Estimation Techniques Research Articles

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

Estimation and Analysis of Trigonometric Models under Bayesian Approach

In this study, we explore three innovative trigonometric models within the Bayesian framework, utilizing the inverse Weibull distribution as our foundation. These models—namely the Sine inverse Weibull, Cosine inverse Weibull, and Tan inverse Weibull—are crafted from distinct distribution families. We employ both maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) simulation techniques to estimate parameters, drawing upon a comprehensive dataset. By scrutinizing posterior samples numerically and graphically, we evaluate the efficacy of our models, generating Bayes estimates for parameters, examining reliability and hazard functions, and establishing credible intervals. Furthermore, we assess the predictive capacity of all three models through posterior predictive checks. We also conduct comparative analyses, pitting our models against competing ones using real-world data. Notably, our results reveal that the proposed trio of models exhibit strikingly similar performance in terms of fitting the data.

A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors

While State-of-Charge (SOC) estimation for supercapacitors has been extensively studied, most research focuses on single units. However, the recent introduction of reconfigurable circuits significantly alters system dynamics, rendering existing SOC estimation techniques inadequate. This paper addresses this challenge by employing a switching systems approach to estimate the SOC of supercapacitors with reconfigurable circuits. We first establish an RC model for the supercapacitor integrated with the reconfigurable circuit and thoroughly analyze the state continuity and observability of the resulting switched system. Subsequently, we propose a switching observer and evaluate its convergence properties by comparing its performance against other observer techniques. Experimental validation on a hardware platform demonstrates the superiority of our proposed observer for accurate SOC estimation in this context.

Different estimation techniques and data analysis for constant-partially accelerated life tests for power half-logistic distribution

Partial accelerated life tests (PALTs) are employed when the results of accelerated life testing cannot be extended to usage circumstances. This work discusses the challenge of different estimating strategies in constant PALT with complete data. The lifetime distribution of the test item is assumed to follow the power half-logistic distribution. Several classical and Bayesian estimation techniques are presented to estimate the distribution parameters and the acceleration factor of the power half-logistic distribution. These techniques include Anderson–Darling, maximum likelihood, Cramér von-Mises, ordinary least squares, weighted least squares, maximum product of spacing and Bayesian. Additionally, the Bayesian credible intervals and approximate confidence intervals are constructed. A simulation study is provided to compare the outcomes of various estimation methods that have been provided based on mean squared error, absolute average bias, length of intervals, and coverage probabilities. This study shows that the maximum product of spacing estimation is the most effective strategy among the options in most circumstances when adopting the minimum values for MSE and average bias. In the majority of situations, Bayesian method outperforms other methods when taking into account both MSE and average bias values. When comparing approximation confidence intervals to Bayesian credible intervals, the latter have a higher coverage probability and smaller average length. Two authentic data sets are examined for illustrative purposes. Examining the two real data sets shows that the value methods are workable and applicable to certain engineering-related problems.

Innovation and Path to Inclusiveness in Developing Countries

This study examines the effect of innovation on inclusive growth in 63 developing countries and employs System Generalized Method of Moments (GMM) estimation techniques. The study used the global innovation index to measure innovation. In addition, we construct an index of inclusive growth. Further, due to the complex nature of inclusive growth, using a single index may not provide the full picture. So, for substantial empirical support, the study uses a set of dimensions of inclusive growth separately. The empirical results show a positive relationship between the measures of innovation with inclusive growth. Further, the study also constructs indifference curves for selected developing economies to measure the inclusiveness in growth. Finally, it is suggested that developing economies should put in policies to promote innovation activities that include poor segments of society to improve the impact of innovation on inclusive growth.

Investigating Asymmetric Relationship between Financial Development and Global Competitiveness in Developed and Developing Countries

Globalization led to economic integration, which increased the competitiveness of economies through interconnection and interdependence. Financial Development (FD) is considered as an engine of Economic Growth (EG) that significantly contributes in economic productivity and raising prosperity in an economy. This study aims to analyze the relationship between FD and global competitiveness from a global perspective. For this purpose, panel data of 87 developed and 60 developing countries have been used for the period 2000- 2020. The Panel Quantile Regression (PQR) estimation technique has been used for the analysis. The results demonstrate that sustainable FD significantly increases the competitiveness of all countries. However, this impact is non-linear across different quantile groups which suggests evidence of an inverted U-shaped relationship. The linearized marginal effect indicates that all developed economies lie before the maturity of the inverted U-shaped curve. In contrast, except Bolivia, Cabo Verde, Egypt, India, Indonesia, Korea Rep of, Lebanon, Mongolia, Morocco, Nigeria, Pakistan, Philippines, Siri Lanka, Tunisia, and Vietnam, all remaining developing nations also lie before the maturity of an inverted U-shaped curve. This indicates that FD accelerates global competitiveness up to a certain optimal point, and excessive financialization may lead to the misallocation of resources and raising financial risk. The study suggests that developing countries need to implement policies to attain optimal level of sustainable FD, which boosts their competitiveness. The major contribution of the present study is to help the policy makers to formulate proper policies to increase global competitiveness.

Analysis of Private Sector Investment on Economic Growth in Nigeria

The study examined the effect of private sector investment on economic growth in the liberalised Nigerian economy 1990 to 2021. The data for this were analysed using descriptive and analytical tools. The study employed Auto-regressive distribution lags model (ARDL) estimation technique based on the unit root test to determine the effect of four major factors; Loan and Advances (LA), Interest rate (INTR) Exchange rate (EXR) and Inflation rate (INFR) on real gross domestic product (RGDP) which proxies economic growth. Findings revealed that there is negative and significant impact of Interest rate (INTR) Exchange rate (EXR) and Inflation rate (INFR) on economic growth in Nigeria. The study thus concludes that, by improving and strengthening private sector investment, economic growth would be enhanced in Nigeria. The study therefore recommends that in order to boost economic growth in Nigeria more emphasis should be placed on encouraging private investment through domestic and foreign financing.

Comparison of different estimation methods for the inverse power perk distribution with applications in engineering and actuarial sciences

Introducing the Inverse Power Perk distribution, this paper presents a versatile probability distribution designed to model positively skewed data with unprecedented flexibility. Building upon the Perk distribution, it accommodates a wide range of shapes including right-skewed, J-shaped, reversed J-shaped, and nearly symmetric densities, as well as hazard rates exhibiting various patterns of increase and decrease. The paper delves into the mathematical properties of this novel distribution and offers a comprehensive overview of estimation techniques, including maximum likelihood estimators, ordinary least square estimators, percentile-based estimators, maximum product of spacing estimators, Cramer-von Mises, weighted least squares estimators, and Anderson-Darling estimators. To assess the performance of these estimation methods across different sample sizes, Monte Carlo simulations are conducted. Through comparisons of average absolute error and mean squared error, the efficacy of each estimator is evaluated, shedding light on their suitability for both small and large samples. In a practical application, three real datasets, including insurance data, are employed to demonstrate the versatility of the current model, when comparing to existing alternatives. The IPP distribution offers significant advantages over traditional distributions, particularly in its superior ability to model tail risks, making it an invaluable tool for practitioners dealing with extreme values and rare events. Its computational efficiency further sets it apart, enabling more robust and faster analysis in large-scale datasets.This empirical analysis further underscores the utility and adaptability of the Inverse Power Perk model in capturing the nuances of diverse datasets, thereby offering valuable insights for practitioners in various fields.

Continual knowledge graph embedding enhancement for joint interaction-based next click recommendation

Knowledge Graph Embedding (KGE) based deep neural networks contribute to recommender systems in diverse application scenarios. However, Catastrophic Forgetting (CForg) significantly degrades their performance. Although exemplar replay is commonly adopted as a possible remedy to alleviate the intensity of CForg, a trade-off between performance and complexity occurs in this process. Therefore, in this work, we introduce Continual Knowledge graph embedding enhancement for joint Interaction-based Next click recommendation (CKIN) to defy the CForg and assuage the complexity. Typically, we introduce the Semantic Relevance Estimation (SRE) technique to ensure information relevance by filtering out irrelevant-data and reducing the space complexity. We introduce the SRE-enhanced deep probabilistic technique to probably replay the most relevant exemplars to defy the CForg and reduce the time complexity. Moreover, we introduce the integration of locality-preserving loss into the KGE framework to optimize the loss. In substantial experiments on real-world datasets, CKIN outperforms the baseline methods by effectively meeting the highlighted challenges.

Development of a reliable adaptive estimation approach for a low‐cost attitude and heading reference system

AbstractA main challenge within inertial navigation systems involves attitude determination, which refers to the process of estimating the angles that define orientations. A new algorithm is presented to estimate a reliable and proper model for a low‐cost attitude and heading reference system (AHRS), while also, including long‐term global navigation satellite system (GNSS) outages with various dynamical manoeuvres. In the proposed approach, the initial AHRS model is continuously refined at each time step through the incorporation of complementary terms using a new prediction method. These complementary terms are determined using attitude information from the accelerometers output and the GNSS as reference systems. The proposed estimation technique undergoes mathematical analysis to ascertain stochastic stability and is further assessed through real‐world aerial experimental tests conducted with hardware‐in‐the‐loop mechanisation. The obtained results demonstrate a significant enhancement in the reliability and accuracy of the AHRS through the proposed estimation algorithm, even under GNSS blockages. The comparative results highlight the higher performance of the suggested data fusion scheme in providing a reliable and accurate model for the AHRS in normal conditions and assisting a powerful neural network‐based algorithm to provide reliable heading information even during long‐term GNSS outages under dynamical manoeuvres.

Shadow economy-income inequality nexus: a panel analysis of West African countries

ABSTRACT This paper examines the nexus between shadow economy and income inequality in the West African region. The study applies second-generation panel estimation techniques to analyze the effect and the direction of causation between the variables. The results reveal that the shadow economy reduces income inequality in the region in both short-run and long-run estimations. A similar result is documented when alternative estimation techniques are employed. In addition, the panel causality results show a bidirectional relationship between shadow economy and income inequality in the West African economy. The implications of the study are discussed.

On autoregressive processes with Lindley-distributed innovations: modeling and simulation

In this paper, we develop an autoregressive process of order one, assuming that the innovation random variable has a Lindley distribution. The key properties of the process are investigated. Five distinct estimation techniques are used to estimate the parameters and simulation studies are conducted. The stationarity of the process is tested using a unit root test. The application of the proposed process to the analysis of time series data is demonstrated using real data sets. Based on some important statistical measures, the analysis of the data sets reveals that the proposed model fits well, and the errors are independent and Lindley-distributed.

Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition

Abstract Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are globally identified if networks are constant over time. We also provide an extension of the method for time-varying networks. We then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net Generalized Method of Moments. We employ the method to study tax competition across US states. The identified social interactions matrix implies that tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights into the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show that the analysis of social interactions can be extended to economic realms where no network data exists.

Statistical Study of the Mechanical Behavior of the New Fiber from the Strelitzia Juncea Plant Fibers: Application in Ecological Yarns

ABSTRACT To develop efficient, durable, and eco-friendly natural fiber composites, this study examines the physical properties of Strelitzia Juncea (SJ) fiber for potential industrial use. The focus is on how gauge length affects mechanical properties of SJ fibers, aiming to manufacture textile yarns. Tensile tests were performed on SJ yarns of varying diameters (0.95, 1.26, and 1.76 mm). In addition, 150 fibers across five-gauge lengths (GL) from 10 to 50 mm gave a strength of 731 to 270 MPa and a Young’s modulus of 28 to 31 GPa, with statistical analysis applied to evaluate the data’s variability. Statistical techniques were applied to assess and quantify this dispersion. Results were analyzed using Weibull distributions with different probability indices, along with least squares (LS) and maximum likelihood (ML) estimation techniques. Furthermore, for each tensile strength characteristic, a one-factor analysis of variance (ANOVA) was performed across the five groups.

FDI and their participation in global value chains: An analysis based on East Asia

Abstract Subject and purpose of work East Asia, particularly China, Japan, and South Korea has experienced rapid economic growth in recent decades, partly through active participation in global value chains (GVCs). Foreign direct investment (FDI) has played a crucial role in this process, enabling the transfer of technology and skills and access to international markets. This paper aims to empirically analyse the effects of FDI on GVCs in East Asian countries over the last two decades. Materials and methods The paper covers six East Asian countries from 2000 to 2022 and explores the relationship between FDI and GVCs using the Driscoll and Kraay (1998) estimator. The estimation techniques are ordinary least squares (OLS) and feasible generalized least squares (FGLS). Results The results reveal that FDI inflows into East Asia positively and significantly affect the countries participation in GVCs, both upstream and downstream. Conclusions These results suggest the need to support FDI in Asian countries to improve their participation in GVCs.

Interaction effects of mental health disorders and labour productivity on economic growth in Africa

ObjectivesMental health disorders are major public health problems confronting millions of people globally as well as in Africa. While these disorders can negatively affect the economic productivity of affected persons which can reduce economic growth, to the best of our knowledge, empirical evidence in this regard is sparse, with none emanating from the African continent. This study therefore examines the individual and combined (interaction) effects of mental health disorders and labour productivity on economic growth in Africa. MethodsThe study uses data comprising 45 African countries over the period, 2002–2019. Prevalence of schizophrenia, depression, dysthymia, bipolar and anxiety are the mental health disorders used while the log difference between the current year's real Gross Domestic Product (GDP) and the past year's real GDP is used to measure economic growth. Labour productivity is measured by the rate of growth in output (GDP) per worker. The system Generalised Method of Moments (GMM) regression is used as the estimation technique. ResultsThe study finds that, in both the short-and long-run periods, while all the mental health disorders have negative significant effects on economic growth, the effect of labour productivity on economic growth is positive and significant. However, the interactions of each of the mental health disorders with labour productivity are found to have negative significant effects on economic growth in both the short-and long-run periods. ConclusionThere is therefore the need to enhance awareness about mental health disorders as well as access to effective and quality mental healthcare to reduce the associated enormous economic losses.

Molecular detection of intestinal parasites in a rural community of Colombia: A one health approach to explore potential environmental-zoonotic transmission.

Protozoan and helminth parasitic infections pose significant public health challenges, especially in developing countries with rural populations marked by suboptimal hygiene practices and socio-economic constraints. The parasites are the etiological agents of these infections and have a notably elevated global prevalence. Therefore, this study focuses on estimating the frequency and transmission dynamics of several parasitic species, including Blastocystis, Giardia, Cryptosporidium spp., Entamoeba histolytica, Ascaris lumbricoides, Trichuris trichiura, Taenia spp. and hookworms, within a rural community in southwest Colombia with a particular emphasis on the One Health framework, considering environmental and zoonotic transmission potentials. This study involved the analysis of 125 samples, encompassing human participants (n = 99), their domestic pets (dogs) (n = 24) and water sources (n = 2). Parasite detection was carried out utilizing a combination of microscopy and molecular techniques. Furthermore, the characterization of Blastocystis subtypes (STs) was achieved through Oxford Nanopore sequencing of the rRNA-18S gene. The investigation also entailed the examination of potential associations between intestinal parasitism and various sociodemographic factors. Results revealed a high frequency of parasitic infections when employing molecular methods, with Blastocystis (n = 109/87%), Giardia (n = 20/16%), Ancylostoma duodenale (n = 28/22%), Ancylostoma ceylanicum (n = 7/5.6%), E. histolytica (n = 6/4.8%), Cryptosporidium spp. (n = 12/9.6%) and even Taenia (n = 1/0.8%) detected. Cryptosporidium spp. was also identified in water samples. Coinfections were prevalent, with 57% (n = 70) of samples exhibiting single-parasite infections and 43% (n = 53) showing various degrees of polyparasitism, emphasizing the complexity of transmission dynamics. Blastocystis subtyping, conducted via Oxford Nanopore sequencing, revealed a diversity of subtypes and coexistence patterns, with ST2 being the most prevalent. This research underscores the importance of using molecular techniques for frequency estimation, particularly emphasizing the relevance of zoonotic transmission in parasitic infections. It highlights the significance of the One Health approach in comprehending the circulation of parasites among animals, humans and environmental sources, thereby directly impacting public health and epidemiological surveillance.

Advancing Precision in Penile Length Measurement: Evidence-based Synthesis of Stretched Penile Length INdicator Technique (SPLINT).

The purpose of this study was to synthesize evidence and propose a technique for estimation of stretched penile length (SPL) applicable to children. This review has been conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PubMed, Embase, and Scopus databases were queried on penile length (PL) measurement techniques in humans published from 1990 onward. The devices and the techniques used for PL measurement, state of the penis, the habitus of the participants, proximal and distal landmarks for measurement, handling the foreskin and pubic pad of fat, optimal stretching of the penis, and other factors (including the environment) which should be accounted for in the technique were identified from the study cohort (90895 participants across 145 included studies). PL has been represented through flaccid (33.79%), stretched (81.3%), and erect (12.41%) PLs as well as the greatest corporeal length (1.4%). Following devices have been used to measure the PL in the study cohort: rulers [54.68%], calipers [9.7%], measuring tapes [14.5%], coloured measuring strips [2.06%], spatulas/tongue depressors [11.03%], FitKit, syringe [1.4%], cotton swab (and ruler), titan cylinders, slide gauge and ultrasonography [1.37%]. The factors relevant to SPL measurement have been incorporated into the proposed SPL INdicator Technique (SPLINT) which is essentially a holistic extension of the "Conventional PL Measurement" technique. There is a wide range of heterogeneity in the technique for estimation of PL across the study cohort; the underlying factors have been identified along with the respective variables, and the SPLINT for SPL has been described.

Financial Technology (Fintech) as a Driver for Poverty Reduction in Nigeria

ABSTRACT: The human race has been confronting the issue of poverty which is threatening its continued existence for a long time. Poor access to finance can lead to poverty this breeds health issues, lack of access to social and recreational facilities, social delinquencies, reduced living standard, malnutrition and economic difficulties. The goal of defeating poverty in its various ramification remains a cardinal concern for policy formulators in the Nigerian state and international bodies such as the World Bank and United Nations. Following the thriving emergence of financial technology (fintech) in Nigeria, there exist a dearth in academic researches to examine how fintech can be a driver in the reduction of poverty. This study examined the impact of financial technology on poverty reduction in Nigeria. Data on poverty rate, financial technology, mortality rate, literacy rate and gross domestic products were obtained from the National Bureau of Statistics (NBS) in Nigeria and the Central Bank of Nigeria (CBN) statistical bulletin for the period of 1991 – 2021. This study employed the multiple linear regression estimation technique to analyse collected data as it examines a cross-section of major key independent variables across a time series data, on the premise of a common effect model. Data analysed indicates a negative and significant nexus between poverty rate and financial technology innovation. The result also suggest that literacy rate and poverty rate have a negative and insignificant relationship. The poverty rate was also found to be positively and insignificantly related to mortality rate and gross domestic products respectively. The study findings have several significant policy implications. Firstly, infrastructures that support internet and mobile telephony should be expanded by providing incentives for investment since technology is the backbone of the operations of fintech. Secondly, education on fintech, information communication technology and finance should be increased to fill the gap among citizens who are not technological inclined especially in the rural areas to increase the use of fintech. Lastly, there should be further regulatory interventions through reforms to remove the barriers to credit.

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm’s performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies.We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis.