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This article solves an operational performance measurement problem of a global logistics firm through an internal benchmarking tool. The intended impact is to enable logistics firms to form a deeper understanding of their own internal processes and metrics. The methodology of this in-depth action research involves a sequential approach with a series of interviews, questionnaire-based surveys, operations data collated through observations and process mapping yielding real-world data. A series of statistical tests are conducted to analyse the collated data. Strategic priorities of the firm are integrated with the firm’s operational performance to ascertain the effective performance by considering both the tangible and intangible measures. The outcomes inform both practitioners and academics how the firm could improve its freight forwarding business’s profitability by ensuring that its operations meet the prioritised criteria. The ‘best practice’ derived from internal benchmarking forms an intermediate step towards external benchmarking. The outcomes facilitate investigating the current business strategy, the standard operating procedures and the scope of improving those. 相似文献
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Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty. 相似文献
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For a two-dimensional contingency table of probabilities, the concept of symmetry around the main diagonal is well defined.
Statistical hypothesis test of symmetry versus positive bias have also been explored. For tables of higher (three or more)
dimensions, however, different concepts of symmetry are available. In this study, we consider statistical inference procedures
of symmetry in partial tables versus various biases in three-dimensional tables. We find the maximum likelihood estimates
of the cell probabilities and the asymptotic distribution of the likelihood ratio test statistic in each case. Simulation
studies are used to investigate the sizes and powers of the tests. The methodologies developed are applied on real data sets. 相似文献
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C.G. Bhattacharya 《Statistics》2013,47(4):493-501
The paper reconsider certain estimators proposed by COHENand SACKROWITZ[Ann.Statist.(1974)2,1274-1282,Ann.Statist.4,1294]for the common mean of two normal distributions on the basis of independent samples of equal size from the two populations. It derives the ncecessary and sufficient condition for improvement over the first sample mean, under squared error loss, for any member of a class containing these. It shows that the estimator proposded by them for simultaneous improvement over botyh sample means has the desired property if and only if the common size of the samples is at least nine. The requirement is milder than that for any other estimator at the present state of knolwledge and may be constrasted with their result which implies the desired property of the estimator only if the common size of the samples is at least fifteen. Upper bounds for variances if the estimators derived by them are also improved 相似文献
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Sourabh Bhattacharya Ashis SenGupta 《Journal of statistical planning and inference》2009,139(12):4179-4192
Very often, the likelihoods for circular data sets are of quite complicated forms, and the functional forms of the normalising constants, which depend upon the unknown parameters, are unknown. This latter problem generally precludes rigorous, exact inference (both classical and Bayesian) for circular data.Noting the paucity of literature on Bayesian circular data analysis, and also because realistic data analysis is naturally permitted by the Bayesian paradigm, we address the above problem taking a Bayesian perspective. In particular, we propose a methodology that combines importance sampling and Markov chain Monte Carlo (MCMC) in a very effective manner to sample from the posterior distribution of the parameters, given the circular data. With simulation study and real data analysis, we demonstrate the considerable reliability and flexibility of our proposed methodology in analysing circular data. 相似文献
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The Bayes estimators of the Gini index, the mean income and the proportion of the population living below a prescribed income
level are obtained in this paper on the basis of censored income data from a pareto income distribution. The said estimators
are obtained under the assumptions of a two-parameter exponential prior distribution and the usual squared error loss function.
This work is also extended to the case when the income data are grouped and the exact incomes for the individuals in the population
are not available. The method for the assessment of the hyperparameters is also outlined. Finally, the results are generalized
for the doubly truncated gamma prior distribution.
Now deceased. 相似文献
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In this paper, we revisit the problem of combining estimates of location considered by Cohen (1976). Our results unify and strengthen the results of Cohen (1976), Bhattacharya (1981) and Akai (1982). 相似文献
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Bayesian palaeoclimate reconstruction 总被引:1,自引:0,他引:1
J. Haslett M. Whiley S. Bhattacharya M. Salter-Townshend Simon P. Wilson J. R. M. Allen B. Huntley F. J. G. Mitchell 《Journal of the Royal Statistical Society. Series A, (Statistics in Society)》2006,169(3):395-438
Summary. We consider the problem of reconstructing prehistoric climates by using fossil data that have been extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise: the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by explicitly modelling uncertainty and reconstructing entire climate histories. The statistical issues that are raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include a spatial process model for compositional data, local modelling of lattice data, the use, as a prior, of a random walk with long-tailed increments, a two-stage implementation of the Markov chain Monte Carlo approach and a fast approximate procedure for cross-validation in inverse problems. We present some details, contrasting its reconstructions with those which have been generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome. 相似文献