A Note on Weighted Least‐squares Estimation of the Shape Parameter of the Weibull Distribution

This study proposes an alternative to the weighted least‐squares (WLS) procedure for estimating the shape parameter of the Weibull distribution. Bergman (Journal of Materials Science Letters 1986; 5:611–614), Faucher and Tyson (F&T) (Journal of Materials Science Letters 1988; 7:1199–1203) suggested using different WLS approaches for Weibull parameters. However, the simulation results show that the novel approach is better than that of Bergman, and is not significantly different from that of F&T. Furthermore, the novel approach is also simpler and easier to comprehend. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimation of the Shape Parameter of the Weibull Distribution Using Linear Regression Methods: Non‐Censored Samples

The two‐parameter Weibull distribution is one of the most widely applied probability distributions, particularly in reliability and lifetime modelings. Correct estimation of the shape parameter of the Weibull distribution plays a central role in these areas of statistical analysis. Many different methods can be used to estimate this parameter, most of which utilize regression methods. In this paper, we presented various regression methods for estimating the Weibull shape parameter and an experimental study using classical regression methods to compare the results of the methods. A complete list of the parameter estimators considered in this study is as follows: ordinary least squares (OLS), weighted least squares (WLS, Bergman, F&T, Lu), non‐parametric robust Theil's (Theil) and weighted Theil's (WeTheil), robust Winsorized least squares (WinLS), and M‐estimators (Huber, Andrew, Tukey, Cauchy, Welsch, Hampel and Logistic). Estimator performances were compared based on bias and mean square error criteria using Monte‐Carlo simulations. The simulation results demonstrated that for small, complete, and non‐outlier data sets, the Bergman, F&T, and Lu estimators are more efficient than the others. When the data set contains one or two outliers in the X direction, Theil is the most efficient estimator. Copyright © 2012 John Wiley & Sons, Ltd.     

Weighted least‐squares estimation of the shape parameter of the Weibull distribution

The purpose of this paper is to propose the weighted least‐squares procedure for estimating the shape parameter of the Weibull distribution. Results from simulation studies illustrate the mean‐squared error of the weighted least‐squares estimator is smaller than competing procedures in all cases considered. Copyright © 2001 John Wiley & Sons, Ltd.     

The Constant Shape Parameter Assumption in Weibull Regression

ABSTRACT

The usual assumption in Weibull regression is that the scale parameter is a function of the predictor variables, and the shape parameter is constant. We consider the problem of estimating parameters in the presence of a nonconstant shape parameter and the effect of assuming a constant shape parameter when it really is not constant. We consider both classical and Bayesian methods of estimation. The misspecification of a constant shape parameter can lead to a loss of power for tests regarding the slope parameters. We find that prediction intervals can be inaccurate when the shape parameter is incorrectly assumed to be constant.     

三参数Weibull分布参数估计求法改进

对文[1]中求三参数威布尔分布位置参数估计的方法做了改进,从而提高了计算的精度与速度。     

Monitoring the Shape Parameter of a Weibull Regression Model in Phase II Processes

In this paper, the interest is focused on monitoring profiles with Weibull distributed‐response and common shape parameter γ in phase II processes. The monitoring of such profiles is completely possible by taking the natural logarithm of the Weibull‐distributed response. This is equivalent to characterize the correspondent process by an extreme value linear regression model with common scale parameter σ = γ^?1. It was found out that from the monitoring of the common log‐scale parameter of the extreme value linear regression model, with the help of a simple scheme, it can be obtained important information about the deterioration of the entire process assuming the β coefficients as nuissance parameters that do not have to be known but stable. Control charts are based on the relative log‐likelihood ratio statistic defined for the log‐scale parameter of the log‐transformation of the Weibull‐distributed response and its respective signed square root. It was also found out that some existing adjustments are needed in order to improve the accuracy of using the distributional properties of the monitoring statistics for relatively small and moderate sample sizes. Simulation studies suggest that resulting charts have appealing properties and work fairly acceptable when non‐large enough samples are available at discrete sampling moments. Detection abilities of the studied corrected control schemes improve when sample size increases. Copyright © 2014 John Wiley & Sons, Ltd.     

Studies on the Effects of Estimator Selection in Robust Parameter Design under Asymmetric Conditions

The primary goal of robust parameter design (RPD) is to determine the optimum operating conditions that achieve process performance targets while minimizing variability in the results. To achieve this goal, typical approaches to RPD problems use ordinary least squares methods to obtain response functions for the mean and variance by assuming that the experimental data follow a normal distribution and are relatively free of contaminants or outliers. Consequently, the most common estimators used in the initial tier of estimation are the sample mean and sample variance, as they are very good estimators when these assumptions hold. However, it is often the case that such assumed conditions do not exist in practice; notably, that inherent asymmetry pervades system outputs. If unaccounted for, such conditions can affect results tremendously by causing the quality of the estimates obtained using the sample mean and standard deviation to deteriorate. Focusing on asymmetric conditions, this paper examines several highly efficient estimators as alternatives to the sample mean and standard deviation. We then incorporate these estimators into RPD modeling and optimization approaches to ascertain which estimators tend to yield better solutions when skewness exists. Monte Carlo simulation and numerical studies are used to substantiate and compare the performance of the proposed methods with the traditional approach. Copyright © 2012 John Wiley & Sons, Ltd.     

On the upper truncated Weibull distribution and its reliability implications

The characteristics and application of the truncated Weibull distribution are studied in this paper. This distribution is applicable to the situation where the test data are bounded in an interval because of test conditions, cost and other restrictions. An important property of the truncated Weibull distribution is that it can have bathtub-shaped failure rate function. In this paper, the parametric analysis and parameter estimation methods of the distribution are investigated. Both the graphical approach and the maximum likelihood estimation are considered. The applicability of this distribution to modeling lifetime data is illustrated by an example and the results of comparisons to other competitive models in modeling the given data are also presented. Moreover, the possible application of the distribution to modeling component or system failure is discussed.     

New EWMA control charts for monitoring the Weibull shape parameter

In this article, we first propose a new exponentially weighted moving average (EWMA ) chart for monitoring the shape parameter of the Weibull distribution. The proposed chart is developed based on the EWMA of the normal random variable, which is transformed from the easy-to-understand chi-squared random variable. In contrast, the existing EWMA charts for monitoring the shape parameter use the sample range or the unbiased estimator of the shape parameter. Unfortunately, the EWMA chart generated from sample ranges is inefficient in detecting changes due to its lack of sufficiency, whereas the one produced using unbiased estimators of the shape parameter has a highly complicated distribution that is difficult to manipulate. Simulation studies are conducted to compare the effectiveness of the proposed EWMA chart and the two existing EWMA charts. Also, a maximum likelihood estimation method is employed to estimate the change point in the process for the proposed EWMA chart once an out-of-control (OC) signal has been triggered. Further, to reduce the time for detecting the OC signal, an EWMA chart with variable sampling intervals (VSIs) for monitoring the shape parameter is developed based on the proposed EWMA chart. This EWMA chart with VSIs is studied, and its performance is evaluated. Finally, an example to demonstrate the applicability and implementation of the proposed charts is provided.     

Parameter estimation for bivariate Weibull distribution using generalized moment method for reliability evaluation

Bivariate Weibull distribution can address the life of a system exhibiting 2‐dimensional characteristics in risk and reliability engineering. The applicability of bivariate Weibull distribution has been hindered by its difficulty with parameter estimation, as the number of parameters in bivariate Weibull distribution is more than those in univariate Weibull distribution. Considering a particular structure of a bivariate Weibull distribution model, this paper proposes a generalized moment method (GMM) for parameter estimation. This GMM method is simple, and it has proved to be efficient. The GMM can guarantee the existence and the uniqueness of the solution. A confidence interval for each estimator is derived from the moments of the bivariate distribution. The paper presents a simulation case and 2 real cases to demonstrate the proposed methods.     

A study of two estimation approaches for parameters of Weibull distribution based on WPP

Least-squares estimation (LSE) based on Weibull probability plot (WPP) is the most basic method for estimating the Weibull parameters. The common procedure of this method is using the least-squares regression of Y on X, i.e. minimizing the sum of squares of the vertical residuals, to fit a straight line to the data points on WPP and then calculate the LS estimators. This method is known to be biased. In the existing literature the least-squares regression of X on Y, i.e. minimizing the sum of squares of the horizontal residuals, has been used by the Weibull researchers. This motivated us to carry out this comparison between the estimators of the two LS regression methods using intensive Monte Carlo simulations. Both complete and censored data are examined. Surprisingly, the result shows that LS Y on X performs better for small, complete samples, while the LS X on Y performs better in other cases in view of bias of the estimators. The two methods are also compared in terms of other model statistics. In general, when the shape parameter is less than one, LS Y on X provides a better model; otherwise, LS X on Y tends to be better.     

Conditional Control Charts for Monitoring the Weibull Shape Parameter under Progressively Type II Censored Data

In this paper, (i) we propose new conditional Shewhart‐type control charts for monitoring the shape parameter of the Weibull distribution under a progressively type II censoring strategy, and (ii) we generalize the control charts proposed by Guo and Wang¹ for the progressively type II censoring case. We provide a comparison between these control charts in terms of the out‐of‐control average run length obtained by simulation for both the known and unknown parameter cases. A real example consisting of data from breaking stress of carbon fibers is also presented for illustration and comparison of the proposed control charts. Copyright © 2014 John Wiley & Sons, Ltd.     

Reliability analysis for electronic devices using beta‐Weibull distribution

Today, in reliability analysis, the most used distribution to describe the behavior of electronic products under voltage profiles is the Weibull distribution. Nevertheless, the Weibull distribution does not provide a good fit to lifetime datasets that exhibit bathtub‐shaped or upside‐down bathtub–shaped (unimodal) failure rates, which are often encountered in the reliability analysis of electronic devices. In this paper, a reliability model based on the beta‐Weibull distribution and the inverse power law is proposed. This new model provides a better approach to model the performance and fit of the lifetimes of electronic devices. To estimate the parameters of the proposed model, a Bayesian analysis is used. A case study based on the lifetime of a surface mounted electrolytic capacitor is presented, the results showed that the estimation of the proposed model differs from the inverse power law–Weibull and that it affects directly the mean time to failure, the failure rate, the behavior, and the performance of the capacitor under analysis.     

A Goodness-of-Fit Test for the Two Parameter vs. Three Parameter Weibull; Confidence Bounds for ThreshoId

An investigation is described herein of modifications of the two-parameter Weibullgoodness-of-fit test of Mann, Scheuer and Fertig (1973). It is assumed that the sole alternative of interest is any three-parameter Weibull distribution. The power of candidate test statistics is investigated, therefore, only under various three-parameter Weibull alternative hypotheses.

It is found that for a fixed selection of gaps (differences of adjacent order statistics) used in the numerator and in the denominator of the approximately F dist ributed test statistic, nothing is gained by weighting the gaps in order to minimize the variances and thus to maximize the numbers of degrees of freedom.

A test statistic which is a modified version of that of Mann, Scheuer and Fertig is shown to have higher power under three-parameter Weibull alternatives, and a simple method for approximating critical values of the test statistic is described. The test statistic is shown to be a monotone function of an unknown threshold (location) parameter for the three-parameter Weibull model. Hence, the methodology described for testing for a zero threshold parameter can be used to obtiain a confidence interval for this parameter. Methods for combining life-test data for application to progressively censored samples are also described.     

Control Charts For Monitoring The Weibull Shape Parameter Based On Type‐II Censored Sample

In this paper, a new statistic is proposed to monitor the Weibull shape parameter when the sample is type II censored. The one‐sided and two‐sided average run length‐unbiased control charts are derived based on the new monitoring statistic. The control limits of the proposed control charts depend on the sample size, the failure number and the false alarm rate. Using Monte Carlo simulation, the performance of the proposed control charts is studied and compared with the range‐based charts proposed by Pascual and Li (2012), which is equivalent to the proposed control charts when r = 2. The simulation results show that the proposed control charts perform better than the ones of Pascual and Li (2012). This paper also evaluates the effects of parameter estimation on the proposed control charts. Finally, an example is used to illustrate the proposed control charts. Copyright © 2012 John Wiley & Sons, Ltd.     

Firework Plots for Evaluating the Impact of Outliers and Influential Observations in Ridge Regression

With many predictors in regression, fitting the full model can induce multicollinearity problems. Thus, ridge regression provides a beneficial means of stabilizing the coefficient estimates in the fitted model. Outliers can distort many measures in data analysis and statistical modeling, while influential points can have disproportionate impact on the estimated values of model parameters. Graphical summaries, called firework plots, are simple tools for evaluating the impact of outliers and influential points in regression. Variations of the plots focus on allowing visualization of the impact on the estimated parameters and variability. This paper describes how three‐dimensional and pairwise firework plots as well as scalable waterfall–firework plots can be used to increase understanding of contributions of individual observations and as a complement to other regression diagnostic techniques in the ridge regression setting. Using these firework plots, we can find outliers and influential points and their impact on model parameters and show how in some applications, the type of analysis used changes the impact of various observations. We illustrate the methods with two examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Solving the Multidisciplinary Robust Parameter Design Problem for Mixed Type Quality Characteristics under Asymmetric Conditions

Quality practitioners often identify robust parameter design (RPD) as one of the most important and effective methods for process and quality improvement. Within this framework, identifying the optimal factor settings that achieve desired process targets with minimum variance is critical and can translate to significant reductions in product waste and processing costs. In solving this problem, most traditional RPD models consider only a single quality characteristic of interest. However, products are often judged by multiple quality characteristics, which often have conflicting objectives. Conventional RPD models that address the multi‐response problem typically only examine like‐type cases, and those that consider mixed types of quality characteristics often overlook any asymmetry that is likely to exist in certain types. In contrast, this article proposes a multidisciplinary RPD methodology that provides an enhanced approach for modeling multiple, mixed type quality characteristics; uses the skew normal distribution to allow for a fuller and more accurate representation of asymmetric system properties and to facilitate simultaneous modeling of both symmetric and asymmetric conditions; and implements a priority‐based optimization scheme that affords engineers' and decision makers' flexibility in establishing and modifying optimization priorities. A numerical example is used to demonstrate the proposed methodology, and the results are compared traditional approaches to illustrate potential improvements. Copyright © 2013 John Wiley & Sons, Ltd.     

More on the mis‐specification of the shape parameter with Weibull‐to‐exponential transformation

When lifetimes follow Weibull distribution with known shape parameter, a simple power transformation could be used to transform the data to the case of exponential distribution, which is much easier to analyze. Usually, the shape parameter cannot be known exactly and it is important to investigate the effect of mis‐specification of this parameter. In a recent article, it was suggested that the Weibull‐to‐exponential transformation approach should not be used as the confidence interval for the scale parameter has very poor statistical property. However, it would be of interest to study the use of Weibull‐to‐exponential transformation when the mean time to failure or reliability is to be estimated, which is a more common question. In this paper, the effect of mis‐specification of Weibull shape parameters on these quantities is investigated. For reliability‐related quantities such as mean time to failure, percentile lifetime and mission reliability, the Weibull‐to‐exponential transformation approach is generally acceptable. For the cases when the data are highly censored or when small tail probability is concerned, further studies are needed, but these are known to be difficult statistical problems for which there are no standard solutions. Copyright © 2000 John Wiley & Sons, Ltd.     

Bias correction for the least squares estimator of Weibull shape parameter with complete and censored data

Estimation of the Weibull shape parameter is important in reliability engineering. However, commonly used methods such as the maximum likelihood estimation (MLE) and the least squares estimation (LSE) are known to be biased. Bias correction methods for MLE have been studied in the literature. This paper investigates the methods for bias correction when model parameters are estimated with LSE based on probability plot. Weibull probability plot is very simple and commonly used by practitioners and hence such a study is useful. The bias of the LS shape parameter estimator for multiple censored data is also examined. It is found that the bias can be modeled as the function of the sample size and the censoring level, and is mainly dependent on the latter. A simple bias function is introduced and bias correcting formulas are proposed for both complete and censored data. Simulation results are also presented. The bias correction methods proposed are very easy to use and they can typically reduce the bias of the LSE of the shape parameter to less than half percent.