Estimation Techniques Research Articles

Deep optimal experimental design for parameter estimation problems

Abstract Optimal experimental design is a well studied field in applied science and engineering. Techniques for estimating such a design are commonly used within the framework of parameter estimation. Nonetheless, in recent years parameter estimation techniques are changing rapidly with the introduction of deep learning techniques to replace traditional estimation methods. This in turn requires the adaptation of optimal experimental design that is associated with these new techniques. In this paper we investigate a new experimental design methodology that uses deep learning. We show that the training of a network as a Likelihood Free Estimator can be used to significantly simplify the design process and circumvent the need for the computationally expensive bi-level optimization problem that is inherent in optimal experimental design for non-linear systems. Furthermore, deep design improves the quality of the recovery process for parameter estimation problems. As proof of concept we apply our methodology to two different systems of Ordinary Differential Equations.

The role of the internet in moderating the effect of industrialization on income inequality in Africa

ABSTRACT While the internet-income inequality nexus and the industrialization-income inequality relationship have been separately investigated, this present study looks at the moderating role of internet penetration in the relationship between industrialization and income inequality. The focus of the study is on a panel of 44 African countries over the period 2000–2020. The empirical evidence is based on the fully modified ordinary least squares (FMOLS) estimation technique. The results show that while internet penetration has a negative and significant impact on income inequality, industrialization has a positive and significant impact on income inequality. Moreover, the interaction between industrialization and internet penetration exerts a positive and significant impact on income inequality. Policy implications are provided based on the findings of this study.

The nonlinear impact of renewable energy, fossil energy and CO2 emissions on human development index for the eight developing countries

This study analyzes the impact of renewable energy, fossil energy, CO2 emissions, urbanization, international trade and industrialization on human development for the eight developing countries over the period 1990–2021 using Karavias and Tzavalis (2014) panel unit root test regarding structural breaks and cross-section dependence and feasible generalized least squares (FGLS) estimator technique. The countries of interest are Brazil, Russia, India, China, South Africa (BRICS countries) plus Egypt, Iran and Turkey while human development index is used as a measure of human development. The findings reveal that there exists a U-shaped relationship between renewable energy consumption and HDI an inverted U-shaped relationship between fossil energy/CO2 emissions and HDI is relevant. Moreover, the positive impact of urbanization, international trade and industrialization on HDI is determined. As for policy implications, the policies oriented to renewable energy and to reduce fossil energy dependence and CO2 emissions should be adopted to improve human development in the developing countries. Additionally, policies promoting urbanization, international trade, and industrialization are necessary to achieve high levels of human development, provided that the negative effects of these policies are mitigated.

A taxonomy of machine learning techniques for construction cost estimation

A taxonomy of machine learning techniques for construction cost estimation

Climate change and economic growth: Evidence for European countries

AbstractClimate change may affect economies and the welfare of people around the world. To design appropriate policy responses, the economic effects of climate change should be known. One strand in the literature empirically estimates the growth effects of climatic variations. However, those studies often neglect economic variables that have proven to be robust in explaining economic growth. Further, often they fail to check for the robustness of their results. The main aim of this study is to detect whether there exists a statistically significant robust relation between climate change and economic growth by estimating different model specifications. To do so panel estimation techniques for 24 European economies for the period from 2002 to 2019 are applied whereby panel fixed effects estimations and dynamic Generalized Methods of Moments estimations are resorted to. No statistically significant robust relationship between the temperature change and economic growth is found just as for precipitation that does not exert a significant effect on growth. As regards the institutional and macroeconomic control variables the rule of law, the fiscal variable, and the output gap are statistically significant and robust. It is argued that far‐reaching policy measures, such as the net zero goal of the European Union, should be given only based on robust results. Otherwise, economic policy may turn out to be inadequate and can lead to welfare losses. Hence, the conclusion is that the net zero goal of the European Green Deal is to be seen skeptical.

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These tools employ active learning, where algorithms sort abstracts based on human feedback. However, researchers using these tools face a crucial dilemma: When should they stop screening without knowing the proportion of relevant studies? Although numerous stopping rules have been proposed to guide users in this decision, they have yet to undergo comprehensive evaluation. In this study, we evaluated the performance of three stopping rules: the knee method, a data-driven heuristic, and a prevalence estimation technique. We measured performance via sensitivity, specificity, and screening cost and explored the influence of the prevalence of relevant studies and the choice of the learning algorithm. We curated a dataset of abstract collections from meta-analyses across five psychological research domains. Our findings revealed performance differences between stopping rules regarding all performance measures and variations in the performance of stopping rules across different prevalence ratios. Moreover, despite the relatively minor impact of the learning algorithm, we found that specific combinations of stopping rules and learning algorithms were most effective for certain prevalence ratios of relevant abstracts. Based on these results, we derived practical recommendations for users of AI-aided screening tools. Furthermore, we discuss possible implications and offer suggestions for future research.

Distributed Bipartite Consensus of Multi-Agent Systems via Disturbance Rejection Control Strategy

This work aims to focus on analyzing the consensus control problem in cooperative–competitive networks in the occurrence of external disturbances. The primary motive of this work is to employ the equivalent input-disturbance estimation technique to compensate for the impact of external disturbances in the considered multi-agent system. In particular, a suitable low-pass filter is implemented to enhance the accuracy of disturbance estimation performance. In addition, a specific signed, connected, and structurally balanced undirected communication graph with positive and negative edge weights is considered to express the cooperation–competition communication among neighboring agents. The cooperative–competitive multi-agent system reaches its final state with same magnitude and in opposite direction under the considered structurally balanced graph. By utilizing the properties of Lyapunov stability theory and graph theory, the adequate conditions assuring the bipartite consensus of the examined multi-agent system are established as linear matrix inequalities. An illustrative example is delivered at the end to check the efficacy of the designed control scheme.

Simulating the effect of counterfactual changes in religious tourism on economic growth in Saudi Arabia

PurposeThe literature has well-documented the positive economic effects of both leisure and religious tourism. However, certain events, such as the COVID-19 pandemic and others, can impair the positive effect. Hence, the purpose of this study is to simulate the effect of counterfactual changes in religious tourism on Saudi Arabia’s economic growth during the MATAF expansion project and the COVID-19 pandemic.Design/methodology/approachThe study employs novel dynamic autoregressive distributed lag (DARDL) and kernel-based regularised least squares (KRLS) estimation techniques to analyse data spanning 1970–2022.FindingsThe results refuted the positive effect of spiritual tourism, especially during the two events. The simulation results show that a shock in predicted religious tourism will lower economic growth marginally in the short term but substantially in the long run.Practical implicationsThe Saudi Arabian government should prioritise strategic infrastructural development such as expanding roads, airports and renovating worship centres during periods of low demand to promote economic growth through religious tourism. This will allow for the accommodation of a growing number of pilgrims without disrupting religious activities. In addition, it is imperative for the government to engage in international cooperation with other governments to proactively avert future pandemics such as the COVID-19 pandemic.Originality/valueEven though many studies have examined the effect of religious tourism on economic growth, to the best of our knowledge, this study is the first one that simulates the effect of counterfactual changes in religious tourism on economic growth during the MATAF expansion project and the COVID-19 pandemic.

The Effects of Climate Change on Kenya’s Economic Growth

Changes in the climate and natural disasters have hampered Kenya's growth in economy, as seen by periodic droughts, floods, and shifts in rainfall and temperature patterns. The research investigated the effects of climatic change on Kenya's GDP growth. Modeling was done using Autoregressive Distributed Lag (ARDL) model estimation technique while analyzing data from 1990 to 2023. Climate change was represented by annual rainfall fluctuations, carbon emissions, and forest depletion, with inflation, exchange rates, and government spending serving as the controlling factors. The regression results indicated that at 5% level of significance short run carbon emissions affect growth negatively but the long run results indicated a positive effect. Forest depletion positively impacts growth in the long run period. Government expenditure, changes in the exchange rate and economic growth had an inverse relationship. Inflation did not have potential long term impact on GDP. This study recommended that the government to balance economic growth with environmental sustainability by promoting cleaner technologies and renewable energy projects. Investments advanced irrigation and drought resistant crops are crucial. Sustainable forest management should involve controlled logging and reforestation to ensure long term health. Reducing exchange rate volatility through foreign reserves and diversified exports will stabilize the economy. Enhancing government expenditure efficiency by focusing on high return investments and minimizing waste will boost GDP stability.

Modelling of the time to death of breast cancer patients at Hiwot Fana Specialized University Hospital

Breast cancer is the most common cause of cancer death and is a frequently diagnosed cancer among women worldwide. It is becoming a challenging health condition in Ethiopia with a high rate of morbidity and mortality. The main aim of this study was to model the time to death in breast cancer patients at Hiwot Fana Specialized University Hospital. A retrospective cohort study was carried out from April 1st, 2020, to April 1st, 2023, and 296 women were included in the study. We used nonparametric methods and Bayesian accelerated failure time models (with Laplace approximation) to identify risk factors and choose a model fitting breast cancer patient data. Model comparison was performed using the marginal likelihood, deviance information criterion and Watanabe Akaike information criterion. From the total of 296 patients in the study, 56 (18.9%) died. The estimated median survival time was 33 months. The log-rank test showed that age group, stage, alcohol consumption, smoking habit, and comorbidity were potential risk factors associated with the time to death in breast cancer patients at the 5% level of significance. The Bayesian Weibull accelerated failure time model was found to be the best fitted model for predicting the survival time of patients with minimum DIC (520.39) and WAIC (521.59) values. The final Bayesian Weibull AFT model with the integrated nested Laplace approximation estimation technique revealed that age group, stage, alcohol consumption, smoking habit, and comorbidity were significantly associated with the time to death in breast cancer patients. Individuals older than 65 years, with stage IV disease, drinking alcohol, smoking cigarettes and having comorbidities had shortened survival times in patients with breast cancer. Hence, Hiwot Fana Specialized University Hospital and related bodies should work on awareness creation to reduce smoking habits and alcohol use as well as give due attention to elderly and stage IV breast cancer patients during intervention.

The moderation effect of institutional quality on the nexus between migrant remittance and financial inclusion in West Africa

BACKGROUND: Migrants' remittances are critical in financing the economies of Africa and the West African region, in addition to FDI and ODA. It is also a significant driver of financial inclusion. OBJECTIVE: This paper assessed the moderating role of institutional quality on migrants' remittances and financial inclusion in West Africa during 2004–2023. METHODS: Secondary data was used for this study, sourced from the World Bank’s World Development Indicators (WDI), the World Governance Indicator (WGI), and the IMF’s Financial Access Survey (FAS), 2023. This study employed the Panel Fixed Effects (PFE) estimation technique, which accounts for Driscoll and Kraay's (1998) standard error. To ensure the robustness of the findings, the analysis was augmented with the utilisation of feasible generalised least squares (FGLS). RESULTS: There was an overall positive impact of institutional quality on financial inclusion (FI), at a significant rate of 120.8%, accounting for deposit-taking microfinance institutions, thereby highlighting that institutions play a crucial part in improving financial inclusion in West Africa. Also, the moderating effect of institutional quality had a more positive impact on microfinance institutions than commercial banks, at a significant rate of 16.2%. CONCLUSION: In summary, attention should be given to establishing more microfinance institutions as they serve as a better remittance distribution channel when compared to commercial banks, emphasising enhancing quality in financial services delivery. This also encourages more people to be financially included, thereby making better economic use of the remittance received in West Africa.

Statistical Analysis and Several Estimation Methods of New Alpha Power-Transformed Pareto Model with Applications in Insurance

This article defines a new distribution using a novel alpha power-transformed method extension. The model obtained has three parameters and is quite effective in modeling skewed, complex, symmetric, and asymmetric datasets. The new approach has one additional parameter for the model. Certain distributional and mathematical properties are investigated, notably reliability, quartile, moments, skewness, kurtosis, and order statistics, and several approaches of estimation, notably the maximum likelihood, least square, weighted least square, maximum product spacing, Cramer-Von Mises, and Anderson Darling estimators of the model parameters were obtained. A Monte Carlo simulation study was conducted to evaluate the performance of the proposed techniques of estimation of the model parameters. The actuarial measures are computed for our recommended model. At the end of the paper, two insurance applications are illustrated to check the potential and utility of the suggested distribution. Evaluation using four selection criteria indicates that our recommended model is the most appropriate probability model for modeling insurance datasets.

Dynamic correction of forest fire spread prediction using observation error covariance matrix estimation technique based on FLC-GRU

BackgroundData assimilation (DA) techniques have played a significant role in improving the prediction accuracy of forest fire spread. The dynamic correction technique weights the predicted and observed values to obtain an analytical value that better reflects the position of the fire perimeter. The weighted importance of each contribution is determined by the magnitude of its associated error. However, as a crucial parameter affecting prediction accuracy, the covariance matrix of observation errors is often inaccurate and neglects its own temporal correlation. This is unfriendly to spread prediction results. To address this issue, we proposed a targeted technique for estimating the observation error covariance matrix (R matrix) based on the Fire Line Convolutional Gated Recurrent Unit (FLC-GRU).ResultsWe integrated this method into the DA framework and validated its applicability and accuracy using Observing System Simulation Experiment (OSSE). Through comparisons with traditional methods, the results indicate that using the FLC-GRU estimated R matrix for correction calculations leads to wildfire prediction locations that are closer to the true values.ConclusionsThe proposed approach learns the covariance matrix directly from time-series observed fire line data, without requiring any prior knowledge or assumptions about the error distribution, in contrast to classical posterior tuning methods. The proposed method significantly improves the rationality and accuracy of R matrix estimation, enhances the utility of observational data, and thereby improves the correction accuracy of forest fire spread predictions. Moreover, the study also demonstrates the applicability of the proposed method within the DA framework.

Stochastic Model for Novel Mixture Distribution with Properties and Its Application

Medical research is the one of the most important applications of statistical analysis. A great deal of occasions, specific statistical considerations are needed for cancer research in order to determine the model that best fits the survival data. In this paper, the proposed a new two parameter continuous distribution. It is called a new mixture distribution (NMD). Some statistical properties of the distribution are derived such as, the moments, moments generating function, reliability analysis. Also, the distribution of order statistics is presented and entropies are derived. The application of the maximum likelihood estimate technique to the performance of traditional parameter estimation. Two authentic data sets describing cancer patients' survival are used to empirically demonstrate the potential importance and usability of the proposed distribution. Comparing the new mixture distribution to some other competing distribution, the analysis's results demonstrated that it performed quite well.

A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations.

Multifractal analysis of fractal interpolation functions

This paper presents a novel algorithm to utilize multifractal spectrum as a quantitative measure for the fractal interpolation functions with respect to scaling factor and fractional order. As of yet, there were no error estimation techniques to interpret the fractal interpolation functions in the literature. To bridge this gap, this paper sketches multifractality as a quantitative measure for inquiring and comparing the effects of different scaling factors. The proposed algorithm for analyzing the multifractal measure depends on the probability measure of data points, which fractal function passes through, enabling to effectively discuss the heterogeneity of fractal interpolation functions. In addition, the impact of fractional orders on the fractional derivative (integral) of fractal interpolation functions is also discussed tailoring the multifractal measure.

Design and performance analysis of a net-zero energy building in Owerri, Nigeria

Building cooling load depends on heat gains from the outside environment. Appropriate orientation and masonry materials play vital roles in the reduction of overall thermal loads buildings. A net-zero energy building performance has been analyzed in order to ascertain the optimum orientation and wall material properties, under the climatic conditions of Owerri, Nigeria. Standard cooling load estimation techniques were employed for the determination of the diurnal interior load variations in a building incorporating renewable energy as the major energy source, and compared with the situation in a conventionally powered building. The results show a 19.28% reduction in the building’s cooling load when brick masonry was used for the wall construction. It was observed that a higher heat gain occurred when the building faced the East-West direction than when it was oriented in the North-South direction. Significant diurnal cooling loads variation as a result of radiation through the windows was also observed, with the east facing windows contributing significantly higher loads during the morning hours while the west facing windows contributed higher amounts in the evening. The economic analysis of the net-zero energy building showed an 11.63% reduction in energy cost compared to the conventional building, with a 7-year payback period for the use of Solar PV systems. Therefore, the concept of net-zero energy building will not only help in energy conservation, but also in cost savings, and the reduction of carbon footprint in the built environment.

Evaluating methods for estimating influence effects in social networks: a simulation study

The contagion effect, also known as the peer effect, plays a significant role in social networks. It refers to the phenomenon in which one person or group can influence the behavior of other individuals with whom they share social connections. However, accurately estimating the contagion effect is challenging due to the confounding impact of peer selection. Previous studies have shown that this can be viewed as a problem of omitted variable bias. In an attempt to address this issue, simulation studies were conducted to compare the effectiveness of various methodologies in estimating peer influence within social networks, including latent variable models and machine learning techniques. Our research demonstrates that the performance of various approaches vary depending on the social network scenario. Overall, the latent space approach exhibits the best performance. This analysis provides valuable insights for researchers studying estimation techniques and emphasizes the importance of understanding the strengths, limitations, and objectives of these methods before using them for inference-based estimation.

Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.

Impact of Ebola Virus Disease and ECOWAS Membership on Intra-Regional Trade in West Africa

The West African sub-region bore the brunt of the world's largest Ebola outbreak, significantly impacting the economic activities and trade shares of the affected countries. This study seeks to examine the repercussions of the Ebola Virus Disease (EVD) on the trade shares of affected countries and to explore the potential influence of ECOWAS membership on intra-regional trade in West Africa. Using the Poison Pseudo Maximum Likelihood (PPML) estimation technique, an analysis of the augmented gravity model of international trade was conducted. The findings indicate a two-fold reduction in the trade shares of affected countries with their intra-regional partners due to the Ebola Virus Disease. Additionally, with Mauritania expressing its desire to join the ECOWAS sub-region, there is a need to explore the impact of the Regional Economic Community on intra-regional trade. Furthermore, the study reveals that ECOWAS membership has the potential to double trade levels in West Africa. The findings also suggest that Mauritania stands to gain significant benefits from becoming a member of the ECOWAS. In conclusion, this study highlights the necessity for ECOWAS to proactively respond to disease outbreaks and underscores the importance of increased research investment. Moreover, it emphasizes the need for the ECOWAS to further improve infrastructure to facilitate intra-regional trade, especially in transportation.