scholarly journals Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Sudeep Pradhan ◽  
Byungjeong Song ◽  
Jaeyeon Lee ◽  
Jung-woo Chae ◽  
Kyung Im Kim ◽  
...  
Keyword(s):  
Bayesian Estimation ◽  
Performance Comparison ◽  
Estimation Methods ◽  
Data Sets ◽  
Population Parameters ◽  
First Order ◽  
Conditional Estimation

scholarly journals Efficiency of Ratio, Product, and Regression Estimators under Maximum and Minimum Values, Using Two Auxiliary Variables

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Abdullah Y. Al-Hossain ◽  
Mursala Khan
Keyword(s):  
Real Data ◽  
Data Sets ◽  
Mean Square ◽  
Population Parameters ◽  
Auxiliary Variables ◽  
First Order ◽  
Regression Estimators ◽  
Class Of Estimators ◽  
Mean Square Errors ◽  
Better Than

To obtain the best estimates of the unknown population parameters have been the key theme of the statisticians. In the present paper we have suggested some estimators which estimate the population parameters efficiently. In short we propose a ratio, product, and regression estimators using two auxiliary variables, when there are some maximum and minimum values of the study and auxiliary variables, respectively. The properties of the proposed strategies in terms of mean square errors (variances) are derived up to first order of approximation. Also the performance of the proposed estimators have shown theoretically and these theoretical conditions are verified numerically by taking four real data sets under which the proposed class of estimators performed better than the other previous works.

scholarly journals A new inverse Weibull distribution: properties, classical and Bayesian estimation with applications

2021 ◽  
Vol 48 (3) ◽  
Author(s):  
Ahmed Z. Afify ◽  
◽  
Ahmed I. Shawky ◽  
Mazen Nassar ◽  
◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Real Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Exponential Distributions ◽  
Unknown Parameters ◽  
Large Samples ◽  
Mathematical Properties ◽  
Inverse Weibull Distribution

This article proposes a new extension of the inverse Weibull distribution called, logarithmic transformed inverse Weibull distribution which can provide better fits than some of its well-known extensions. The proposed distribution contains inverse Weibull, inverse Rayleigh, inverse exponential, logarithmic transformed inverse Rayleigh and logarithmic transformed inverse exponential distributions as special sub-models. Our main focus is to derive some of its mathematical properties along with the estimation of its unknown parameters using frequentist and Bayesian estimation methods. We compare the performances of the proposed estimators using extensive numerical simulations for both small and large samples. The importance and potentiality of this distribution is analyzed via two real data sets.

Comparative performance of species richness estimation methods

Parasitology ◽  
1998 ◽  
Vol 116 (4) ◽  
pp. 395-405 ◽  
Author(s):  
B. A. WALTHER ◽  
S. MORAND
Keyword(s):  
Species Richness ◽  
Estimation Method ◽  
Estimation Methods ◽  
Sampling Effort ◽  
Data Sets ◽  
Richness Estimation ◽  
First Order ◽  
Species Richness Estimation ◽  
Total Species Richness ◽  
Total Species

In most real-world contexts the sampling effort needed to attain an accurate estimate of total species richness is excessive. Therefore, methods to estimate total species richness from incomplete collections need to be developed and tested. Using real and computer-simulated parasite data sets, the performances of 9 species richness estimation methods were compared. For all data sets, each estimation method was used to calculate the projected species richness at increasing levels of sampling effort. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. Performance was evaluated with increasing sampling effort and across different model communities. For the real data sets, the Chao2 and first-order jackknife estimators performed best. For the simulated data sets, the first-order jackknife estimator performed best at low sampling effort but, with increasing sampling effort, the bootstrap estimator outperformed all other estimators. Estimator performance increased with increasing species richness, aggregation level of individuals among samples and overall population size. Overall, the Chao2 and the first-order jackknife estimation methods performed best and should be used to control for the confounding effects of sampling effort in studies of parasite species richness. Potential uses of and practical problems with species richness estimation methods are discussed.

A Spectrum-Adaptive Decomposition Method for Effective Atomic Number Estimation Using Dual Energy CT

2020 ◽  
Vol 2020 (14) ◽  
pp. 293-1-293-7
Author(s):  
Ankit Manerikar ◽  
Fangda Li ◽  
Avinash C. Kak
Keyword(s):  
Atomic Number ◽  
Decomposition Method ◽  
Traditional Approach ◽  
Effective Atomic Number ◽  
Dual Energy ◽  
Performance Comparison ◽  
Estimation Methods ◽  
Dual Energy Ct ◽  
Projection Data ◽  
X Ray

Dual Energy Computed Tomography (DECT) is expected to become a significant tool for voxel-based detection of hazardous materials in airport baggage screening. The traditional approach to DECT imaging involves collecting the projection data using two different X-ray spectra and then decomposing the data thus collected into line integrals of two independent characterizations of the material properties. Typically, one of these characterizations involves the effective atomic number (Zeff) of the materials. However, with the X-ray spectral energies typically used for DECT imaging, the current best-practice approaches for dualenergy decomposition yield Zeff values whose accuracy range is limited to only a subset of the periodic-table elements, more specifically to (Z < 30). Although this estimation can be improved by using a system-independent ρe — Ze (SIRZ) space, the SIRZ transformation does not efficiently model the polychromatic nature of the X-ray spectra typically used in physical CT scanners. In this paper, we present a new decomposition method, AdaSIRZ, that corrects this shortcoming by adapting the SIRZ decomposition to the entire spectrum of an X-ray source. The method reformulates the X-ray attenuation equations as direct functions of (ρe, Ze) and solves for the coefficients using bounded nonlinear least-squares optimization. Performance comparison of AdaSIRZ with other Zeff estimation methods on different sets of real DECT images shows that AdaSIRZ provides a higher output accuracy for Zeff image reconstructions for a wider range of object materials.

An alternative distribution to Lindley and Power Lindley distributions with characterizations, different estimation methods and data applications

2020 ◽  
Vol 70 (4) ◽  
pp. 953-978
Author(s):  
Mustafa Ç. Korkmaz ◽  
G. G. Hamedani
Keyword(s):  
Hazard Function ◽  
Mixture Distribution ◽  
Real Data ◽  
Quantile Function ◽  
Estimation Methods ◽  
Data Sets ◽  
Unknown Parameters ◽  
Lorenz Curves ◽  
Proposed Model ◽  
New Distribution

AbstractThis paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.

Moulting growth of the Australian giant crab, Pseudocarcinus gigas

2002 ◽  
Vol 53 (5) ◽  
pp. 869 ◽  
Author(s):  
Richard McGarvey ◽  
Andrew H. Levings ◽  
Janet M. Matthews
Keyword(s):  
South Australia ◽  
Growth Increment ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Most Probable Number ◽  
Minimum Length ◽  
Data Sets ◽  
Probable Number ◽  
Commercial Harvest ◽  
Female Data

The growth of Australian giant crabs, Pseudocarcinus gigas, has not been previously studied. A tagging program was undertaken in four Australian states where the species is subject to commercial exploitation. Fishers reported a recapture sample of 1372 females and 383 males from commercial harvest, of which 190 females and 160 males had moulted at least once. Broad-scale modes of growth increment were readily identified and interpreted as 0 , 1 and 2 moults during time at large. Single-moult increments were normally distributed for six of seven data sets. Moult increments were constant with length for males and declined slowly for three of four female data sets. Seasonality of moulting in South Australia was inferred from monthly proportions captured with newly moulted shells. Female moulting peaked strongly in winter (June and July). Males moult in summer (November and December). Intermoult period estimates for P. gigas varied from 3 to 4 years for juvenile males and females (80–120 mm carapace length, CL), with rapid lengthening in time between moulting events to approximately seven years for females and four and a half years for males at legal minimum length of 150 mm CL. New moulting growth estimation methods include a generalization of the anniversary method for estimating intermoult period that uses (rather than rejects) most capture–recapture data and a multiple likelihood method for assigning recaptures to their most probable number of moults during time at large.

scholarly journals Characteristics and Application of the NHPP Log-Logistic Reliability Model

2018 ◽  
Vol 8 (1) ◽  
pp. 44
Author(s):  
Lutfiah Ismail Al turk
Keyword(s):  
Evaluation Criteria ◽  
Real Data ◽  
Least Square ◽  
Estimation Methods ◽  
Data Sets ◽  
Reliability Model ◽  
Reliability Engineering ◽  
Reliability Models ◽  
Time Domain Data ◽  
Linear Least Square

In this paper, a Nonhomogeneous Poisson Process (NHPP) reliability model based on the two-parameter Log-Logistic (LL) distribution is considered. The essential model&rsquo;s characteristics are derived and represented graphically. The parameters of the model are estimated by the Maximum Likelihood (ML) and Non-linear Least Square (NLS) estimation methods for the case of time domain data. An application to show the flexibility of the considered model are conducted based on five real data sets and using three evaluation criteria. We hope this model will help as an alternative model to other useful reliability models for describing real data in reliability engineering area.

Sign in / Sign up

Export Citation Format

Share Document