scholarly journals On Shrinkage Estimation for R(s, k) in Case of Exponentiated Pareto Distribution

2019 ◽  
Vol 32 (1) ◽  
pp. 152 ◽  
Author(s):  
Eman Ahmed Abdulateef ◽  
Abbas Najim Salman
Keyword(s):  
Pareto Distribution ◽  
Mean Squared Error ◽  
Multicomponent System ◽  
Estimation Method ◽  
Shrinkage Estimation ◽  
Scale Parameters ◽  
Squared Error ◽  
Simulation Based ◽  
Independent Distribution ◽  
Two Parameters

   This paper concerns with deriving and estimating the reliability of the multicomponent system in stress-strength model R(s,k), when the stress and strength are identical independent distribution (iid), follows two parameters Exponentiated Pareto Distribution(EPD) with the unknown shape and known scale parameters. Shrinkage estimation method including Maximum likelihood estimator (MLE), has been considered. Comparisons among the proposed estimators were made depending on simulation based on mean squared error (MSE) criteria.

scholarly journals Estimating Systematic Risk under Extremely Adverse Market Conditions*

2017 ◽  
Vol 17 (3) ◽  
pp. 432-461 ◽  
Author(s):  
Maarten R C van Oordt ◽  
Chen Zhou
Keyword(s):  
Mean Squared Error ◽  
Explanatory Variable ◽  
Asymptotic Properties ◽  
Estimation Method ◽  
Tail Dependence ◽  
Stock Portfolios ◽  
Squared Error ◽  
Regression Approach ◽  
Model Coefficient ◽  
Heavy Tailed

AbstractThis paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties. Simulations show that our estimation method yields a lower mean-squared error than regressions conditional on tail observations. In an empirical application, we illustrate the better performance of our approach relative to the conditional regression approach in projecting the losses of industry-specific stock portfolios in the event of a market crash.

scholarly journals Applying the Shrinkage Technique for Estimating the Scale Parameter of Weighted Rayleigh Distribution

2020 ◽  
pp. 2335-2340
Author(s):  
Intesar Obeid Hassoun ◽  
Adel Abdulkadhim Hussein
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
Rayleigh Distribution ◽  
Squared Error ◽  
Simulation Based

This paper includes the estimation of the scale parameter of weighted Rayleigh distribution using well-known methods of estimation (classical and Bayesian). The proposed estimators were compared using Monte Carlo simulation based on mean squared error (MSE) criteria. Then, all the results of simulation and comparisons were demonstrated in tables. 

scholarly journals PENERAPAN METODE PENDUGAAN AREA KECIL (SMALL AREA ESTIMATION) PADA PENENTUAN PROPORSI RUMAH TANGGA MISKIN DI KABUPATEN KLUNGKUNG

2013 ◽  
Vol 2 (3) ◽  
pp. 35 ◽  
Author(s):  
PUTU EKA ARIWIJAYANTHI ◽  
I WAYAN SUMARJAYA ◽  
TJOKORDA BAGUS OKA
Keyword(s):  
Small Area ◽  
Empirical Bayes ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Parameter Estimates ◽  
Bayes Methods ◽  
Mean Squared Errors ◽  
Direct Estimation ◽  
Squared Error ◽  
Empirical Bayes Methods

Small area is an area with insufficient sample for direct estimation. Limited survey objects, cause direct estimation can not produce better parameter estimates. Based on this, an indirect estimation method called empirical Bayes is used to obtain a better estimate. This study will compare means squared error by  direct estimation method and empirical Bayes method to find a better method on a small area. Jackknife is used to get the means squared error in the empirical Bayes. The results is, empirical Bayes methods give a better parameters based on mean squared errors. Empirical Bayes can produce a smaller mean squared error more than direct estimation in small area.

scholarly journals Parameter Estimation of Photovoltaic Models via Cuckoo Search

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jieming Ma ◽  
T. O. Ting ◽  
Ka Lok Man ◽  
Nan Zhang ◽  
Sheng-Uei Guan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Cuckoo Search ◽  
High Accuracy ◽  
Flight Behavior ◽  
Squared Error ◽  
Simulation Results ◽  
Bioinspired Algorithms ◽  
Significant Attention

Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE) value. The proposed method outperforms other algorithms applied in this study.

scholarly journals Impact of subsampling and tree depth on random forests

2018 ◽  
Vol 22 ◽  
pp. 96-128 ◽  
Author(s):  
Roxane Duroux ◽  
Erwan Scornet
Keyword(s):  
Decision Trees ◽  
Ensemble Learning ◽  
Random Forests ◽  
Mean Squared Error ◽  
Mach Learn ◽  
Learning Methods ◽  
Data Set ◽  
Squared Error ◽  
Tree Construction ◽  
Two Parameters

Random forests are ensemble learning methods introduced by Breiman [Mach. Learn. 45 (2001) 5–32] that operate by averaging several decision trees built on a randomly selected subspace of the data set. Despite their widespread use in practice, the respective roles of the different mechanisms at work in Breiman’s forests are not yet fully understood, neither is the tuning of the corresponding parameters. In this paper, we study the influence of two parameters, namely the subsampling rate and the tree depth, on Breiman’s forests performance. More precisely, we prove that quantile forests (a specific type of random forests) based on subsampling and quantile forests whose tree construction is terminated early have similar performances, as long as their respective parameters (subsampling rate and tree depth) are well chosen. Moreover, experiments show that a proper tuning of these parameters leads in most cases to an improvement of Breiman’s original forests in terms of mean squared error.

scholarly journals Evaluation of Penman-Monteith Model Based on Sentinel-2 Data for the Estimation of Actual Evapotranspiration in Vineyards

2021 ◽  
Vol 13 (3) ◽  
pp. 478
Author(s):  
Víctor García-Gutiérrez ◽  
Claudio Stöckle ◽  
Pilar Macarena Gil ◽  
Francisco Javier Meza
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Actual Evapotranspiration ◽  
Vitis Vinifera L ◽  
Landsat 8 ◽  
Temporal Scales ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Sentinel 2

Water scarcity is one of the most important problems of agroecosystems in Mediterranean and semiarid areas, especially for species such as vineyards that largely depend on irrigation. Actual evapotranspiration (ET) is a variable that represents water consumption of a crop, integrating climate and biophysical variables. Actual evapotranspiration models based on remote sensing data from visible bands of Sentinel-2, including Penman-Monteith–Stewart (RS-PMS) and Penman-Monteith–Leuning (RS-PML), were evaluated at different temporal scales in a Cabernet Sauvignon vineyard (Vitis vinifera L.) located in central Chile, and their performance compared with independent ET measurements from an eddy covariance system (EC) and outputs from models based on thermal infrared data from Landsat 7 and Landsat 8, such as Mapping EvapoTranspiration with high Resolution and Internalized Calibration (METRIC) and Priestley–Taylor Two-Source Model (TSEB-PT). The RS-PMS model showed the best goodness of fit for all temporal scales evaluated, especially at instantaneous and daily ET, with root mean squared error (RMSE) of 28.9 Wm−2 and 0.52 mm day−1, respectively, and Willmott agreement index (d1) values of 0.77 at instantaneous scale and 0.7 at daily scale. Additionally, both approaches of RS-PM model were evaluated incorporating a soil evaporation estimation method, one considering the soil water content (fSWC) and the other hand, using the ratio of accumulated precipitation and equivalent evaporation (fZhang), achieving the best fit at instantaneous scale for RS-PMS fSWC method with relative root mean squared error (%RMSE) of 15.2% in comparison to 58.8% of fZhang. Finally, the relevance of the RS-PMS model was highlighted in the assessment and monitoring of vineyard drip irrigation in terms of crop coefficient (Kc) estimation, which is one of the methods commonly used in irrigation planning, yielding a comparable Kc to the one obtained by the EC tower with a bias around 9%.

scholarly journals Estimation of the Shape Parameter of a Wear-Out Failure Period for a Three-Parameter Weibull Distribution in a Small Sample

2020 ◽  
Vol 9 (6) ◽  
pp. 39
Author(s):  
Toru Ogura ◽  
Takatoshi Sugiyama ◽  
Nariaki Sugiura
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Sample Size ◽  
Unbiased Estimator ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Minimum Variance ◽  
Small Sample ◽  
Scale Parameters ◽  
Squared Error

We propose a method to estimate a shape parameter for a three-parameter Weibull distribution. The proposed method first derives an unbiased estimator for the shape parameter independent of the location and scale parameters and then estimates the shape parameter using a minimum-variance linear unbiased estimator. Since the proposed method is expressed using a hyperparameter, its optimal hyperparameter is searched using Monte Carlo simulations. The recommended hyperparameter used for estimating the shape parameter depends on the sample size, and this causes no problems since the sample size is known when data is obtained. The proposed method is evaluated using a bias and a root mean squared error, and the results are very promising when the population shape parameter is 2 or more in the Weibull distribution representing the wear-out failure period. A numerical dataset is analyzed to demonstrate the practical use of the proposed method.

scholarly journals Efficient Numerical Pricing of American Call Options Using Symmetry Arguments

2019 ◽  
Vol 12 (2) ◽  
pp. 59 ◽  
Author(s):  
Lars Stentoft
Keyword(s):  
Monte Carlo ◽  
Relative Efficiency ◽  
Mean Squared Error ◽  
Real Life ◽  
Relative Importance ◽  
Call Options ◽  
Squared Error ◽  
Simulation Based ◽  
Asset Volatility ◽  
Symmetric Method

This paper demonstrates that it is possible to improve significantly on the estimated call prices obtained with the regression and simulation-based least-squares Monte Carlo method by using put-call symmetry. The results show that, for a large sample of options with characteristics of relevance in real-life applications, the symmetric method performs much better on average than the regular pricing method, is the best method for most of the options, never performs poorly and, as a result, is extremely efficient compared to the optimal, but unfeasible method that picks the method with the smallest Root Mean Squared Error (RMSE). A simple classification method is proposed that, by optimally selecting among estimates from the symmetric method with a reasonably small order used in the polynomial approximation, achieves a relative efficiency of more than 98 % . The relative importance of using the symmetric method increases with option maturity and with asset volatility. Using the symmetric method to price, for example, real options, many of which are call options with long maturities on volatile assets, for example energy, could therefore improve the estimates significantly by decreasing their bias and RMSE by orders of magnitude.

Estimating Percentiles of Nonnormal Anthropometric Populations Final Report

1978 ◽  
Vol 22 (1) ◽  
pp. 445-449 ◽  
Author(s):  
H. F. Martz
Keyword(s):  
Grip Strength ◽  
Air Force ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Final Report ◽  
Nonparametric Method ◽  
Root Finding ◽  
Squared Error ◽  
Gaussian Method ◽  
Normally Distributed

The most commonly used method for estimating percentiles of anthropometric populations is based on the assumption that the population is normally distributed. This assumption is approximately true for many such variables, e.g., hip breadth. On the other hand, numerous nonnormally distributed anthropometric populations are known to exist, e.g., grip strength. The question of how to estimate percentiles of nonnormal populations is addressed here. A nonparametric percentile estimation method, based on the use of a kernel-type probability density function estimator, is presented. A “nonparametric” method is defined as a method that does not make or require any assumption about the statistical distribution of the underlying population. Thus, the method can be applied to any population of anthropometric data, regardless of the normality of the data. The method is simple to use; however, a single nonlinear equation must be numerically solved on a computer by any one of numerous well-documented nonlinear root finding methods. Two examples are used to illustrate the method. In the first example, selected samples of size 50 of hip breadth data are randomly drawn from a population of size 2420 observations from the 1967 anthropometric survey of U.S. Air Force flying personnel. The proposed method is compared to the standard gaussian method. Since this population was selected as normally distributed, the standard method outperforms the proposed nonparametric method. In the case of grip-strength data, the proposed method yields more accurate estimates, in a mean squared error sense, of the upper percentiles of this population. For anthropometric distributions known to be nonnormal or where normality cannot be assumed, the proposed nonparametric method appears a method for consideration.

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