Interval Estimation Of Parameter Research Articles

Reliability analysis based on doubly-truncated and interval-censored data

Field data provide important information on product reliability. Interval sampling is widely used for collection of field data, which typically report incident cases during a certain time period. Such sampling scheme induces doubly truncated (DT) data if the exact failure time is known. In many situations, the exact failure date is known only to fall within an interval, leading to doubly truncated and interval censored (DTIC) data. This article considers analysis of DTIC data under parametric failure time models. We consider a conditional likelihood approach and propose interval estimation for parameters and the cumulative distribution functions. Simulation studies show that the proposed method performs well for finite sample size.

Efficient approximation of global population dynamic models through statistical inference using local data

Biological growth curves are pivotal in predicting natural growth across disciplines, typically analyzed using nonlinear least squares or maximum likelihood methods. Bhowmick et al. (2014) introduced the interval-specific rate of parameters (ISRP) for growth equations, improving the estimation of relative growth rate (RGR) and model selection accuracy. Despite its effectiveness, computing these model-specific RGR estimates involves complex calculations and lacks explicit expressions for many nonlinear models. Also, for highly nonlinear models and non-monotonic data where the parameters are non-linearly related, the computation of interval estimates is almost impossible and may suffer from significant approximation errors. So, the need for a more efficient computation method for ISRP remains a significant challenge in growth studies. In this article, we propose a computational approach to obtain interval estimates of parameters based on the maximum likelihood estimation method. The likelihood function is maximized using the data on smaller intervals. Our study underscores the importance of an efficient ISRP computation technique, providing a more stable, unbiased, and normally distributed estimator. The most important advantage is that it can be implemented using existing optimizers in software packages efficiently, therefore, giving more accessibility to the practitioners. Both simulation studies and real data analysis have been carried out to validate the proposed estimation process. Additionally, its applicability to non-monotonic growth profiles and its robustness in handling highly non-linear growth equations highlight its versatility. We also developed a web application GpEM-R which is freely available for researchers and practitioners to analyze growth data.

Accelerated degradation data analysis based on inverse Gaussian process with unit heterogeneity

The unit heterogeneity of products and the nonlinear parameter-stress relationship often exist in practice. Therefore, considering the unit heterogeneity, the nonlinear accelerated model and inverse Gaussian process are developed to depict the accelerated degradation data. On the other hand, this more realistic model leads a challenge to derive the model parameter interval estimation. Thereby, a novel two-step interval estimation method is proposed for the proposed accelerated degradation model. First, generalized confidence intervals of the parameters characterizing random effect are derived from the Cornish–Fisher expansion, and their cumulative distribution functions are obtained. Then, using the generalized pivotal quantity procedure, generalized confidence intervals of the accelerated model parameters are derived. In addition, generalized confidence intervals of predictive reliability indexes are derived to guide the practical application. Finally, simulation studies and two real examples on Spiral springs and integrated circuit devices are presented to demonstrate the implementation of the proposed method.

Combined class of distributions with an exponentiated Weibull family for reliability application

ABSTRACT We develop a novel class of distributions after the exponentiated Weibull family and vtub-shaped failure rate for systems are considered and compounded together. This combined class of distributions can be applied to reliability applications using the data, and we investigate its properties. We study the mathematical properties of combined distributions including order statistics and estimation by maximum likelihood. In addition, we implement an EM algorithm to determine unknown parameters using maximum likelihood estimates, and maximum entropy characterizations are discussed under appropriate constraints. Using maximum likelihood estimation, we obtain point estimates and interval estimation for parameters. We develop a novel approach to measuring the reliability of multi-component systems as well as a single-component system and illustrate the usefulness of the proposed approach. We apply the approach to real data sets and discuss numeric examples to show the statistical properties of the new compounding class of distributions derived in the paper.

Two-stage procedure in a first-order autoregressive process and comparison with a purely sequential procedure

A two-stage procedure in a first-order autoregressive model is considered that investigates the point and the interval estimation of parameters based on the least squares estimator. The two-stage procedure is shown to be as effective as the best fixed-sample-size procedure. In this regard, the significant properties of the procedure, such as asymptotic risk efficiency, asymptotic efficiency, and asymptotic consistency, are established. A Monte Carlo simulation study is conducted to compare the performance of the two-stage procedure and the purely sequential procedure. Finally, real-time series data are considered to illustrate the applicability of the two-stage procedure.

Confidence interval estimation of gamma distribution lifetime data using score and bootstrap methods

Confidence interval estimation of parameters determines the value interval, which is calculated based on statistical measurements and has specific estimates probability that contains the actual parameters. A method is needed to estimate the parameters' confidence interval, and the methods used are the Score method and the Bootstrap method. This study aims to estimate parameters by using the maximum likelihood estimation method and analyze the reliability of the aircraft engine cooling system's lifetime that follows the Gamma Distribution, and estimate the confidence interval of the parameters.

Fuzzy Xbar and S Control Charts Based on Confidence Intervals

There have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variability, which leads to less cost, production time, number of defects, scrap, rework and more customer satisfaction. Quality can be improved by reducing product variability. On the other hand, uncertainty or subjectivity is a part of many engineering and real life problems. However, these problems cannot be solved by traditional methods. This study focuses on constructing Xbar and S control charts in fuzzy environment. The approach is developed by considering the theoretical structure of the Shewhart control charts. The core of the approach depends on the combination of parametric interval estimation and fuzzy statistics. Control limits and samples are presented by fuzzy numbers which ensures to maintain fuzziness in control charts. An important property of the approach is that the fuzzy charts can be reduced to Shewhart control charts. A simulation study was conducted for the performance evaluation of fuzzy Xbar and S control charts. The proposed fuzzy control chart is sensitive to process mean shifts and variance changes, and outperforms the traditional control charts under the changes of variance. In addition, an example from the literature shows that the approach is an effective way of presenting fuzziness in the quality characteristics, which enables the approach to have high applicability to the real life problems.

Estimation Methods for the New Weibull-Pareto Distribution: Simulation and Application

In this paper, we introduce the alternative methods to estimation for the new weibull-pareto distribution parameters. We discussed of point estimation and interval estimation for parameters of the new weibull-pareto distribution. We have also discussed the method of Maximum Likelihood estimation, the method of Least Squares estimation, the method of Weighted Least Squares estimation and the method of Maximum Product Spacing estimation. In addition, we discussed the raw moment of random variable X and the reliability functions (survival and hazard functions). Further, we compared between the results of the methods that have been discussed using Monte Carlo Simulation method and application study.

Inference using MCMC to a new conjugate prior to positive parameters, applied in environmental data

Choosing the prior distribution may have great impact on the result of the posterior distribution and, consequently, point and interval estimation of parameters and the respective predictive density. In situations where the parameters are positive, the gamma distribution is the most common as a conjugate prior to a wide family of parameters. Bourguignon presented the weighted Lindley (WL) distribution as an alternative conjugate prior to parameters conjugated from the gamma family. This work consists in presenting a general way to perform inference to this parameters in a p-parametric vector distribution. The method is illustrated with two cases where both the WL distribution and the gamma distribution are conjugated to these families. Estimation of posterior points is sampled using MCMC techniques. The results of the applications showed advantage on using the WL prior distribution, compared to results with the usual prior gamma distribution.

Bayesian approach for cure models with a change-point based on covariate threshold: application to breast cancer data

ABSTRACT In this study, a Bayesian approach was suggested to estimate a change-point according to a covariate threshold when some patients never experienced the event of interest. Gibbs sampler algorithm with latent binary cure indicators was used to simplify the implementation of Markov chain Monte Carlo method. Then, the accuracy of new model was demonstrated by simulation studies to compute the point and interval estimates of parameters. Finally, an effective threshold was suggested in age at surgery time to experience the metastasis when the model was applied for a data set of breast cancer patients.

Stacking-velocity tomography for tilted orthorhombic media

Tilted orthorhombic (TOR) models are typical for dipping anisotropic layers, such as fractured shales, and can also be due to nonhydrostatic stress fields. Velocity analysis for TOR media, however, is complicated by the large number of independent parameters. Using multicomponent wide-azimuth reflection data, we develop stacking-velocity tomography to estimate the interval parameters of TOR media composed of homogeneous layers separated by plane dipping interfaces. The normal-moveout (NMO) ellipses, zero-offset traveltimes, and reflection time slopes of P-waves and split S-waves ([Formula: see text] and [Formula: see text]) are used to invert for the interval TOR parameters including the orientation of the symmetry planes. We show that the inversion can be facilitated by assuming that the reflector coincides with one of the symmetry planes, which is a common geologic constraint often employed for tilted transversely isotropic media. This constraint makes the inversion for a single TOR layer feasible even when the initial model is purely isotropic. If the dip plane is also aligned with one of the symmetry planes, we show that the inverse problem for [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-waves can be solved analytically. When only [Formula: see text]-wave data are available, parameter estimation requires combining NMO ellipses from a horizontal and dipping interface. Because of the increase in the number of independent measurements for layered TOR media, constraining the reflector orientation is required only for the subsurface layer. However, the inversion results generally deteriorate with depth because of error accumulation. Using tests on synthetic data, we demonstrate that additional information such as knowledge of the vertical velocities (which may be available from check shots or well logs) and the constraint on the reflector orientation can significantly improve the accuracy and stability of interval parameter estimation.

Statistical Analysis of Two-Parameter Generalized BS-Logistic Fatigue Life Distribution

The logarithmic moment estimations of parameters are proposed for two-parameter generalized Birnbaum-Saunders Logistic fatigue life distribution GBS-Logistic(α, β) under the full sample. The precisions of point estimations are investigated and compared with other point estimations by Monte-Carlo simulations. The approximate interval estimations of parameters are given by using Taylor expansion, and the precisions of approximate interval estimations are investigated by Monte-Carlo simulations. Finally, several examples show the feasibility of the methods.

Reliability statistical analysis about products of Birnbaum–Saunders fatigue life distribution for life test and progressive stress accelerated life test

ABSTRACTBirnbaum–Saunders fatigue life distribution is an important failure model in the probability physical methods. It is more suitable for describing the life rules of fatigue failure products than common life distributions such as Weibull distribution and lognormal distribution. Besides, it is mainly applied to analytical research about fatigue failure and degradation failure of electronic product performance. The characteristic properties such as numerical characteristics and image features of density function and failure rate function are studied for generalized BS fatigue life distribution GBS(α, β, m) in this paper. Then the point estimates and approximate interval estimates of parameters are proposed for generalized BS fatigue life distribution GBS(α, β, m), and the precision of estimates are investigated by Monte Carlo simulations. Finally, when the scale parameter satisfies inverse power law model, the failure distribution model is given for the products of two-parameter BS fatigue life distribution BS(α, β) under progressive stress accelerated life test according to the time conversion idea of famous Nelson assumption, and then the points estimates of parameters are given.

위상동기신호를 이용한 한전계통의 저주파진동 검출과 고유치해석

This paper describes the results of a low‐frequency oscillation analysis using data measured in PMU installed in the KEPCO system, and the comparison with eigenvalues computed from the linear model. The dominant oscillation modes are estimated by applying various algorithms. The algorithms are: the extended Prony method; multiple time interval parameter estimation method; subspace system identification method; and spectral analysis. From the measurement data, modes of frequency 0.68[㎐] and 0.92[㎐] were estimated, and modes of frequency 0.63[㎐] and 0.80[㎐] were computed from the eigenvalue calculation. There was a difference between the mode estimated from measurement data and that from the linear model. This is possibly because of an error in the dynamic data of the KEPCO system used in eigenvalue calculation. Because wide area modes exist in the KEPCO system, these modes should be monitored continuously for the reliable operation of the system. In order to prevent total blackouts caused by wide area oscillation, moreover, contingency analysis should be performed in relation to this mode and appropriate measures should be established.

Estimation of Field Reliability Based on Aggregate Lifetime Data

Because of the exponential distribution assumption, many reliability databases recorded data in an aggregate way. Instead of individual failure times, each aggregate data point is a summation of a series of collective failures representing the cumulative operating time of one component position from system commencement to the last component replacement. The data format is different from traditional lifetime data and the statistical inference is challenging. We first model the individual component lifetime by a gamma distribution. Confidence intervals for the gamma shape parameter can be constructed using a scaled χ2 approximation to a modified ratio of the geometric mean to the arithmetic mean, while confidence intervals for the gamma rate and mean parameters, as well as quantiles, are obtained using the generalized pivotal quantity method. We then fit the data using the inverse Gaussian (IG) distribution, a useful lifetime model for failures caused by degradation. Procedures for point estimation and interval estimation of parameters are developed. We also propose an interval estimation method for the quantiles of an IG distribution based on the generalized pivotal quantity method. An illustrative example demonstrates the proposed inference methods. Supplementary materials for this article are available online.

Reliability Parameter Interval Estimation of NC Machine Tools considering Working Conditions

Aiming at the problem that the parameter interval estimation of NC machine tool’s reliability model considering working conditions established by Hongzhou is difficult to implement, given that it has several independent variables, an improved interval estimation method based on Bootstrap is proposed. Firstly, the two-step estimation method was used to calculate the point estimation of NC machine tool’s reliability parameter in test field, based on which B resamplings are generated based on the point estimation. The reliability parameter’s point estimation of the resamplings was obtained by maximum likelihood estimation. Permutation of B point estimations was made in ascending order and the interval estimations were obtained by the α quantile of the permutation. Case study indicated that the location and length of the interval estimation of NC machine tools’ reliability parameter, under different levels of working condition covariates, vary obviously.

Theory of interval traveltime parameter estimation in layered anisotropic media

Moveout approximations for reflection traveltimes are typically based on a truncated Taylor expansion of traveltime squared around the zero offset. The fourth-order Taylor expansion involves normal moveout velocities and quartic coefficients. We have derived general expressions for layer-stripping second- and fourth-order parameters in horizontally layered anisotropic strata and specified them for two important cases: horizontally stacked aligned orthorhombic layers and azimuthally rotated orthorhombic layers. In the first of these cases, the formula involving the out-of-symmetry-plane quartic coefficients has a simple functional form and possesses some similarity to the previously known formulas corresponding to the 2D in-symmetry-plane counterparts in vertically transversely isotropic (VTI) media. The error of approximating effective parameters by using approximate VTI formulas can be significant in comparison with the exact formulas that we have derived. We have proposed a framework for deriving Dix-type inversion formulas for interval parameter estimation from traveltime expansion coefficients in the general case and in the specific case of aligned orthorhombic layers. The averaging formulas for calculation of effective parameters and the layer-stripping formulas for interval parameter estimation are readily applicable to 3D seismic reflection processing in layered anisotropic media.

STATISTICAL ANALYSIS OF SERIES SYSTEM FOR MASKED DATA UNDER SUCCESSIVE CENSORED LIFE TEST

In this paper, considering series system of masked data under simple successive censored and multiple successive censored life test, the likelihood function and maximum likelihood estimate are respectively proposed for series system composed of two units under two kinds of situations. One is the series system composed of two units with constant failure rate, and the other is the series system composed of two units with linear failure rate through the origin. The approximate interval estimates of parameters are given by using the method of likelihood ratio. Besides, the examples show the feasibility of the methods through Monte-Carlo simulations.

New method to incorporate Type B uncertainty into least-squares procedures in radionuclide metrology

We discuss a new method to incorporate Type B uncertainty into least-squares procedures. The new method is based on an extension of the likelihood function from which a conventional least-squares function is derived. The extended likelihood function is the product of the original likelihood function with additional PDFs (Probability Density Functions) that characterize the Type B uncertainties. The PDFs are considered to describe one's incomplete knowledge on correction factors being called nuisance parameters. We use the extended likelihood function to make point and interval estimations of parameters in the basically same way as the least-squares function used in the conventional least-squares method is derived. Since the nuisance parameters are not of interest and should be prevented from appearing in the final result, we eliminate such nuisance parameters by using the profile likelihood. As an example, we present a case study for a linear regression analysis with a common component of Type B uncertainty. In this example we compare the analysis results obtained from using our procedure with those from conventional methods.

Parameter Inference in a Hybrid System With Masked Data

We consider statistical inference for two fundamental hybrid systems based on masked data: series-parallel, and parallel-series. Under constant, and linear hazard functions for component life, we present the maximum likelihood, and interval estimation of parameters of interest. Simulation studies are performed to demonstrate the efficiency of the methods.