Estimation Of Different Types Research Articles

Mathematical model for the prediction of high blood pressure through digital dermatoglyphics

Abstract Background Hypertension is considered not only a disease belonging to the large group of cardiovascular conditions, but also a risk factor that impacts at an economic, social, environmental, and cultural level, among others. Worldwide, it is recognized that arterial hypertension is increasing, with projections that estimate a 30% increase in prevalence by the year 2025. Objective. To develop a mathematical model for the prediction of high blood pressure in young people through digital dermatoglyphics. Methods This study was carried out in a sequence of three activities, which included in the first instance the collection and analysis of all the fingerprints of the upper limbs of each study participant with an image recognition device, to later carry out a characterization. in terms of number and type of footprint on each of the participants’ fingers. Subsequently, a multiple linear regression model was carried out for the right and left hand, which includes information on the type of footprint, number of relevant characteristics and other physical characteristics of everyone with which it is possible to determine an expected value of the pressure. diastolic and systolic arterial pressure, a model that was validated through the estimation of different types of error, the correlation coefficient, and the tracking signal, in a contrast between predicted and observed blood pressure in a second control sample with different participants. those used in the formulation of the mathematical model. Results The model had a significant R-square, allowing to establish a relationship of similarity and explanation of the blood pressure values, from the quantification of the characteristics of the fingerprints of the right and left hand with a significance level of 95%. Conclusions An important advance is presented for the prediction of high blood pressure through genetic markers with a non-invasive and low-cost method. Key messages • This type of research allows progress in the establishment of relationships between the subject's genetic conditions and the potential risk for the development of diseases. • Biotechnology is currently presented as a tool that contributes to the challenges that are generated in different areas such as the health sector.

VLTENet: A Deep-Learning-Based Vertebra Localization and Tilt Estimation Network for Automatic Cobb Angle Estimation.

Scoliosis diagnosis and assessment rely upon Cobb angle estimation from X-ray images of the spine. Recently, automated scoliosis assessment has been greatly improved using deep learning methods. However, in such methods, the Cobb angle is usually predicted based on regression models that don't account for information of the spine structure. Alternatively, the Cobb angle can be estimated indirectly through landmark-detection and vertebra-segmentation, but this approach is still highly sensitive to small detection and segmentation errors. This paper proposes a novel deep-learning architecture, called the vertebra localization and tilt estimation network (VLTENet). This network boosts the Cobb angle estimation accuracy through employing vertebra localization and tilt estimation as network prediction goals. In particular, the VLTENet model innovatively combines a deep high-resolution network (HRNet) and a fully-convolutional U-Net architecture for capturing long-range contextual information, the overall structure, and local details in spinal X-ray images. A feature fusion channel attention (FFCA) module is also proposed to selectively emphasize more informative features and suppress less informative ones. In addition, a joint spine loss function (JS-Loss) is designed to account for the spine shape and other spatial constraints, so that the network focuses more on spine-related regions and ignore irrelevant background regions. Finally, we propose a new Cobb angle estimation method conforms with the clinical Cobb angle calculation guidelines, and produces accurate estimates for different types of scoliosis. Extensive experiments on the publically-available AASCE challenge dataset and on an in-house dataset demonstrated the superiority of our method for the task of automatic assessment of scoliosis.

Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme.

Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, regression, exponential, and difference type estimators for mean estimation under the MRSS design. In this paper, we propose three new mean estimators under the MRSS scheme. Our idea is based on three-fold utilization of supplementary information. Specifically, we utilize the ranks and second raw moments of the supplementary information and the original values of the supplementary variable. The appropriateness of the proposed group of estimators is demonstrated in light of both real and artificial data sets based on the Monte-Carlo simulation. Additionally, the performance comparison is also conducted regarding the reviewed families of estimators. The results are empowered and the predominant execution of the proposed group of estimators is seen throughout the paper.

Direct and economy-wide energy rebound effects in China's transportation sector: a comparative analysis.

Although the existing literature has evaluated the energy rebound effect (ERE) from various aspects, the estimates of different types of ERE obtained by different methods still deserve further discussion. For this reason, by analyzing the pros and cons of assessment methods, this study offers a comparison between the direct and economy-wide EREs based on China's transportation sector during the period of 2003-2019. Specifically, on the basis of the translog cost function, we use the dynamic ordinary least square (DOLS) method with seemingly unrelated regression (SUR) to evaluate the sectoral direct ERE. Considering that the direct ERE estimation is limited by its strict assumptions, this article further assesses the sectoral ERE from a macro perspective. By constructing the dynamic two-stage panel function, the generalized method of moments (GMM) was adopted to estimate the sectoral economy-wide ERE. The empirical results demonstrate that first, capital and labor relative to energy are Morishima substitutes; second, the sectoral short-term economy-wide ERE in China was 71.60%, while the long-term economy-wide ERE was 32.00% during the study period; third, there are significant regional differences in the EREs of Chinese transportation industry both for the short and long term, and the east China demonstrated the highest sectoral economy-wide ERE.

A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.

On use of subsampling of the non-respondents for estimation of distribution function

In this study, we propose a general class of estimators of the finite population distribution function (DF) using two auxiliary variables under subsampling of non-respondents. We use the Hansen and Hurwitz [1] pioneered model in our subsampling technique. Layout of response and non-response classes are discussed in various tables in detail. Expressions for the biases and mean square errors (MSEs) of the estimators are obtained up to first order of approximation. We also obtain the conditions by comparing the proposed estimator with existing estimators. Three real data sets are used to support the theoretical findings. In our findings, it is observed that the proposed class of estimators is more efficient as compared to all other existing estimators including the usual mean estimator, ratio estimator, exponential-ratio estimator, traditional difference estimator, Rao [2] difference estimator, Kumar et al. [3] estimator and many other recent difference type estimators by using the criterion of MSE.

Can immersive virtual reality increase respondents’ certainty in discrete choice experiments? A comparison with traditional presentation formats

Stated preference methods such as discrete choice experiments (DCEs) are used to elicit respondents' preferences and willingness to pay (WTP) for environmental goods or services whose value cannot be observed in actual markets. However, DCEs may deliver biased estimates because of respondents' unfamiliarity with the hypothetical scenarios to be valued. There is evidence that visualization techniques can enhance respondents' cognitive ability and improve the evaluation and interpretation of complex information. We leverage recent technological advances to create an immersive virtual reality environment delivered to respondents via a head-mounted display in order to conduct a split split-sample experiment on the value of urban greenery (i.e., trees, bio-retention planters) using three different presentation formats (text only; video; virtual reality). We find that (i) respondent certainty can be increased by employing more immersive visualization techniques such as virtual reality, and that (ii) the presentation format has a significant impact on WTP estimates for different types of urban green and can change respondents’ rank order for the urban green options considered in the study.

Optimal estimation of population variance in the presence of random non-response using simulation approach

This paper introduces some new ratio- and difference-type estimators of the population variance in the presence of random non-response based on Searls (The utilization of a known coefficient of variation in the estimation procedure. J Am Stat Asso. 1964;59:1225–1226) philosophy. The proposed ratio- and difference-type estimators remain better than the estimators obtained by Ahmeda et al. (Estimation of finite population variance in presence of random non-response using auxiliary variables. Infr Mang Sci. 2005;16(2):73–82) in the presence of random non-response using auxiliary variables. The properties (bias and mean square error) of the proposed estimators presented were derived up to the first-order approximation using the Taylor series approach. Conditions for which the new estimators more efficient than other estimators considered in the study were also established. Numerical examples were conducted, and the results revealed that the proposed class of estimators is more efficient than existing estimators.

On distribution function under two-phase stratified sampling using two auxiliary variables for mean estimation

We suggest a new class of difference-type estimators under two-phase stratified sampling when using information from two auxiliary variables including their distribution functions. The bias and MSE expressions are derived up to first degree of approximations. It is observed that the proposed class of difference-type estimators is more efficient as compared to the estimators: sample mean, ratio, exponential-ratio, traditional difference-types, Chand, Singh and Choudhury, and Anieting and Ezugwu regression type. The comparisons are made in Section 4 and results based on real data sets and simulation are given in Section 5. It is observed that the performances of the generalized class of estimators is remarkable.

A review of global land cover maps in terms of their potential use for habitat suitability modelling

Recently, there has been a significant increase in number of land cover maps available to researchers and they are now more commonly used. The broad variety requires some system for determining the differences between maps and for estimating their applicability for specific research purposes. We focused on comparing land cover maps from the point of view of how the land cover categories used characterize potentially suitable habitats for species. This comparison includes only freely available global land cover projects with resolutions from 1 km to 10 m. The criteria chosen were temporal and spatial resolution, number of classes and map precision. To demonstrate the differences, two areas of different sizes were always chosen. Our results reveal that maps can significantly vary in their estimates of different types of land cover, even at the same spatial resolution. Results also revealed that one type of vegetation in this area is poorly recorded in all land cover maps. Copernicus CGLS-LC100 and ESA CCI-LC maps appear to be the most suitable for evaluating potentially suitable habitats.

Determination of species of some wood veneers using machine vision

AbstractThe veneer industry is widely used in many countries of the world. Properties such as obtaining curved surfaces, concealing defects, usability in different designs, and patterns can be counted as the main advantages of veneers compared to solid wood. Although advanced production and quality control systems have developed in coating production, the number of countries where traditional production methods are used is quite high. The biggest problem in transition to advanced systems is investment costs. Therefore, companies allocate low budgets for growth. This study carried out machine vision estimation of different types of veneers obtained by the quarter‐cutting and rotary methods. Fifteen different veneers samples were used in the study. Over 923 features were extracted from 75 veneer images to determine the selected features. Artificial neural network and decision tree techniques were used as decision‐making algorithms. It was determined that the artificial neural network made predictions with higher accuracy in estimating the veneer type. Rays, annual rings, and light‐dark color contrast were among the parameters effective in machine vision prediction. The importance of data is increasing in businesses that are undergoing digital transformation and automation. Today, data are expressed not only in numbers or texts, but also in features extracted from images. With this study, numerical features were extracted from the veneer images and species predictions were made. It was determined that among the algorithms used, the artificial neural network yielded more accurate results than the decision tree technique.

Sample identification and pedigree reconstruction in Wolverine (Gulo gulo) using SNP genotyping of non-invasive samples

For conservation genetic studies using non-invasively collected samples, genome-wide data may be hard to acquire. Until now, such studies have instead mostly relied on analyses of traditional genetic markers such as microsatellites (SSRs). Recently, high throughput genotyping of single nucleotide polymorphisms (SNPs) has become available, expanding the use of genomic methods to include non-model species of conservation concern. We have developed a 96-marker SNP array for use in applied conservation monitoring of the Scandinavian wolverine (Gulo gulo) population. By genotyping more than a thousand non-invasively collected samples, we were able to obtain precise estimates of different types of genotyping errors and sample dropout rates. The SNP panel significantly outperforms the SSR markers (and DBY intron markers for sexing) both in terms of precision in genotyping, sex assignment and individual identification, as well as in the proportion of samples successfully genotyped. Furthermore, SNP genotyping offers a simplified laboratory and analysis pipeline with fewer samples needed to be repeatedly genotyped in order to obtain reliable consensus data. In addition, we utilised a unique opportunity to successfully demonstrate the application of SNP genotype data for reconstructing pedigrees in wild populations, by validating the method with samples from wild individuals with known relatedness. By offering a simplified workflow with improved performance, we anticipate this methodology will facilitate the use of non-invasive samples to improve genetic management of many different types of populations that have previously been challenging to survey.

Optimality of ratio and regression type estimators using dual of auxiliary variable under non response

The present note addresses the problem of estimation of population mean in the presence of non response. We have proposed the difference and ratio type estimators in two distinct situations of non response using the dual of an auxiliary variable. Some conventional estimators like mean, ratio, product, power transformation, regression, difference, and some other difference type estimators are the special cases of the proposed estimators apart from being less efficient than proposed estimators in the respective situations.The present work can be considered as the efficient extension of a recent work that is based on the dual use of auxiliary information. The theoretical results are obtained under large sample approximation. A comparative study based on real and simulated data is conducted where the results are quite encouraging, providing the improvement over all the discussed works.

Estimation of different types of entropies for the Kumaraswamy distribution.

The estimation of the entropy of a random system or process is of interest in many scientific applications. The aim of this article is the analysis of the entropy of the famous Kumaraswamy distribution, an aspect which has not been the subject of particular attention previously as surprising as it may seem. With this in mind, six different entropy measures are considered and expressed analytically via the beta function. A numerical study is performed to discuss the behavior of these measures. Subsequently, we investigate their estimation through a semi-parametric approach combining the obtained expressions and the maximum likelihood estimation approach. Maximum likelihood estimates for the considered entropy measures are thus derived. The convergence properties of these estimates are proved through a simulated data, showing their numerical efficiency. Concrete applications to two real data sets are provided.

Distinct error rates for reference and nonreference genotypes estimated by pedigree analysis.

Errors in genotype calling can have perverse effects on genetic analyses, confounding association studies, and obscuring rare variants. Analyses now routinely incorporate error rates to control for spurious findings. However, reliable estimates of the error rate can be difficult to obtain because of their variance between studies. Most studies also report only a single estimate of the error rate even though genotypes can be miscalled in more than one way. Here, we report a method for estimating the rates at which different types of genotyping errors occur at biallelic loci using pedigree information. Our method identifies potential genotyping errors by exploiting instances where the haplotypic phase has not been faithfully transmitted. The expected frequency of inconsistent phase depends on the combination of genotypes in a pedigree and the probability of miscalling each genotype. We develop a model that uses the differences in these frequencies to estimate rates for different types of genotype error. Simulations show that our method accurately estimates these error rates in a variety of scenarios. We apply this method to a dataset from the whole-genome sequencing of owl monkeys (Aotus nancymaae) in three-generation pedigrees. We find significant differences between estimates for different types of genotyping error, with the most common being homozygous reference sites miscalled as heterozygous and vice versa. The approach we describe is applicable to any set of genotypes where haplotypic phase can reliably be called and should prove useful in helping to control for false discoveries.

Analysis of Incidence Risk for Different Types of Stroke among the Mayak Worker Cohort

The incidence risk estimates for different types of strokes among the cohort of 22 377 Mayak workers occupationally exposed to prolonged radiation and followed up until the end of 2013 are presented. Information about the work histories and estimates of the liver absorbed doses from external and internal radiations were obtained from the “Mayak Worker Dosimetry System—2013” database; medical data and lifestyle factors were obtained from the “Clinic” medical and dosimetry database. Over the entire follow-up period, 221 cases of intracerebral hemorrhage (ICH), 1463 cases of brain cerebral infarction (BI), and 342 cases of nonspecified stroke (NS) were registered in the study cohort. It was shown that the ICH, BI, and NS incidence risks in the study cohort were significantly associated with nonradiation factors such as gender, age, calendar period, duration of employment at the main plants, smoking, alcohol assumption, body mass index, and comorbidities (arterial hypertension and diabetes), which agreed with the literature data. Analyses did not reveal an effect of external γ-ray exposure on the ICH and BI incidence. However, NS relative risk (RR) was statistically significantly below the one in the groups of workers exposed to external γ-rays at the total absorbed liver doses above 0.20 Gy as compared with workers exposed at doses below 0.10 Gy. BI incidence RR was statistically significantly above the one in the groups of workers exposed to internal α-radiation at the total absorbed liver dose above 0.10 Gy as compared with workers exposed at lower doses. There was no effect of internal exposure on the ICH and NS incidence. These findings agreed with the results obtained earlier for the Mayak worker cohort.

Estimating outcome-specific effects in meta-analyses of multiple outcomes: A simulation study.

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented.

On improved class of difference type estimators for population median in survey sampling

We propose two new improved class of difference type estimators for population median under simple random sampling (SRS) and two-phase sampling (TPS). Expressions for bias and MSE are derived up to first order of approximation. We make a comparison of proposed estimators with all other commonly known estimators in literature. Numerical findings show that the proposed class of estimators perform better as compared to all other estimators considered here.

A modified efficient difference-type estimator for population mean under two-phase sampling design

A modified efficient difference-type estimator for population mean under two-phase sampling design

An Improved Regression Type Estimator of Population Mean with Two Auxiliary Variables in Stratified Double Sampling

Shabbir (Commun Stat Theory Methods 47(1):92–101, 2018) suggested a new difference type estimator of finite population mean in stratified double sampling using the ranks of two auxiliary variables as additional information. In this paper, on applying Searls’s (J Am Stat Assoc 59:1225–1226, 1964) approach to Shabbir’s (2018) estimator, we propose an improved estimator of population mean. The expressions of bias and mean square error of the proposed estimator have been obtained, up to first order of approximation. Theoretical and numerical comparisons of the mean square error of the proposed estimator with that of various existing estimators have been made. It is observed that proposed estimator always performs better than Shabbir’s (2018) estimator and also better than most of the other various existing estimators in the literature of survey sampling.