AN EMPIRICAL STUDY OF TWO CLASSES OF ESTIMATORS FOR PROCESS VARIATION TRANSMISSION

1999 ◽  
Vol 06 (03) ◽  
pp. 289-300 ◽  
Author(s):  
DANIEL Y. T. FONG
Keyword(s):  
Maximum Likelihood ◽  
Final Stage ◽  
Maximum Likelihood Estimators ◽  
Quality Characteristic ◽  
Manufacturing Processes ◽  
Finite Sample ◽  
Interval Estimates ◽  
Moderate Number ◽  
Finite Sample Properties ◽  
Remedial Actions

Manufacturing processes often consist of a number of sequential stages. Of interest is to control the variation in one or more quality characteristics of a production unit at the final stage. By understanding how variation is transmitted and added across the stages, remedial actions in reducing variation at the final stage can be properly planned. With one quality characteristic measured at each stage, a set of naive estimators is previously proposed and shown to perform indistinguishably well with maximum likelihood estimators. Thus naive estimators are more convenient than maximum likelihood estimators as the former exist in closed form while the latter do not. This article considers situations when more than one quality characteristic is measured throughout the stages. Methods of analyzing variation transmission are briefly reviewed and the finite sample properties of naive and maximum likelihood estimators for multivariate measurements are further examined. A broad conclusion is that for moderate number of production units, naive estimators have smaller bias and variability. Furthermore, "proper" naive estimates provide more accurate interval estimates at a given confidence level. Finally, a set of piston-machining data is used for illustration.

Estimation of P(X

2017 ◽  
Vol 34 (7) ◽  
pp. 1111-1122 ◽  
Author(s):  
Soumya Roy ◽  
Biswabrata Pradhan ◽  
E.V. Gijo
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Quality Characteristic ◽  
Logistic Distribution ◽  
Type Ii ◽  
Finite Sample ◽  
Data Set ◽  
Content Type ◽  
Type Ii Censored Data ◽  
Interval Estimators

Purpose The purpose of this paper is to compare various methods of estimation of P(X<Y) based on Type-II censored data, where X and Y represent a quality characteristic of interest for two groups. Design/methodology/approach This paper assumes that both X and Y are independently distributed generalized half logistic random variables. The maximum likelihood estimator and the uniformly minimum variance unbiased estimator of R are obtained based on Type-II censored data. An exact 95 percent maximum likelihood estimate-based confidence interval for R is also provided. Next, various Bayesian point and interval estimators are obtained using both the subjective and non-informative priors. A real life data set is analyzed for illustration. Findings The performance of various point and interval estimators is judged through a detailed simulation study. The finite sample properties of the estimators are found to be satisfactory. It is observed that the posterior mean marginally outperform other estimators with respect to the mean squared error even under the non-informative prior. Originality/value The proposed methodology can be used for comparing two groups with respect to a suitable quality characteristic of interest. It can also be applied for estimation of the stress-strength reliability, which is of particular interest to the reliability engineers.

Estimation of Dynamic Term Structure Models

2012 ◽  
Vol 02 (02) ◽  
pp. 1250008 ◽  
Author(s):  
Gregory R. Duffee ◽  
Richard H. Stanton
Keyword(s):  
Maximum Likelihood ◽  
Term Structure ◽  
Empirical Work ◽  
Interest Rate Risk ◽  
Small Sample ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Finite Sample ◽  
Term Structure Models ◽  
Finite Sample Properties

We study the finite-sample properties of some of the standard techniques used to estimate modern term structure models. For sample sizes and models similar to those used in most empirical work, we reach three surprising conclusions. First, while maximum likelihood works well for simple models, it produces strongly biased parameter estimates when the model includes a flexible specification of the dynamics of interest rate risk. Second, despite having the same asymptotic efficiency as maximum likelihood, the small-sample performance of Efficient Method of Moments (a commonly used method for estimating complicated models) is unacceptable even in the simplest term structure settings. Third, the linearized Kalman filter is a tractable and reasonably accurate estimation technique, which we recommend in settings where maximum likelihood is impractical.

Finite Sample Properties of Several Predictors From an Autoregressive Model

1987 ◽  
Vol 3 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Koichi Maekawa
Keyword(s):  
Maximum Likelihood ◽  
Sample Size ◽  
Asymptotic Expansions ◽  
Autoregressive Model ◽  
Mean Squared Error ◽  
The Other ◽  
Finite Sample ◽  
Squared Error ◽  
Distributional Properties ◽  
Finite Sample Properties

We compare the distributional properties of the four predictors commonly used in practice. They are based on the maximum likelihood, two types of the least squared, and the Yule-Walker estimators. The asymptotic expansions of the distribution, bias, and mean-squared error for the four predictors are derived up to O(T−1), where T is the sample size. Examining the formulas of the asymptotic expansions, we find that except for the Yule-Walker type predictor, the other three predictors have the same distributional properties up to O(T−1).

Topp–Leone Linear Exponential Distribution

2018 ◽  
Vol 33 (1) ◽  
pp. 31-43
Author(s):  
Bol A. M. Atem ◽  
Suleman Nasiru ◽  
Kwara Nantomah
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Finite Sample ◽  
Data Set ◽  
Finite Sample Properties ◽  
Linear Exponential Distribution

Abstract This article studies the properties of the Topp–Leone linear exponential distribution. The parameters of the new model are estimated using maximum likelihood estimation, and simulation studies are performed to examine the finite sample properties of the parameters. An application of the model is demonstrated using a real data set. Finally, a bivariate extension of the model is proposed.

scholarly journals Asymptotic Theory for Regressions with Smoothly Changing Parameters

2013 ◽  
Vol 5 (2) ◽  
pp. 133-162 ◽  
Author(s):  
Eric Hillebrand ◽  
Marcelo C. Medeiros ◽  
Junyue Xu
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Theory ◽  
Asymptotic Properties ◽  
Smooth Transition ◽  
Finite Sample ◽  
Likelihood Estimator ◽  
Output Data ◽  
Asymptotically Normal ◽  
Finite Sample Properties ◽  
Quasi Maximum Likelihood

Abstract: We derive asymptotic properties of the quasi-maximum likelihood estimator of smooth transition regressions when time is the transition variable. The consistency of the estimator and its asymptotic distribution are examined. It is shown that the estimator converges at the usual -rate and has an asymptotically normal distribution. Finite sample properties of the estimator are explored in simulations. We illustrate with an application to US inflation and output data.

Approximating the Finite Sample Bias for Maximum Likelihood Estimators by Using the Score

1996 ◽  
Vol 12 (1) ◽  
pp. 199-199
Author(s):  
Bart Lambrech ◽  
William Perraudin ◽  
Stephen Satchell
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Finite Sample ◽  
Sample Bias ◽  
Finite Sample Bias
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