic of Croatia in the period from 2000 to 2020. This paper analyses the effects that direct foreign investments have had and are having on selected macroeconomic indicators of the Republic of Croatia over a 20-year time series. Most often, advocates of a greater inflow of FDI argue their positions with GDP growth, higher employment, price stability, increased exports of goods and services, and a generally better standard of living for Croatian citizens. The very entry into the Union created numerous advantages for Croatian companies, facilitated the exchange of goods with the members, but did not bring the expected influx of investments. Of the desired numerous greenfield and brownfield investments and mergers and acquisitions of companies, the realization was below expectations due to the stated objective, but also numerous subjective reasons. More precisely, investments have arrived in tourism and trade, but have been absent in basic industry and agriculture. The developments that have been observed in recent years will certainly, in addition to attracting foreign capital, contribute to the Croatian economy, especially in the IT field and green technologies. Investment experience so far shows that in the Republic of Croatia, most foreign investments have been reduced to the purchase of majority ownership in domestic vital companies, which has resulted in job reductions, disincentives for exports, and impairment of national competitiveness. In the sense of the above, the starting hypothesis of the work is: based on the knowledge of the theoretical characteristics of foreign direct investments and the functioning of the movement of international capital and technology transfer, the importance of foreign direct investments for the economy of the Republic of Croatia can be presented. In the analytical part of the paper, statistical methods of linear regression and correlation were used to analyse the influence of direct foreign investments on GDP, employment, long-term interest rate and current account of the balance of payments. The basic research results show that there is a clear and moderate connection between FDI and GDP. The conclusion of the research in the paper emphasizes how the beneficial effect of the inflow of FDI on the gross domestic product indirectly or directly positively affects employment, long-term interest rates and the balance of payments of the Republic of Croatia. Based on the analysis carried out, it can be stated that foreign direct investments can bring improvement to the national economy only when; if they target more advanced industries so the potential technological spillovers are greater, if they establish strong ties with domestic companies that can absorb spillovers in such a way, if part of the production is exported which has a positive effect on the balance of payments and if they motivate domestic companies to follow them.

Simple Regression Modeling on the Number of Fronds Increases with Age on Oil Palm Seedling’s Growth

The productivity of oil palm crops is a crucial factor in the current oil palm plantation industry, considering the prevalent issues and criticisms about the environmental impact of land clearing for oil palm plantations. Given that the productivity of oil palm crops is determined by their quality and growth rate, it is important to study the speed and quality of oil palm crop growth during the early stages, namely seedling growth, where the number of fronds can be measured. This research aims to analyse the number of seedling fronds growing during their 9-month growth period since being transferred from the pre-nursery to the main nursery plantation. The fronds growth observation results of 300 seedlings over nine months are evaluated using the distribution analysis each month, and the growth model in the form of a linear equation is developed using a statistical linear regression method. The correlation coefficients indicate a reasonably significant increase in the number of fronds as the age of the seedlings, with R values = 0.6728, R = 0.8346, and R = 0.9489 for the minimum, the maximum and average number of fronds, respectively. It is concluded that the growth of the number of oil palm fronds can be easily simulated using the developed regression model.

Minimum Mixture Error Entropy-Based Robust Cubature Kalman Filter for Outlier-Contaminated Measurements

This letter investigates the robust state estimation of the nonlinear systems with outlier-contaminated measurements. Due to the advantage of mixture error entropy with two kernel bandwidths in handling non-Gaussian noise caused by the outliers, a novel minimum mixture error entropy (MMEE) criterion-based robust cubature Kalman filter is proposed, in which the cost function is constructed by MMEE criterion, and the nonlinear measurement model is linearized by the statistical linear regression method. By a benchmark target tracking scenario with non-Gaussian measurement noise and INS/GNSS loose combination vehicle tracking experiment, the effectiveness of the proposed filter is demonstrated.

A multi-satellite co-tracking method for a single space target based on adaptive distributed spherical simplex information-weighted consensus filter

A multi-satellite co-tracking method for a single non-cooperative target is proposed to extend the arc of observation as well as to improve the accuracy of tracking. Firstly, the motion of the target is considered to be affected by J2 perturbation, and the model of multi-satellite co-tracking a single space target is designed with only measured angles. Then, a multi-satellite co-tracking method for a single space target based on an adaptive distributed spherical simplex information-weighted consensus filter (ADSSICF) is proposed. The spherical simplex is used to reduce the computational cost of the information filter, and to improve the efficiency of tracking. The angle-only measurement equation is linearized by the statistical linear regression method, and linearization errors are compensated for by updating measurement noise. By designing an adaptive consensus algorithm, only a small number of iterations are needed to achieve network consensus, to improve the convergence speed of the consensus algorithm, and prove the stability of ADSSICF. Finally, a simulation of four low-orbit satellites tracking a single space target is established. The focus of this paper is on the real-time performance and tracking accuracy of the multi-satellite co-tracking a single space target with angle-only based on ADSSICF. The performance of ADSSICF is verified from three indicators: the consensus and convergence of algorithm, the accuracy of state estimation.

ЕАЭС и ОПОП – как супердержавам Евразии взаимодействовать в рамках интеграционных проектов

Introduction: Russia and China are two extremely powerful countries on the Eurasian continent. The interaction of these superpowers is fraught with risks and difficulties, especially in those conditions when both sides are pursuing their own goals. For the People’s Republic of China, the basis of foreign economic policy is the OPOP project, which combines logistics, trade, and investment components. China’s main goal is to promote it and create conditions for its most effective implementation in Eurasia. For Russia, the most important is a less ambitious institution – the EAEU, which serves to promote the interests of the Russian Federation in the countries closest to Russia’s partners. Purpose: to identify the benefits and risks of interaction between the EAEU and the OPOP and offer the cooperation tools that minimize risks. Methods: the research is based on general scientific methods, in particular, deduction; the statistical method of linear regression and the analysis of the synergetic results of economic development are also used. Results: the interaction of the two projects of the Eurasian superpowers is inevitable, and therefore it is extremely important for Russia and the EAEU to properly develop a partnership with the PRC, as well as to soberly assess the existing risks. The EAEU needs the growth drivers today. Such a driver can be a partnership with the PRC and active participation in the OPOP. The proposed coupling strategy takes into account the risks of sinoization; respectively, to reduce them, it is proposed to strengthen the centripetal trends in the EAEU by the economic methods. Conclusions: the coupling of the EAEU and the OPOP is inevitable. It is accompanied by risks; at the same time, the risks of coupling do not outweigh its benefits. For the further development of the integration initiatives in Eurasia, it is necessary to strengthen the attractiveness of the Russian economy model for the EAEU countries.

Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.

Maximum mixture correntropy based outlier-robust nonlinear filter and smoother

In this paper, we are dedicated to studying the robust filtering and smoothing problem for a nonlinear non-Gaussian system. Considering the advantage of mixture correntropy with two kernel bandwidths in dealing with non-Gaussian noise, we propose the novel robust recursive filter and smoother based on the cost functions induced by the maximum mixture correntropy criterion, where the nonlinear dynamic model function and measurement model function are linearized by using the statistical linear regression (SLR) method. In the proposed robust recursive filter and smoother, we apply a third-order spherical cubature rule to obtain the prior estimation of the state and covariance matrix, and approximate the multi-dimensional Gaussian integrals encountered in the SLR method. Furthermore, two additional weights are introduced into the proposed robust recursive filter and smoother to modify the filtering and smoothing gains, respectively. The simulation results of manoeuvring target tracking under different non-Gaussian noise scenarios illustrate the effectiveness of the proposed robust recursive filter and smoother.

Climate induced Coastline Changes: A case study in Togo (West Africa)

Changing climate is a global distress these days. Global warming is one of the men driven outcome of climate change which causes the glaciers to melt, shoreline regression and raises the level of sea. The regression of shoreline in Togo resulted in vandalization of human habitat and infrastructure. This research aims to monitor the coastal erosion utilizing the geospatial techniques in Togo from 1988 to 2020. The process of extraction and existence of change in shoreline is analyzed. Scientific problems regarding the precision of classification algorithms methods utilized for shoreline extraction using various satellite images are also considered. Thus, NDWI index derived from multisource satellite images were used in this research paper. The performance of Iso Cluster Unsupervised Classification, Otsu threshold segmentation and Sup- port Vector Machine (SVM) Supervised Classification techniques are monitored for the shoreline extraction. This study also takes into account the topographic morphology including non linear and linear coastal surfaces. The rate of change of shoreline was estimated through the statistical linear regression method (LRR). The results demonstrated that the SVM Supervised Classification method worked accurately for topographic morphology than other methods.

Pengaruh ROA, ROE Dan NIM Terhadap Return Saham Bank BUMN

AbstractFrom this research aims to find out return on assets, return on equity and net interest margin that affect simultaneously or in part against the share price on the Indonesia Stock Exchange.This research is a comparative causal data source used from bank aunual reports 2015 to 2019 from the Indonesia Stock Exchange. According to the criteria in this study, the sample is 4 i.e. state-owned banks. To analyze models and test research hypotheses, we used several statistical methods of linear regression analysis.Based on the results of hypothetical research and tests, we can conclude as follows:1) ROA, ROE and NIM have a significant effect on stock returns.2) ROA and ROE ratios, partially affecting stock returns, while NIM ratios have been shown to have no affect.

Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)

Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats.

Effective Computational Approach for Prediction and Estimation of Space Object Breakup Dispersion during Uncontrolled Reentry

This paper provides an effective approach for the prediction and estimation of space debris due to a vehicle breakup during uncontrolled reentry. For an advanced analysis of the time evolution of space debris dispersion, new efficient computational approaches are proposed. A time evolution of the dispersion of space pieces from a breakup event to the ground impact time is represented in terms of covariance ellipsoids, and in this paper, two covariance propagation methods are introduced. First, a derivative-free statistical linear regression method using the unscented transformation is utilized for performing a covariance propagation. Second, a novel Gaussian moment-matching method is proposed to compute the estimation of the covariance of a debris dispersion by using a Gauss-Hermite cubature-based numerical integration approach. Compared to a linearized covariance propagation method such as the Lyapunov covariance equation, the newly proposed Gauss-Hermite cubature-based covariance computation approach could provide high flexibilities in terms of effectively representing an initial debris dispersion and also precisely computing the time evolution of the covariance matrices by utilizing a larger set of sigma points representing debris components. In addition, we also carry out a parametric study in order to analyze the effects on the accuracy of the covariance propagation due to modeling uncertainties. The effectiveness of the newly proposed statistical linear regression method and the Gauss-Hermite computational approach is demonstrated by carrying out various simulations.

Simplex Cubature Kalman‐Consensus Filter for Distributed Space Target Tracking

A simplex cubature Kalman‐consensus filter, which is suitable for distributed space target tracking using multiple radars, is proposed to improve the target tracking accuracy. The detailed orbital dynamics model and radar measurement model are given as the system filtering models. The intractable nonlinear Gaussian weighted integral in the filter is decomposed into the spherical integral and radial integral, which are calculated using the spherical simplex rule and the second‐order Gauss‐Laguerre quadrature rule, respectively. In this way, a new simplex cubature rule is derived. By means of the statistical linear regression method, the posterior mean, covariance, and the transmitted messages in the extended Kalman‐consensus filter are approximated using the deduced simplex cubature rule, which results in the proposed simplex cubature Kalman‐consensus filter. No data fusion center exists in the filter, and each radar only needs to exchange the information with its neighbors to reach a consensus estimate. The simulation results show that the proposed filter can achieve more accurate results compared with the cubature Kalman‐consensus filter.

THE INFLUENCE OF THE GLOBAL ECONOMIC CRISIS ON THE INTERNATIONAL INVESTMENT POSITION OF EUROPEAN UNION MEMBER STATES

In the last decades, the international flow of investments considerably grew. They increased dynamically until 2008, when the sudden decline occurred, being caused by the global economic crisis. It had a direct bearing on the structure and the proportions of the internal investment position of many countries. The present paper conducts the analysis of the changes in international investment position rating of European Union member states. With that purpose in mind, the statistical method of linear regression by Hellwig was used.

Nonlinear Non-Gaussian Filtering Algorithm Based on Cubature Kalman and Particle Filter

To resolve the nonlinear non-Gaussian tracking problem effectively, a novel filtering algorithm based on Cubature Kalman Filter (CKF) and Particle Filters (PF) is proposed, which is called Cubature Kalman Particle Filter (CPF). CKF is used to generate the importance density function for PF. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points. It need not compute the Jacobian matrix. Moreover, it makes efficient use of the latest observation information into system state transition density, thus greatly improving the filter performance. The simulation results show that CPF has higher estimation accuracy and less computational load comparing against the widely used Unscented Particle Filter (UPF).

Shoreline changes and morphology of spits along southern Karnataka, west coast of India: A remote sensing and statistics-based approach

The changes in shoreline positions and morphology of spits along the Karnataka coast, western India, are investigated for the period from 1910 to 2005 using multi-dated satellite images and topomaps. The study area has been divided into four littoral cells, and each cell into a number of transects at uniform intervals. The rate of change in shoreline position has been estimated using the statistical linear regression method and cross-validated with regression coefficient ( R 2) and Root Mean Square Error (RMSE) methods. Past and future shoreline positions have been demarcated and future positions of shoreline are estimated for time periods of 10- and 24-years. The study reveals that (i) about 57% of transects exhibit ± 10 m RMSE values, indicating better agreement between the estimated and satellite-based shoreline positions, and (ii) transects closer to the cell boundaries exhibit about 50% uncertainties in shoreline change rate estimations. Based on RMSE values, the influence of natural processes and anthropogenic factors on shoreline changes has been evaluated. The cells/transects dominated by natural processes reveal low RMSE values, whereas the cells/transects influenced by anthropogenic activities such as sand mining, construction of coastal structures, exhibit higher RMSE values. However, the variation in RMSE values is not significant if the total number of transects is considered. Significant changes in morphology of spits have been recorded. Spits demonstrate variations in areal extent, i.e., length and area, if 1910 and 1967 are considered separately as base years. The present study demonstrates that combined use of satellite imagery and statistical techniques such as linear regression and RMSEs can be very useful in quantifying shoreline changes and spit morphology.

Demineralization Depth Using QLF and a Novel Image Processing Software

Quantitative Light-Induced fluorescence (QLF) has been widely used to detect tooth demineralization indicated by fluorescence loss with respect to surrounding sound enamel. The correlation between fluorescence loss and demineralization depth is not fully understood. The purpose of this project was to study this correlation to estimate demineralization depth. Extracted teeth were collected. Artificial caries-like lesions were created and imaged with QLF. Novel image processing software was developed to measure the largest percent of fluorescence loss in the region of interest. All teeth were then sectioned and imaged by polarized light microscopy. The largest depth of demineralization was measured by NIH ImageJ software. The statistical linear regression method was applied to analyze these data. The linear regression model was Y = 0.32X + 0.17, where X was the percent loss of fluorescence and Y was the depth of demineralization. The correlation coefficient was 0.9696. The two-tailed t-test for coefficient was 7.93, indicating the P-value = .0014. The F test for the entire model was 62.86, which shows the P-value = .0013. The results indicated statistically significant linear correlation between the percent loss of fluorescence and depth of the enamel demineralization.

Partition of suspended and riverbed sediments related to the salt-wedge in the lower reaches of a small mountainous river

A month-long comprehensive field experiment during the beginning of the flood season of a small mountainous river in southern Taiwan was carried out in 2004. The major goal of the study was to investigate the effect of hydrodynamic sorting related to the salt-water intrusion on the spatial variability of suspended and riverbed sediments and their coupling of different sizes. The experiment included the deployment of an instrumented tetrapod near the river mouth with an upward-looking ADCP and two CTDs mounted at 50 and 100 cm above the bed (cmab), respectively. On three different days along the river, turbidity, salinity and temperature of the water column were profiled; and water samples were taken from the surface and near the bed at different stations. Additionally, one sediment sample was also taken from the riverbed. Suspended sediment concentrations (SSC) were analyzed for five different sizes, i.e. > 500, 250–500, 63–250, 10–63 and 1.2–10 μm by filtration. For each riverbed sample the size-composition was analyzed for the subsample that contained lithogenic and nonlithogenic components; and the subsample having major nonlithogenic components (organic matter, carbonates, and biogenic opal) was removed. The grain-size frequency distributions of the riverbed samples were analyzed using a laser particle analyzer. The results were grouped into the following size classes: > 473, 249–473, 62–249, 10–62, and < 10 μm for comparison with those of the suspended sediments near the riverbed. Some suspended sediment samples were analyzed for POC and PON. Some riverbed samples were analyzed for TOC. Statistical methods of linear regression and Empirical Orthogonal (Eigen) Function (EOF) were used in the data analysis. Two-layered estuarine circulation pattern was observed at the tetrapod site. The tidally-driven salt-water intrusion is the major factor influencing the hydrodynamics of the Gaoping River, which in turn, affect the longitudinal and vertical distribution of the suspended sediments and the longitudinal distribution of the riverbed sediments. During the flood, the intrusion front of the salt-wedge creates a dynamic barrier. Upriver from this barrier, the riverbed substrate is coarser, composed of sediments mostly coarser than 249 μm. Within the salt-wedge the riverbed substrate is finer, consisting of mostly mud (< 62 μm). The barrier creates a trap on the riverbed immediately seaward from the intrusion front, retaining higher percentages of clay-sized sediments and TOC. The barrier also creates partition in the terrestrial and marine sources of organic matter in the suspended and riverbed sediments. Within the salt-wedge the major contributor of riverbed TOC is the clay-sized marine sediment transport upriver by the intruding seawater. The terrestrial POC is a minor contributor to the riverbed TOC. The riverbed and suspended sediments are coupled. Most size-classes in corresponding suspended and riverbed sediments have a reciprocal relationship (negative feedback) through resuspension and deposition on the water–bed interface. The only size-class of 62–473 μm on the riverbed and 63–250 μm in the suspension are co-varying (positive feedback). This size class contains largely a transient floc population that is formed and disintegrated in-situ both on the riverbed and in the suspension in the course of a tidal cycle.

Service life estimation of concrete bridge decks

As existing bridges continue to deteriorate, the Korean Ministry of Construction and Technology (MOCT) including each State Department of Transportation (DOT) in the U.S. faces increasing demands on the limited funds available for bridge maintenance. In such circumstances, highway bridge owners need realistic and effective deterioration models to find the deterioration rate for present and future condition of the bridge deck and to estimate the end of service life of the bridge. The MOCT recently make it mandat ory that life cycle cost analysis (LCCA) for value engineering be used to all government, construction projects including private se ctor projects over 10billion won in terms of Korean currency., In the LCCA concept, it is important to estimate the end of service li fe of the bridge. Several models are available to predict the deterioration rates of bridge main structure such as deck, superstructu re, and substructure. This paper presents a new model that predicts the end of service life in concrete bridge decks based on (i) the deterioration rate and (ii) the condition rating for decommissioning. This paper proposes the deterioration models of concrete bridge decks for 30 DOTs. The data required for developing the models was obtained from the National Bridge Inventory (NBI) database. Whereas, the additional data required for this research were collected through a questionnaire survey. Statistical linear regre ssion method was used to analyze the deterioration models of concrete, bridge decks for each DOT.