Search results

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns.

The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio.

Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution.

In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T² control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T² control chart.

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

Within spatial econometrics a whole family of different spatial specifications has been developed, with associated estimators and tests. This lead to issues of model comparison and model choice, measuring the relative merits of alternative specifications and then using appropriate criteria to choose the “best” model or relative model probabilities. Bayesian theory provides a comprehensive and coherent framework for such model choice, including both nested and non-nested models within the choice set. The paper reviews the potential application of this Bayesian theory to spatial econometric models, examining the conditions and assumptions under which application is possible. Problems of prior distributions are outlined, and Bayes factors and marginal likelihoods are derived for a particular subset of spatial econometric specifications. These are then applied to two well-known spatial data-sets to illustrate the methods. Future possibilities, and comparisons with other approaches to both Bayesian and non-Bayesian model choice are discussed.

Inferences for multicomponent reliability is derived for a family of inverted exponentiated densities having common scale and different shape parameters.

Different estimates for multicomponent reliability is derived from frequentist viewpoint. Two bootstrap confidence intervals of this parametric function are also constructed.

Form a Monte-Carlo simulation study, the authors find that estimates obtained from maximum product spacing and Right-tail Anderson–Darling procedures provide better point and interval estimates of the reliability. Also the maximum likelihood estimate competes good with these estimates.

In literature several distributions are introduced and studied in lifetime analysis. Among others, exponentiated distributions have found wide applications in such studies. In this regard the authors obtain various frequentist estimates for the multicomponent reliability by considering inverted exponentiated distributions.

Airport noise is an undesirable consequence of arriving and departing flights. Much research effort has focused on how such noise affects the prices of houses located nearby and consistently finds that more noise is associated with lower housing prices.¹ On the other hand, few studies have examined the determinants of airport noise.

The purpose of this paper is to theoretically using two new models to analyze the effect of respondents’ uncertainty about their stated willingness to pay (WTP) on welfare estimates in the contingent valuation method (CVM) theoretically using two new models, and empirically to reveal consumers’ WTP to improve drinking water supply safety (WSS) in China.

In this paper, two alternative preference uncertainty treatment approaches are proposed to estimate consumers’ WTP theoretically and they are applied to China’s WSS improvement program from a payment card method, which depends on how consumers’ certainty level about their valuation is. Furthermore, four regression models are presented to investigate the determinants of consumers’ WTP.

Theoretically, the alternative approaches that proposed in this research can remove overestimation bias from traditional CVM method but with lower estimation efficiency. In addition, the empirical results of the uncertainty adjusted models show that the expected WTP to improve drinking WSS is from 0.55 to 0.56 Renminbi yuan/ton, which are lower than the estimates from the conventional standard CVM models. Consumers’ preferences for their concerns about WSS, attitudes toward WSS improvement programs, trusts in implement authorities and their knowledge of WSS have significant effects on the WTP for improving drinking WSS and on respondents’ uncertainty too.

Theoretically to the authors’ knowledge, it is the first attempt to compare alternative approaches to treat respondent uncertainty using numerical certainty scale combined with payment card format valuation questions in CVM. Empirically it is the first study at this large scale that investigates consumers’ WTP for improving drinking WSS incorporating with respondent uncertainty in China. In addition, to assess consumer preferences for improved drinking water safety and the sources of uncertainty, information on consumers’ attitudes toward WSS are considered at the first time.

Due to the fact that the non-standard products, being used by customers, may cause failures in products with sales delays, which naturally affect the warranty policy. Thus, it seems to be necessary to study these two concepts simultaneously. The paper aims to discuss these issues.

In this paper, a model is developed for estimating the expected warranty costs under sales delay conditions when two operator costs (failing but not reported and non-failing but reported) are included.

The proposed model is validated using a numerical example for a two types of intermittent and fatal failures occur under a non-renewing warranty policy.

Sales delay is the time interval between the date of production and the date of sale. Most reported literature on warranty claims data analysis related to sales delay have mainly focussed on estimating the probability distribution of the sales delay.

The data employed to measure income inequality usually come from household surveys, which commonly suffer from atypical observations such as outliers and contamination points. This is of importance since a single atypical observation can make classical inequality indices totally uninformative. To deal with this problem, robust univariate parametric or ad hoc procedures are commonly used; however, neither is fully satisfactory. The purpose of this paper is to propose a methodology to deal with this problem.

The author propose two robust procedures to estimate inequality indices that can use all the information from a data set, and neither of them rely on a parametric distributional assumption. The methodology performs well irrespectively of the size and quality of the data set.

Applying these methods to household data for UK (1979) and Mexico (2006 and 2011), the author find that for UK data the Gini, Coefficient of Variation and Theil Inequality Indices are over estimated by between 0.02 and 0.04, while in the case of Mexico the same indices are over estimated more deeply, between 0.1 and almost 0.4. The relevance of including atypical observations that follow the linear pattern of the data are shown using the data from Mexico (2011).

The methodology has two main limitations: the procedures are not able to identify a bad leverage outlier from a contamination point; and in the case that the data has no atypical observations, the procedures will tag as atypical a very small fraction of observations.

A reduction in the estimate of inequality has important consequences from a policy maker perspective. First, ceteris paribus, the optimal amount of resources destinated to directly address inequality/poverty. Those “extra” resources can be destinated to promote growth. Notice that this is a direct consequence of having a more egalitarian economy than previously thought, this is due to the fact that poor people will actually enjoy a bigger share of any national income increment. This also implies that, in order to reduce poverty, public policies should focus more on economic growth.

To the knowledge, in the inequality literature this is the first methodology that is able to identify outliers and contamination points in more than one direction. That is, not only at the tails of the distribution, but on the whole marginal distribution of income. This is possible via the use of other variables related to income.

The purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.

The proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.

The simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.

The research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.

Due to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.