Updated fiducial distribution of parameters in the associated delta-lognormal population.

In this paper we consider a special kind of semicontinous distribution. We try to concern with the situation where the probability of zero observation is associated with the location and scale parameters in lognormal distribution. We first propose a goodness-of-fit test to ensure that the data can be fit by the associated delta-lognormal distribution. Then we define the updated fiducial distributions of the parameters and establish the results that the confidence interval has asymtotically correct level while the significance level of the hypothesis testing is also asymtotically correct. We propose an exact sampling method to sample from the updated fiducial distribution. It can be seen in our simulation study that the inference on the parameters is largely improved. A real data example is also used to illustrate our method.

NEV supply chain coordination and sustainability considering sales effort and risk aversion under the CVaR criterion.

In a two-echelon new energy vehicle (NEV) supply chain consisting of a risk-neutral manufacturer and a risk-averse retailer, the coordination and sustainability problem is investigated. The risk-averse retailer, who makes sales effort and undertakes the incurred effort cost, decides the order quantity and sales effort level under the Conditional Value-at-Risk (CVaR) criterion. We derive the optimal centralized decisions of a vertically integrated supply chain where the retailer is owned by the manufacturer. Taking such a centralized case as the benchmark, we prove that the subsidy-sharing-based wholesale price (SS-WP) contract fails to coordinate the NEV supply chain under the decentralized case where the retailer makes decisions independently. Then we design a subsidy-sharing-based sales rebate/penalty (SS-SRP) contract and derive the contract parameters to achieve coordination. We evaluate the coordination efficiency of this contract and find that a well-designed SS-SRP contract can promote the NEV sales and lead to a Pareto-improving win-win situation for both the NEV manufacturer and retailer compared to the non-coordination case. A series of numerical experiments are carried out to compare the effects of significant parameters under the SS-WP and SS-SRP contract and provide additional observations and implications, including an indication of the necessary conditions to sustainably maintain the NEV supply chain.

[Study on the application of supervised principal component regression procedure to near-infrared spectroscopy quantitative analysis].

The present paper introduces the principle of a new modeling method, called supervised principal component regression, with which the model of the near-infrared (NIR) spectroscopy quantitative analysis was established. Usually, there are many difficulties such as collinearity when establishing the quantitative analysis model for the high dimension of the spectral data. Using this new method, firstly according to some criterion, the wavelength information is selected in order to reduce the dimension of spectral data. Then the selected lower dimensional spectral data set is used to establish the principal component regression model. Taking sixty-six wheat samples as experiment materials, forty samples were chosen randomly to establish the predicting model, while the remaining twenty-sixth wheat samples were viewed as prediction set. In this example, 4 wavelengths, 4 632, 4 636, 5 994 and 5 997 cm(-1), were selected at first according to the coefficients between the response variable and each spectral data. Then two principal components of the spectral data at those four wavelengths were extracted to establish the principal component regression model. The model was used to the prediction set. The coefficient was 0.991 and the average relative error was 1.5% between the model predication results and Kjeldahl's value for the protein content. It is very important to select the most significant part of wavelength information from a large number of spectral data, not only because this procedure can alleviate the influence of collinearity in modeling, but also because it can be used to guide the design of special NIR analysis instrument for analyzing specific component in some samples.