scholarly journals A Comparison of the Methods for Estimation of Reliability Function for Burr-XII Distribution by Using Simulation.

2013 ◽  
Vol 10 (1) ◽  
pp. 85-96
Author(s):  
Baghdad Science Journal
Keyword(s):  
Empirical Study ◽  
Sample Size ◽  
Mean Square Error ◽  
Shape Parameter ◽  
Reliability Function ◽  
Mean Square ◽  
Simulation Experiments ◽  
Two Parameters ◽  
Parameter Values ◽  
Better Than

This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values

scholarly journals A Class of Estimators for Finite Population Mean in Double Sampling under Nonresponse Using Fractional Raw Moments

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Manzoor Khan ◽  
Javid Shabbir ◽  
Zawar Hussain ◽  
Bander Al-Zahrani
Keyword(s):  
Empirical Study ◽  
Mean Square Error ◽  
Finite Population ◽  
Degree Of Approximation ◽  
Double Sampling ◽  
Mean Square ◽  
Population Mean ◽  
Class Of Estimators ◽  
Better Than

This paper presents new classes of estimators in estimating the finite population mean under double sampling in the presence of nonresponse when using information on fractional raw moments. The expressions for mean square error of the proposed classes of estimators are derived up to the first degree of approximation. It is shown that a proposed class of estimators performs better than the usual mean estimator, ratio type estimators, and Singh and Kumar (2009) estimator. An empirical study is carried out to demonstrate the performance of a proposed class of estimators.

Generalized Levinson–Durbin Sequences and Binomial Coefficients

Integers ◽  
2009 ◽  
Vol 9 (2) ◽  
Author(s):  
Paul Shaman
Keyword(s):  
Stochastic Process ◽  
Sample Size ◽  
Mean Square Error ◽  
Discrete Time ◽  
Minimum Mean Square Error ◽  
Stationary Stochastic Process ◽  
Binomial Coefficients ◽  
Stationary Processes ◽  
Mean Square ◽  
Special Case

AbstractThe Levinson–Durbin recursion is used to construct the coefficients which define the minimum mean square error predictor of a new observation for a discrete time, second-order stationary stochastic process. As the sample size varies, the coefficients determine what is called a Levinson–Durbin sequence. A generalized Levinson–Durbin sequence is also defined, and we note that binomial coefficients constitute a special case of such a sequence. Generalized Levinson–Durbin sequences obey formulas which generalize relations satisfied by binomial coefficients. Some of these results are extended to vector stationary processes.

scholarly journals Using simulation to estimate parameters and reliability function for extreme value distribution

2010 ◽  
Vol 16 (59) ◽  
pp. 1
Author(s):  
Makki Akram
Keyword(s):  
Mean Square Error ◽  
Extreme Value Distribution ◽  
Reliability Function ◽  
Extreme Value ◽  
Value Distribution ◽  
Mean Square

يتضمن هذا البحث تقدير معلمتي القياس والموقع  والدالة المعولية  لتوزيع القيمة المتطرفة (EXV)  بواسطة طريقتين وهما:- طريقة الامكان الاعظم (MLE) . طريقة العزوم الاحتمالية المرجحة (PWM) . حيث تم توليد بيانات عند حجوم عينات مختلفة (n=10,25,50,100)، واعطيت قيم افتراضية اولية للمعلمتين  و، وكررت تجارب المحاكاة (RP=1000)  ، وقدرت دالة المعولية عند الزمنين (T=2,5) ،ومن ثم اعتمد المقياس الاحصائي متوسط مربع الخطأ Mean Square Error (MSE)) ) للمقارنة بين نتائج المقدرات,ولخصت جميع النتائج في جداول خاصة اعدت لهذا الغرض، ولوحظ ان مقدرات (MLE) كانت افضل من(PWM) لمعظم الحالات المدروسة.

scholarly journals Uncertainties Brought by Weight Assignment in Ecosystem Health Modelling

2018 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Chuangye Song
Keyword(s):  
Sample Size ◽  
Mean Square Error ◽  
Ecosystem Health ◽  
Objective Method ◽  
Relative Importance ◽  
Mean Square ◽  
Weighting Method ◽  
Weight Assignment ◽  
Entropy Variation ◽  
Weighting Methods

Weight assignment is the most important step in ecosystem health modelling. However, few researches were conducted to test the uncertainties brought by weighting methods in ecosystem health modelling. In this research, aimed to test the rationality and uncertainties brought by objective weighting methods, we made a comparison between different objective weighting methods (Entropy, Variation coefficient, Mean square error, Critic). We found that (1) the weights assigned by different objective method are quite different; (2) the variation of sample size does not exert significant influences on weight assignment. However, the weight of indicator has the tendency of increasing or decreasing with the increment of sample size; (3) the weights assigned by these four objective methods were not able to reflect the actual relative importance of indicators. Therefore, we don’t advise to use objective weighting method as the sole approach to assign the weight of indicator in ecosystem health modelling.

scholarly journals Ameliorated Ratio Estimator of Population Mean Using New Linear Combination

2020 ◽  
pp. 76-79
Author(s):  
T. A. Raja ◽  
S. Maqbool
Keyword(s):  
Linear Combination ◽  
Mean Square Error ◽  
Auxiliary Variable ◽  
Ratio Estimator ◽  
Mean Square ◽  
Population Mean ◽  
Main Variable ◽  
Better Than

We propose a new modified ratio estimator of population mean of the main variable using the linear combination of known values of Co-efficient of Kurtosis and Tri-Mean of the auxiliary variable. Mean Square Error (MSE) and bias of the proposed estimator is calculated and compared with the existing estimator. The comparison is demonstrated numerically which shows that the proposed estimator performs better than the existing estimators.

scholarly journals Parameter Estimation of Inverse Rayleigh Distribution under Competing Risk Model for Masked data

2015 ◽  
Vol 20 (2) ◽  
pp. 122-127 ◽  
Author(s):  
M.S. Panwar ◽  
Bapat Akanshya Sudhir ◽  
Rashmi Bundel ◽  
Sanjeev K. Tomer
Keyword(s):  
Sample Size ◽  
Mean Square Error ◽  
Confidence Intervals ◽  
Risk Model ◽  
Life Time ◽  
Real Data ◽  
Rayleigh Distribution ◽  
Mean Square ◽  
True Value ◽  
The Mean

This paper tries to derive maximum likelihood estimators (MLEs) for the parameters of the inverse Rayleigh distribution (IRD) when the observed data is masked. MLEs, asymptotic confidence intervals (ACIs) and boot-p confidence intervals (boot-p CIs) for the lifetime parameters have been discussed. The simulation illustrations provided that as the sample size increases the estimated value approaches to the true value, and the mean square error decreases with the increase in sample size, and mean square error increases with increase in level of masking, the ACIs are always symmetric and the boot-p CIs approaches to symmetry as the sample size increases whereas the mean life time due to the local spread of the disease is less than that due to the metastasis spread in case of real data set.Journal of Institute of Science and Technology, 2015, 20(2): 122-127

scholarly journals Sample Size determination for Censored Data

2021 ◽  
Vol 12 (2) ◽  
pp. 267-269
Author(s):  
Naseem Asghar ◽  
Umair Khalil ◽  
Dost Muhammad Khan ◽  
Zardad Khan ◽  
Iftikhar Ud Din
Keyword(s):  
Sample Size ◽  
Mean Square Error ◽  
Censored Data ◽  
Standard Error ◽  
Sample Size Determination ◽  
Mean Square ◽  
Size Determination ◽  
Kaplan Meier ◽  
M Estimator ◽  
Point Estimator

This study aims to describe sample size determination procedure in survival analysis using a real-world example. In this method simulation is used for sample size and precision calculations with censored data that concentrates on various sample sizes involved in carrying out the estimates and precision calculation. The Kaplan-Meier (K-M) estimator is chosen as a point estimator, and the precision measurement focuses on the mean square error, standard error, and confidence limits. Information obtained on the recovery time, in days, of patients from the population are compared with results taken from the sample group. Results showed a cutoff point of sample of size 675 on the basis of mean square error, standard error and confidence limit. 

scholarly journals A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Maria Javed ◽  
Muhammad Irfan ◽  
Sajjad Haider Bhatti ◽  
Ronald Onyango
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Auxiliary Information ◽  
Stratified Random Sampling ◽  
Mean Square ◽  
Population Mean ◽  
New Family ◽  
Simulation Based ◽  
Better Than

This study suggests a new optimal family of exponential-type estimators for estimating population mean in stratified random sampling. These estimators are based on the traditional and nontraditional measures of auxiliary information. Expressions for the bias, mean square error, and minimum mean square error of the proposed estimators are derived up to first order of approximation. It is observed that proposed estimators perform better than the traditional estimators (unbiased, combined ratio, and combined regression) and other recent estimators. A real dataset is used to highlight the applicability of proposed estimators. In addition, a simulation study is carried out to assess the performance of new family as compared to other estimators.

FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling

1991 ◽  
Vol 3 (3) ◽  
pp. 375-385 ◽  
Author(s):  
A. D. Back ◽  
A. C. Tsoi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Square Error ◽  
Network Architecture ◽  
Learning Rule ◽  
Mean Square ◽  
Time Series Modeling ◽  
Neural Network Architecture ◽  
Mean Square Error Criterion ◽  
Better Than

A new neural network architecture involving either local feedforward global feedforward, and/or local recurrent global feedforward structure is proposed. A learning rule minimizing a mean square error criterion is derived. The performance of this algorithm (local recurrent global feedforward architecture) is compared with a local-feedforward global-feedforward architecture. It is shown that the local-recurrent global-feedforward model performs better than the local-feedforward global-feedforward model.

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