Using limited dependent variable estimators for estimating percent decay

1993 ◽  
Vol 23 (2) ◽  
pp. 266-274 ◽  
Author(s):  
Valerie M. Lemay ◽  
Antal Kozak ◽  
Peter L. Marshall
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Unexpected Result ◽  
Data Set ◽  
A Value ◽  
Wide Range ◽  
Truncated Normal ◽  
Tobit Estimator

The data used for the estimation of percent decay are bounded by zero and 100. Because a value of 100% indicates that the tree is completely decayed, this value is not observable in nature. However, a value of zero percent is often observed over a wide range of the independent variables. The distribution of percent decay is a combination of a truncated continuous distribution for percent decay greater than zero and a discrete component for the zero percents. The use of ordinary least squares with this type of data results in biased and inconsistent estimates of the coefficients of a percent decay equation. An alternative is the tobit estimator (a combined regression and probit estimator based on a maximum likelihood equation), which results in consistent estimates of the coefficients if the error terms of the model are independent and identically distributed as the truncated normal distribution. A Monte Carlo simulation using data for three species with different proportions of zero percents was performed to compare the ordinary least squares and tobit estimators. As expected, the tobit estimator resulted in quite different estimates of the coefficients of the equations than did ordinary least squares. An unexpected result was that the estimated expected percent decay was slightly more biased for the tobit estimator than for the ordinary least squares estimator, even with a large number of zero percents in the data set. Possible explanations for the Monte Carlo simulation results and recommendations for fitting percent decay equations are given in the paper.

scholarly journals New Estimator for AR (1) Model with Missing Observations

2021 ◽  
Vol 23 (09) ◽  
pp. 147-159
Author(s):  
Mohamed Khalifa Ahmed Issa ◽  
Keyword(s):  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Least Squares ◽  
Unit Root ◽  
Constant Term ◽  
Ordinary Least Squares ◽  
Missing Observations ◽  
New Form ◽  
Near Unit Root

In this paper, new form of the parameters of AR(1) with constant term with missing observations has been derived by using Ordinary Least Squares (OLS) method, Also, the properties of OLS estimator are discussed, moreover, an extension of Youssef [18]has been suggested for AR(1) with constant with missing observations. A comparative study between (OLS), Yule-Walker (YW) and modification of the ordinary least squares (MOLS) is considered in the case of stationary and near unit root time series, using Monte Carlo simulation.

Estimation of Models with Variable Coefficients

1991 ◽  
Vol 3 ◽  
pp. 27-49 ◽  
Author(s):  
John E. Jackson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Least Squares ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Variable Coefficients ◽  
Coefficient Estimates ◽  
Explanatory Variables ◽  
Varying Coefficients

The ordinary least squares (OLS) estimator gives biased coefficient estimates if coefficients are not constant for all cases but vary systematically with the explanatory variables. This article discusses several different ways to estimate models with systematically and randomly varying coefficients using estimated generalized least squares and maximum likelihood procedures. A Monte Carlo simulation of the different methods is presented to illustrate their use and to contrast their results to the biased results obtained with ordinary least squares. Several applications of the methods are discussed and one is presented in detail. The conclusion is that, in situations with variables coefficients, these methods offer relatively easy means for overcoming the problems.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

Monte Carlo simulation of chlorine adsorbed on graphite

1988 ◽  
Vol 66 (4) ◽  
pp. 955-962 ◽  
Author(s):  
Soong-Hyuck Suh ◽  
Seamus F. O'Shea
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Basal Plane ◽  
Thermodynamic Data ◽  
Molecular Chlorine ◽  
Monte Carlo Calculations ◽  
Structure And Dynamics ◽  
Wide Range ◽  
Do So ◽  
Molecule Surface

Monte Carlo calculations are reported for patches of molecular chlorine adsorbed on the basal plane of graphite. A variety of combinations of intermolecular and molecule–surface potentials have been tested, and the resulting structural predictions cover a wide range of behaviour. None of the combinations employed here can explain the recent experimental observations, but it is clear that at least some could be adjusted to do so. However, the lack of thermodynamic data concerning the interaction between the molecules and the surface presents a major obstacle. Further experiments which give information about the energetics of the system are needed before a definitive account of the structure and dynamics can be given.

scholarly journals The Development of an Ordinary Least Squares Parametric Model to Estimate the Cost Per Flying Hour of ‘Unknown’ Aircraft Types and a Comparative Application †

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 104 ◽  
Author(s):  
Ilias Lappas ◽  
Michail Bozoudis
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Ordinary Least Squares ◽  
Parametric Model ◽  
Development Programs ◽  
Cost Estimating ◽  
Life Cycle Stages ◽  
Wide Range ◽  
High Level ◽  
The Cost

The development of a parametric model for the variable portion of the Cost Per Flying Hour (CPFH) of an ‘unknown’ aircraft platform and its application to diverse types of fixed and rotary wing aircraft development programs (F-35A, Su-57, Dassault Rafale, T-X candidates, AW189, Airbus RACER among others) is presented. The novelty of this paper lies in the utilization of a diverse sample of aircraft types, aiming to obtain a ‘universal’ Cost Estimating Relationship (CER) applicable to a wide range of platforms. Moreover, the model does not produce absolute cost figures but rather analogy ratios versus the F-16’s CPFH, broadening the model’s applicability. The model will enable an analyst to carry out timely and reliable Operational and Support (O&S) cost estimates for a wide range of ‘unknown’ aircraft platforms at their early stages of conceptual design, despite the lack of actual data from the utilization and support life cycle stages. The statistical analysis is based on Ordinary Least Squares (OLS) regression, conducted with R software (v5.3.1, released on 2 July 2018). The model’s output is validated against officially published CPFH data of several existing ‘mature’ aircraft platforms, including one of the most prolific fighter jet types all over the world, the F-16C/D, which is also used as a reference to compare CPFH estimates of various next generation aircraft platforms. Actual CPFH data of the Hellenic Air Force (HAF) have been used to develop the parametric model, the application of which is expected to significantly inform high level decision making regarding aircraft procurement, budgeting and future force structure planning, including decisions related to large scale aircraft modifications and upgrades.

scholarly journals Indirect Inference Estimation of Spatial Autoregressions

Econometrics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 34
Author(s):  
Yong Bao ◽  
Xiaotian Liu ◽  
Lihong Yang
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Asymptotic Distribution ◽  
Monte Carlo Study ◽  
Ordinary Least Squares ◽  
Indirect Inference ◽  
Finite Sample ◽  
Spatial Unit ◽  
Asymptotically Normal ◽  
Distribution Result

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

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