scholarly journals A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
R. Pourmousa ◽  
M. Rezapour ◽  
M. Mashinchi
Keyword(s):  
Linear Prediction ◽  
Real Data ◽  
Continuous Data ◽  
Data Set ◽  
Truncated Data ◽  
Fault Inference ◽  
Prediction Approach ◽  
The Relationship ◽  
Threshold Point ◽  
Error Of Measurement

AbstractIn the statistical literature, truncated distributions can be used for modeling real data. Due to error of measurement in truncated continuous data, choosing a crisp trimmed point caucuses a fault inference, so using fuzzy sets to define a threshold pointmay leads us more efficient results with respect to crisp thresholds. Arellano-Valle et al. [2] defined a selection distribution for analysis of truncated data with crisp threshold. In this paper, we define fuzzy multivariate selection distribution that is an extension of the selection distributions using fuzzy threshold. A practical data set with a fuzzy threshold point is considered to investigate the relationship between high blood pressure and BMI.

scholarly journals Spatial pattern and genetic diversity estimates are linked in stochastic models of population differentiation

2000 ◽  
Vol 23 (3) ◽  
pp. 541-544 ◽  
Author(s):  
José Alexandre Felizola Diniz-Filho ◽  
Mariana Pires de Campos Telles
Keyword(s):  
Genetic Diversity ◽  
Spatial Pattern ◽  
Stochastic Models ◽  
Population Differentiation ◽  
Real Data ◽  
Population Heterogeneity ◽  
Data Set ◽  
Mantel’S Test ◽  
The Relationship ◽  
Diversity Estimates

In the present study, we used both simulations and real data set analyses to show that, under stochastic processes of population differentiation, the concepts of spatial heterogeneity and spatial pattern overlap. In these processes, the proportion of variation among and within a population (measured by G ST and 1 - G ST, respectively) is correlated with the slope and intercept of a Mantel's test relating genetic and geographic distances. Beyond the conceptual interest, the inspection of the relationship between population heterogeneity and spatial pattern can be used to test departures from stochasticity in the study of population differentiation.

Residual moveout in anisotropic angle-domain common-image gathers

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. S93-S103 ◽  
Author(s):  
Biondo Biondi
Keyword(s):  
Real Data ◽  
Velocity Estimation ◽  
Aperture Angle ◽  
Data Set ◽  
3D Data ◽  
Wavefield Continuation ◽  
Transverse Isotropic ◽  
Phase Angles ◽  
The Relationship ◽  
General Method

I develop the fundamental concepts for quantitatively relating perturbations in anisotropic parameters to the corresponding reflector movements in angle-domain common-image gathers (ADCIGs) after anisotropic wavefield-continuation migration. The proposed theory potentially enables the application of residual moveout (RMO) analysis of ADCIGs to velocity estimation in realistic anisotropic conditions. I demonstrate that linearization of the relationship between anisotropic velocity parameters and reflector movements can be derived by assuming stationary raypaths. This assumption leads to a fairly simple analytical derivation. I then apply the general method to the particular case of RMO analysis of reflections from flat reflectors in a vertical transverse isotropic (VTI) medium. This analysis yields expressions to predict RMO curves in migrated ADCIGs. These RMO expressions are functions of both the phase aperture angle and the group aperture angle. Several numerical examples demonstrate the accuracy of the RMO curves predicted by my kinematic analysis. The synthetic examples also show that approximating the group angles with the phase angles in the application of the RMO expressions may lead to substantial errors for events reflected at wide aperture angles. The results obtained by migrating a 2D line extracted from a Gulf of Mexico 3D data set confirm the accuracy of the proposed method. The RMO curves predicted by the theory match the RMO function observed in the ADCIGs computed from the real data.

scholarly journals Seismic trace interpolation in the f‐x‐y domain

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1232-1239 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Data Processing ◽  
Linear Prediction ◽  
Interpolation Method ◽  
Prediction Method ◽  
Real Data ◽  
Spatial Prediction ◽  
Fractional Step ◽  
Input Frequency ◽  
Interpolation Algorithms ◽  
The Relationship

Seismic trace interpolation is implemented as a 2‐D (x, y) spatial prediction, performed separately on each frequency (f) slice. This so‐called f‐x‐y domain trace interpolation method is based on the relation that the linear prediction (LP) operator estimated at a given frequency may be used to predict data at a higher frequency but a smaller trace spacing. The relationship originally given for thef‐x domain trace interpolation is successfully extended to the f‐x‐y domain. The extension is achieved by masking the data samples selectively from the input frequency slice to design the LP operators. Two interpolation algorithms using the full‐step and the fractional‐step predictions, respectively, are developed. Both methods use an all‐azimuth prediction in the x‐y domain, but the fractional‐step prediction method is computationally more efficient. While the interpolation method can be applied to a common‐offset cube of 3‐D seismic, it can also be applied to 2‐D seismic traces for prestack data processing. Synthetic and real data examples demonstrate the capability of the interpolation method.

Engineering graphics’ knowledge: its persistence and application evaluation

2015 ◽  
Author(s):  
Zenonas Turskis ◽  
Birutė Juodagalvienė ◽  
Inga Garnytė Sapranavičienė
Keyword(s):  
Data Analysis ◽  
Civil Engineering ◽  
Real Data ◽  
Design Data ◽  
Data Set ◽  
Evaluation Data ◽  
Engineering Graphics ◽  
The Relationship ◽  
Constructional Design

The article deals with the knowledge of engineering graphics, obtained during studies and its application in architectural and constructional design. Data analysis was carried out and the relationship between the exams’ grades in lower and upper semesters was determined. The evaluations of the following subjects have been examined: General Engineering Graphics, Applied Engineering Graphics, Building Architecture and Structures 1, Building Architecture and Structures 2. The present paper describes the development of an approach that uses a real data set. The investigated data illustrates relevant concepts and methods in the application of introductory civil engineering. The creative use of students' scores evaluation data is recommended to facilitate the learning of civil engineering. The course has enrolments of approximately 250 students.

scholarly journals Relationship Between Performance Intelligence Quotient and Physical Characteristics

2020 ◽  
Vol 7 (11) ◽  
pp. 6256-6262
Author(s):  
Mahashweta Das ◽  
Chiranjib Ghosh
Keyword(s):  
Intelligence Quotient ◽  
Brain Size ◽  
Real Data ◽  
Random Variable ◽  
Physical Characteristics ◽  
Data Set ◽  
Constant Variance ◽  
And Gender ◽  
Performance Intelligence Quotient ◽  
The Relationship

Generally, it may be expected that physical characteristics such as brain size, height, weight, gender and body mass index (BMI) can be associated with the performance intelligence quotient (PIQ) score. The current report examines the relationship between PIQ and physical characteristics such as brain size, height, weight, gender and BMI based on a real data set. It is derived herein that PIQ is non-constant variance random variable, and its mean is positively associated with brain size (P=0.0002) and negatively associated with height (P=0.0046). Variance of PIQ is negatively partially associated with brain size (P=0.0903). It is also independent of weight, BMI and gender. PIQ is higher for the individuals with larger brain size, shorter height and irrespective of gender, body weight and BMI.

scholarly journals Robust estimation of the extreme value index of Pareto-type distributions under random truncation with applications

2021 ◽  
pp. 235-245
Author(s):  
Yahia Djabrane ◽  
Zahnit Abida ◽  
Brahimi Brahim
Keyword(s):  
Real Data ◽  
Extreme Value ◽  
Robust Estimator ◽  
Extreme Value Index ◽  
Data Set ◽  
Truncated Data ◽  
Brake Pads ◽  
Consistency And Asymptotic Normality ◽  
Pareto Type Distributions ◽  
Random Truncation

In this paper, we introduce a new robust estimator for the extreme value index of Pareto-type distributions under randomly right-truncated data and establish its consistency and asymptotic normality. Our considerations are based on the Lynden-Bell integral and a useful huberized M-functional and M-estimators of the tail index. A simulation study is carried out to evaluate the robustness and the nite sample behavior of the proposed estimator.  Extreme quantiles estimation is also derived and applied to real data-set of lifetimes of automobile brake pads.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

2020 ◽  
pp. 507-522
Author(s):  
Parisa Torkaman
Keyword(s):  
Least Squares ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Real Data ◽  
Minimum Variance ◽  
Cumulative Distribution ◽  
Estimation Methods ◽  
Data Set ◽  
Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

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