Classical Estimation Research Articles

Assessment of Government Liberalization Policy and the Growth in Nigeria’s Trade Industry

Following the outbreak of COVID-19 and the contraction of government revenue, it has become increasingly difficult for government to continue with “subsidy oriented” policies hence, this paper evaluates Nigeria’s liberalization policies as a major drivers of trade growth in the country. A quantitative approach was adopted in this paper. Data used in this paper were secondary data sourced from World bank indicators and Central Bank of Nigeria Statistical Bulletin (1980-2018 version). This paper used Error Correction Classical Linear Regression estimate to analyse the data. The result of the study showed that changes in the key liberalization policy variables have significant influence on output growth but not statistically significant to trade growth. Industrialization oriented policies was therefore recommended for Nigeria to actualize her dreams of being an industrialized country, a strong and competitive institution that foster a sustainable industrial sector through policy is also advised. This study adds to the limited available evidence concerning the contribution of government liberation policy on the growth of Nigeria trade sector. It stands as the first to consider trade openness, government liberation policy and the Nigeria industrial sector. 
 
 Received: 18 June 2023 / Accepted: 15 February 2024 / Published: xx March 2024

A case study for Kuwait mortality during the consequent waves of COVID-19

The World Health Organization (WHO) announced on March 11, 2020, that COVID-19 could be considered a pandemic. This epidemic has become a huge issue for academics, doctors, healthcare providers, epidemiologists, and decision-makers alike. Motivated by studying natural phenomena, we concentrated on making a statistical model capable of fitting natural pandemics into the whole world. In this manuscript, we introduced a new novel distribution called the Inverse Power Haq (IPH) distribution. We created this distribution due to its formidable merits. We introduced its statistical properties and plotted its probability density function (PDF), cumulative distribution function (CDF), and hazard rate function (HRF). We utilized classical estimation techniques to determine the parameters for the suggested distribution. In the paper's conclusion, we performed a data analysis, applying the distribution to COVID-19 infection in Kuwait for practical purposes. Our analysis demonstrated the superior performance of the proposed distribution compared to its competitors, establishing it as the most suitable choice for modeling COVID-19 data in patients of different ages. Lastly, the conclusion section includes a summary of our overall findings and conclusions.

Direct model parameter identification of twisted string actuators using Nelder-Mead simplex method

A twisted string actuator (TSA) is a simple rotary-to-linear motion converter with a large transmission ratio. This paper presents a simple procedure for direct model parameters identification of TSAs, which accepts the model in initial ODE-based form, while the previous techniques required a complex reformulation of the model. Also, the identification process estimates all unknown or impossible-to-measure parameters directly (not in an arithmetic combination) in the original form of the mathematical model of the TSA, which is a benefit in comparison with common and classic adaptive parameter estimators. The proposed nonlinear identification procedure employs the Nelder–Mead simplex method to minimize the cost function as the core. Moreover, the identification results are verified in different working frequencies. The presented model parameter optimization procedure reduced the deviation between the mathematical model and the real TSA behavior for a set of optimum parameters up to [Formula: see text] in terms of the defined cost function. As an application for the mathematical model of the TSA with the identified parameters, a feedback linearization controller is designed for the position control of the actuator. The simulation and implementation of the controller prove the proper performance of the controller.

Linear mixed models and latent growth curve models for group comparison studies contaminated by outliers.

The linear mixed model (LMM) and latent growth model (LGM) are frequently applied to within-subject two-group comparison studies to investigate group differences in the time effect, supposedly due to differential group treatments. Yet, research about LMM and LGM in the presence of outliers (defined as observations with a very low probability of occurrence if assumed from a given distribution) is scarce. Moreover, when such research exists, it focuses on estimation properties (bias and efficiency), neglecting inferential characteristics (e.g., power and type-I error). We study power and type-I error rates of Wald-type and bootstrap confidence intervals (CIs), as well as coverage and length of CIs and mean absolute error (MAE) of estimates, associated with classical and robust estimations of LMM and LGM, applied to a within-subject two-group comparison design. We conduct a Monte Carlo simulation experiment to compare CIs and MAEs under different conditions: data (a) without contamination, (b) contaminated by within-subject outliers, (c) contaminated by between-subject outliers, and (d) both contaminated by within- and between-subject outliers. Results show that without contamination, methods perform similarly, except CIs based on S, a robust LMM estimator, which are slightly less close to nominal values in their coverage. However, in the presence of both within- and between-subject outliers, CIs based on robust estimators, especially S, performed better than those of classical methods. In particular, the percentile CI with the wild bootstrap applied to the robust LMM estimators outperformed all other methods, especially with between-subject outliers, when we found the classical Wald-type CI based on the t statistic with Satterthwaite approximation for LMM to be highly misleading. We provide R code to compute all methods presented here. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Finite-Sample Bias of the Linear Excess Relative Risk in Cohort Studies of Computed Tomography-Related Radiation Exposure and Cancer.

The linear excess relative risk (ERR) is the most commonly reported measure of association in radiation epidemiological studies, when individual dose estimates are available. While the asymptotic properties of the ERR estimator are well understood, there is evidence of small sample bias in case-control studies of treatment-related radiation exposure and second cancer risk. Cohort studies of cancer risk after exposure to low doses of radiation from diagnostic procedures, e.g., computed tomography (CT) examinations, typically have small numbers of cases and risks are small. Therefore, understanding the properties of the estimated ERR is essential for interpretation and analysis of such studies. We present results of a simulation study that evaluates the finite-sample bias of the ERR estimated by time-to-event analyses and its confidence interval using simulated data, resembling a retrospective cohort study of radiation-related leukemia risk after CT examinations in childhood and adolescence. Furthermore, we evaluate how the Firth-corrected estimator reduces the finite-sample bias of the classical estimator. We show that the ERR is overestimated by about 30% for a cohort of about 150,000 individuals, with 42 leukemia cases observed on average. The bias is reduced for higher baseline incidence rates and for higher values of the true ERR. As the number of cases increases, the ERR is approximately unbiased. The Firth correction reduces the bias for all cohort sizes to generally around or under 5%. Epidemiological studies showing an association between radiation exposure from pediatric CT and cancer risk, unless very large, may overestimate the magnitude of the relationship, while there is no evidence of an increased chance for false-positive results. Conducting large studies, perhaps by pooling individual studies to increase the number of cases, should be a priority. If this is not possible, Firth correction should be applied to reduce small-sample bias.

A Bayesian spatially explicit estimation of daily egg production: application to anchovy in the Bay of Biscay

Biomass estimates of fish resources by the daily egg production method (DEPM) are sensitive to the high variability of the daily egg production ( P0) and egg mortality (Z) in space. This work presents a Bayesian approach to estimate these parameters. A prior distribution of Z based on literature serves to overcome the biologically implausible Z estimates that can result from frequentist approaches. In addition to the classical estimation of a single P0 over the spawning area, the Bayesian framework allows also the modelling of egg densities in space, by including either spatial random effects, smoothing functions, or kriging like models, providing insights into the spatial variability of P0. The Bayesian approach was applied to the Bay of Biscay anchovy DEPM surveys. Results showed that this Bayesian approximation solved the implausible Z problem resulting in tighter credible intervals of both P0 and Z. Overall, spatial models outperformed the non-spatial model in terms of goodness of fit and resulted in slightly different total production estimates across models for each year, with a moderate decrease on uncertainty estimates.

On the application of one modulus inequality to the mapping theory

The authors study mappings that satisfy some estimate of the distortion of the modulus of families of paths. Under certain conditions on the domains between which the mappings act, we established that, these mappings are Hölder logarithmic continuous at the boundary points. It is known that, the Hölder continuity is established for many classes of mappings, say quasiconformal and quasiregular mappings. In this regard, it is possible to point to the classical distortion estimates by Martio-Rickman-Väisälä type, as well as the estimates related to the modern classes of mappings with finite distortion. In particular, V.I. Ryazanov together with R.R. Salimov and E.O. Sevost'yanov established local distortion estimates for plane and spatial mappings under FMO condition, or under the Lehto-type integral condition. Recently, the second co-author have obtained Hölder logarithmic continuity for the studied class at points of the unit sphere. This article considers the situation of similar mappings of different domains, not only the unit sphere. Namely, we consider mappings between quasiextremal distance domains (QED-domains) and convex domains. Note that, quasiextremal distance domains introduced by Gehring and Martio are structures in which the modulus of families of paths is metrically related to the diameter of sets. Also, convex domains are involved in the formulation of the main result; we consider mappings that surjectively act onto them. In addition, the article contains the formulations and proofs for some other results on this topic. We consider several more cases in detail, in particular when: 1) the definition domain is a domain with a locally quasiconformal boundary, and the image domain is a bounded convex domain; 2) the definition domain is a regular domain in the sense of prime ends, and the image domain is a bounded convex domain; 3) the mapping acts between the QED-domain and the bounded convex domain and has a fixed point. In all three cases, the mapping is Hölder logarithmic continuous; moreover, in case 2), which refers to prime ends, logarithmic continuity should also be understood in terms of prime ends.

Generalised local polynomial estimators of smooth functionals of a distribution function with nonnegative support

This paper introduces generalised smooth asymmetric kernel estimators for smooth functionals with non-negative support. More precisely, for x ∈ [ 0 , ∞ ) , and for a functional, Φ ( x , F ) of the distribution function F, we develop estimators of the functional Φ and its derivatives. The proposed estimator can be seen as the solution to a minimisation problem in the polynomial space L 2 ( q ) , where q is an asymmetric density function. The framework presented here covers several classical nonparametric functional estimators and is linked with estimation using hierarchical kernels. We establish the asymptotic properties of the proposed estimators in the general framework. Furthermore, special attention is paid to comparing the asymptotic mean integrated square error (AMISE) of the proposed estimator with that of other classical symmetric/asymmetric density estimators. Additionally, a comparison of finite sample behaviour is conducted for both density estimation and hazard rate estimation via simulation and real data application.

Human Pose Estimation Based on Efficient and Lightweight High-Resolution Network (EL-HRNet).

As an important direction in computer vision, human pose estimation has received extensive attention in recent years. A High-Resolution Network (HRNet) can achieve effective estimation results as a classical human pose estimation method. However, the complex structure of the model is not conducive to deployment under limited computer resources. Therefore, an improved Efficient and Lightweight HRNet (EL-HRNet) model is proposed. In detail, point-wise and grouped convolutions were used to construct a lightweight residual module, replacing the original 3 × 3 module to reduce the parameters. To compensate for the information loss caused by the network's lightweight nature, the Convolutional Block Attention Module (CBAM) is introduced after the new lightweight residual module to construct the Lightweight Attention Basicblock (LA-Basicblock) module to achieve high-precision human pose estimation. To verify the effectiveness of the proposed EL-HRNet, experiments were carried out using the COCO2017 and MPII datasets. The experimental results show that the EL-HRNet model requires only 5 million parameters and 2.0 GFlops calculations and achieves an AP score of 67.1% on the COCO2017 validation set. In addition, PCKh@0.5mean is 87.7% on the MPII validation set, and EL-HRNet shows a good balance between model complexity and human pose estimation accuracy.

Injection moulding process parameter and strain rate dependence mechanical properties measurement and theoretical estimation of EVA polymer foam

Abstract In this study, the combined experimental and theoretical estimation on elastic characteristics of EVA (ethylene-vinyl acetate) copolymer material is demonstrated. Strain rate dependence stress–strain behavior of EVA in raw and foam material form are measured by the quasi static and dynamic compression test. Simultaneously, the influence of different mould temperature and injection pressure on mechanical properties of EVA foam are investigated. According to the usage scenario of EVA foam in sport footwear application, the EVA foam is generally deformed under higher strain rate as compared with the quasi-static compression condition. For this reason, the stress–strain behavior of EVA foam under high strain rates of 2.66 × 10−3 and 2.66 × 10−2 s−1 are estimated. The analytic results revealed that the strain rate slightly enhanced the modulus of EVA foam, and the foregoing strain rate effect is substituting into the classic Young's modulus estimation equation of closed cell cellular solid. Accordingly, the strain rate dependence Young's modulus of EVA foam is successfully estimated by both experiment and theoretical estimation.

Calibration estimator of population mean in stratified extreme ranked set sampling with simulation study

Calibration estimation sets the original weights to include the known population characteristics of auxiliary variables using constraints. In this article, we have proposed a new calibration estimator of the population mean in stratified extreme ranked set sampling design, which is more efficient and costeffective design against other sampling designs in the literature. A detailed simulation study is carried out to observe the performance of proposed estimators. We have used the information of auxiliary variable to avoid ranking errors in our simulations. We have created samples from a bi-variate normal distribution with different values of ?xy. While one of these variables is taken as the variable of interest, the other is accepted as an auxiliary variable and used in ranking the sample units in each set. As a result of the simulation study using both synthetic and real data sets, we have found that our proposed estimators are more efficient than Sinha et al. [19] calibration estimator and classical stratified estimator.

Incomplete Gamma Kernels: Generalizing Locally Optimal Projection Operators.

We present incomplete gamma kernels, a generalization of Locally Optimal Projection (LOP) operators. In particular, we reveal the relation of the classical localized L1 estimator, used in the LOP operator for point cloud denoising, to the common Mean Shift framework via a novel kernel. Furthermore, we generalize this result to a whole family of kernels that are built upon the incomplete gamma function and each represents a localized Lp estimator. By deriving various properties of the kernel family concerning distributional, Mean Shift induced, and other aspects such as strict positive definiteness, we obtain a deeper understanding of the operator's projection behavior. From these theoretical insights, we illustrate several applications ranging from an improved Weighted LOP (WLOP) density weighting scheme and a more accurate Continuous LOP (CLOP) kernel approximation to the definition of a novel set of robust loss functions. These incomplete gamma losses include the Gaussian and LOP loss as special cases and can be applied to various tasks including normal filtering. Furthermore, we show that the novel kernels can be included as priors into neural networks. We demonstrate the effects of each application in a range of quantitative and qualitative experiments that highlight the benefits induced by our modifications.

Bridging Actions: Generate 3D Poses and Shapes In-Between Photos.

Generating realistic 3D human motion has been a fundamental goal of the game/animation industry. This work presents a novel transition generation technique that can bridge the actions of people in the foreground by generating 3D poses and shapes in-between photos, allowing 3D animators/novice users to easily create/edit 3D motions. To achieve this, we propose an adaptive motion network (ADAM-Net) that effectively learns human motion from masked action sequences to generate kinematically compliant 3D poses and shapes in-between given temporally-sparse photos. Three core learning designs underpin ADAM-Net. First, we introduce a random masking process that randomly masks images from an action sequence and fills masked regions in latent space by interpolation of unmasked images to simulate various transitions under given temporally-sparse photos. Second, we propose a long-range adaptive motion (L-ADAM) attention module that leverages visual cues observed from human motion to adaptively recalibrate the range that needs attention in a sequence, along with a multi-head cross-attention. Third, we develop a short-range adaptive motion (S-ADAM) attention module that weightedly selects and integrates adjacent feature representations at different levels to strengthen temporal correlation. By coupling these designs, the results demonstrate that ADAM-Net excels not only in generating 3D poses and shapes in-between photos, but also in classic 3D human pose and shape estimation.

Analysis of Weibull progressively first-failure censored data with beta-binomial removals

<p>This study examined the estimations of Weibull distribution using progressively first-failure censored data, under the assumption that removals follow the beta-binomial distribution. Classical and Bayesian approaches for estimating unknown model parameters have been established. The estimations included scale and shape parameters, reliability and failure rate metrics as well as beta-binomial parameters. Estimations were considered from both point and interval viewpoints. The Bayes estimates were developed by using the squared error loss and generating samples for the posterior distribution through the Markov Chain Monte Carlo technique. Two interval estimation approaches are considered: approximate confidence intervals based on asymptotic normality of likelihood estimates and Bayes credible intervals. To investigate the performance of classical and Bayesian estimations, a simulation study was considered by various kinds of experimental settings. Furthermore, two examples related to real datasets were thoroughly investigated to verify the practical importance of the suggested methodologies.</p>

Bayesian rank likelihood-based estimation: An application to low birth weight in Ethiopia.

Low birth weight is a significant risk factor associated with high rates of neonatal and infant mortality, particularly in developing countries. However, most studies conducted on this topic in Ethiopia have small sample sizes, often focusing on specific areas and using standard models employing maximum likelihood estimation, leading to potential bias and inaccurate coverage probability. This study used a novel approach, the Bayesian rank likelihood method, within a latent traits model, to estimate parameters and provide a nationwide estimate of low birth weight and its risk factors in Ethiopia. Data from the Ethiopian Demographic and Health Survey (EDHS) of 2016 were used as a data source for the study. Data stratified all regions into urban and rural areas. Among 15, 680 representative selected households, the analysis included complete cases from 10, 641 children (0-59 months). The evaluation of model performance considered metrics such as the root mean square error, the mean absolute error, and the probability coverage of the corresponding 95% confidence intervals of the estimates. Based on the values of root mean square error, mean absolute error, and probability coverage, the estimates obtained from the proposed model outperform the classical estimates. According to the result, 40.92% of the children were born with low birth weight. The study also found that low birth weight is unevenly distributed across different regions of the country with the highest amounts of variation observed in the Afar, Somali and Southern Nations, Nationalities, and Peoples regions as represented by the latent trait parameter of the model. In contrast, the lowest low birth weight variation was recorded in the Addis Ababa, Dire Dawa, and Amhara regions. Furthermore, there were significant associations between birth weight and several factors, including the age of the mother, number of antenatal care visits, order of birth and the body mass index as indicated by the average posterior beta values of (β1= -0.269, CI=-0.320, -0.220), (β2= -0.235, CI=-0.268, -0.202), (β3= -0.120, CI=-0.162, -0.074) and (β5= -0.257, CI=-0.291, -0.225). The study showed that the low birth weight estimates obtained from the latent trait model outperform the classical estimates. The study also revealed that the prevalence of low birth weight varies between different regions of the country, indicating the need for targeted interventions in areas with a higher prevalence. To effectively reduce the prevalence of low birth weight and improve maternal and child health outcomes, it is important to concentrate efforts on regions with a higher burden of low birth weight. This will help implement interventions that are tailored to the unique challenges and needs of each area. Health institutions should take measures to reduce low birth weight, with a special focus on the factors identified in this study.

Analysis of a new jointly hybrid censored Rayleigh populations

<abstract><p>When a researcher wants to perform a life-test comparison study of items made by two separate lines inside the same institution, joint censoring strategies are particularly important. In this paper, we present a new joint Type-Ⅰ hybrid censoring that enables an experimenter to stop the investigation as soon as a pre-specified number of failures or time is first achieved. In the context of newly censored data, the estimates of the unknown mean lifetimes of two different Rayleigh populations are acquired using maximum likelihood and Bayesian inferential techniques. The normality characteristic of classical estimators is used to offer asymptotic confidence interval bounds for each unknown parameter. Against gamma conjugate priors, the Bayes estimators and related credible intervals are gathered about symmetric and asymmetric loss functions. Since classical and Bayes estimators are acquired in closed form, simulation tests can be easily made to evaluate the effectiveness of the proposed methodologies. The efficiency of the suggested approaches is examined in terms of four metrics, namely: Root mean squared error, average relative absolute bias, average confidence length, and coverage probability. To demonstrate the applicability of the offered approaches to real events, two real applications employing data sets from the engineering area are analyzed. As a result, when the experimenter's primary goal is to complete the test as soon as the total number of failures or the threshold period is recorded, the numerical results reveal that the recommended strategy is adaptable and very helpful in completing the study.</p></abstract>

Bayesian estimation : Number of species from Poisson mixed exponential-gamma distribution using objective priors

In this paper, we have considered the Poisson-exponential-gamma mixture model as a species problem. The classical estimation of the parameter has been proposed through profile and conditional likelihood methods. While in the Bayesian paradigm, Bernardo’s reference and Jeffery’s priors have been used to estimate the number of species. Although a Markov Chain Monte Carlo (MCMC) technique has been used for obtaining the Bayes estimates of the number of species parameter. The performances of the proposed estimators are compared in terms of their risks through the simulation study. The applicability, and suitability of the proposed model have been performed using Mount Kenya insect species data.

Model averaging with causal effects for partially linear models

<abstract><p>Treatment effects with heterogeneity and heteroskedasticity are widely studied and applied in many fields, such as statistics and econometrics. The conditional average treatment effect provides an excellent measure of the heterogeneous treatment effect. In this paper, we propose a model averaging estimation for the conditional average treatment effect with partially linear models based on the jackknife-type criterion under heteroscedastic error. Within this context, we provide theoretical justification for our model averaging approach, and we establish asymptotic optimality and weight convergence properties for our model under certain conditions. The performance of our proposed estimator is compared with that of classical estimators by using a Monte Carlo study and empirical analysis.</p></abstract>

Analytical estimations of the side lobe characteristics for the pattern factor of the continuous uniform line source in electromagnetics

This contribution proposes an interesting analytical method to estimate the side lobe characteristics for the pattern factor of the continuous uniform line source in electromagnetics. The new solutions are observed more precise than the classical analytical estimations. Detailed formulas and results are given in explicit validation of the methodology. In particular, to estimate the first side lobe direction, the error between the new method and the truly accurate one is only −0.08%, whereas for the classical analytical study is about 4.87%.

Estymacja parametrów asymetrycznych procesów niegaussowskich o średniej kroczącej metodą maksymalizacji wielomianu stochastycznego PMM

In this paper consider is the application of the Polynomial Maximization Method PMM to find estimates of the parameters of non-Gaussian Moving Average model. This approach is adaptive and is based on the analysis of higher-order statistics. The case of asymmetry of distributions of Moving Average of the stochastic processes is also considered. It is shown that the asymptotic variance of estimates of the Polynomial Maximization Method (2nd order) have such analytical expressions, whose allow to finding estimates and analyzing their uncertainties. Above approach can be significantly less than the variance of the classic estimates based on minimizing the Conditional Sum of Squares or Maximum Likelihood (in the Gaussian case). The increase of accuracy depends on the values of the coefficient’s asymmetry and the kurtosis of residuals. The results of statistical modeling by the Monte Carlo Method confirm the effectiveness of the proposed approach.