A Practical Solution to the Small Sample Size Bias and Uncertainty Problems of Model Selection Criteria in Two-Input Process Multiple Response Surface Methodology Datasets

Multiple response surface methodology (MRSM) most often involves the analysis of small sample size datasets which have associated inherent statistical modeling problems. Firstly, classical model selection criteria in use are very inefficient with small sample size datasets. Secondly, classical model selection criteria have an acknowledged selection uncertainty problem. Finally, there is a credibility problem associated with modeling small sample sizes of the order of most MRSM datasets. This work focuses on determination of a solution to these identified problems. The small sample model selection uncertainty problem is analysed using sixteen model selection criteria and a typical two-input MRSM dataset. Selection of candidate models, for the responses in consideration, is done based on response surface conformity to expectation to deliberately avoid selection of models using the problematic classical model selection criteria. A set of permutations of combinations of response models with conforming response surfaces is determined. Each combination is optimised and results are obtained using overlaying of data matrices. The permutation of results is then averaged to obtain credible results. Thus, a transparent multiple model approach is used to obtain the solution which gives some credibility to the small sample size results of the typical MRSM dataset. The conclusion is that, for a two-input process MRSM problem, conformity of response surfaces can be effectively used to select candidate models and thus the use of the problematic model selection criteria is avoidable.

An optimized industry processing technology of peanut tofu and the novel prediction model for suitable peanut varieties

Peanut protein is easily digested and absorbed by the human body,and peanut tofu does not contain flatulence factors and beany flour.However,at present,there is no industrial preparation process of peanut tofu,whereas the quality of tofu prepared by different peanut varieties is quite different.This study established an industrial feasible production process of peanut tofu and optimized the key process that regulates its quality.Compared with the existing method,the production time is reduced by 53.80%,therefore the daily production output is increased by 183.33%.The chemical properties of 26 peanut varieties and the quality characteristics of tofu prepared from these 26 varieties were determined.The peanut varieties were classified based on the quality characteristics of tofu using the hierarchical cluster analysis(HCA)method,out of which 7 varieties were screened out which were suitable for preparing peanut tofu.An evaluation standard was founded based on peanut tofu qualities.Six chemical trait indexes were correlated with peanut tofu qualities(P<0.05).A logistic regressive model was developed to predict suitable peanut varieties and this prediction model was verified.This study may help broaden the peanut protein utilization,and provide guidance for breeding experts to select certain varieties for product specific cultivation of peanut.

A general evaluation system for optimal selection performance of radar clutter model

The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathematical simplification or empirical data fitting.However,the lack of standard model labels is a challenge in the optimal selection process.To solve this problem,a general three-level evaluation system for the model selection performance is proposed,including model selection accuracy index based on simulation data,fit goodness indexs based on the optimally selected model,and evaluation index based on the supporting performance to its third-party.The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways,and can be popularized and applied to the evaluation of other similar characterization model selection.

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.

Reliability Evaluation Optimal Selection Model of Component-Based System

If the components in a component-based software system come from different sources, the characteristics of the components may be different. Therefore, evaluating the reliability of a component-based system with a fixed model for all components will not be reasonable. To solve this problem, this paper combines a single reliability growth model with an architecture-based reliability model, and proposes an optimal selecting approach. First, the most appropriate model of each component is selected according to the historical reliability data of the component, so that the evaluation deviation is the smallest. Then, system reliability is evaluated according to both the relationships among components and the using frequency of each component. As the approach takes into account the historical data and the using frequency of each component, the evaluation and prediction results are more accurate than those of using a single model.

EXPERIMENTAL STUDIES OF MEDICAL EQUIPMENT SELECTION BY USING THE MULTI-STAGE WEIGHTING EVALUATION METHOD

As more and more sophiscated medical equipment is being purchased byhospitals, selection is becoming an increasinly complex and important process. But it'sdifficult to compare different products analytically. Therefore personal preferences andbiases will probably be ntroduced. This paper describes a scientific model selectionmethod to quantify a product's value to make comparison easy.

Evaluation of numerical earthquake forecasting models

Evaluation of numerical earthquake forecasting models needs to consider two issues of equal importance:the application scenario of the simulation,and the complexity of the model.Criterion of the evaluation-based model selection faces some interesting problems in need of discussion.

Vendor Evaluation and Selection Using Fuzzy AHP

Vendor evaluation and selection is one of the most important issues in manufacturing management. It is a multi-criteria decision problem including various factors. Traditional methods have many shortcomings. Fuzzy AHP model is established to solve this problem. A general evaluation method is provided and steps are presented. As a case study,the model is implemented in an electro-mechanical product company. And a detailed example is given to illustrate the effectiveness of this approach.

Limiting stand density and basal area projection models for even-aged Tecomella undulata plantations in a hot arid region of India

This paper presents equations for estimating limiting stand density for Z undulata plantations grown in hot desert areas of Raj asthan State in India. Five different stand level basal area projection models, belonging to the path invariant algebraic difference form of a non-linear growth function, were also tested and compared. These models can be used to predict future basal area as a function of stand variables like dominant height and stem number per hectare and are necessary for reviewing different silvicultural treatment options. Data from 22 sample plots were used for modelling. An all possible growth intervals data structure was used. Both, qualitative and quantitative criteria were used to compare alternative models. The Akaike＇s information criteria differ- ence statistic was used to analyze the predictive ability of the models. Results show that the model proposed by Hui and Gadow performed best and hence this model is recommended for use in predicting basal area development in 12 undulata plantations in the study area. The data used were not from thinned stands, and hence the models may be less accurate when used for predictions when natural mortality is very significant.

Selection of Wind Turbine Systems for the Sultanate of Oman

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a solution.In addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind turbines.Furthermore,the use of small-scale energy from wind devices has been on the rise in recent years.This upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from wind.To calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal solution.As a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of Oman.This research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of Oman.Further,six distinct miniature wind turbines were investigated for four different locations.The outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small businesses.The government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

Methodology for Selecting the Best Model for Forecasts： The Case Study of Forest Bioenergy

The aim of this paper is the analysis of methodology for selecting the best model for forecasting of fuelwood demand in Greece for the years 2020, 2025 and 2030 with a final objective the decision-making in the sector of forest bioenergy. A complete time series of historical data exists that concerns： （a） the consumption of fuelwood and （b） the six most important from the independent variables that could influence the consumption of fuelwood, whose data cover the time period 1989-2010. The evaluation and choice of the best model was realized with the help of the following six statistical criteria： （a） the size of standard error of theoretical values of dependant variable, S. E.; （b） the value of adjusted R square （R2）; （c） the non-existence of autocorrelation among the residuals （ei） through the criterion Durbin-Watson; （d） the statistical significance of models coefficients through t criterion; （e） the statistical significance of models through F criterion and （f） the non-existence of multicolinearity through the values of Variance Inflation Factor.

A MCDM-based model for vendor selection:a case study in the particleboard industry

We investigated procurement of raw materials for particleboard to minimize costs and develop an efficient optimization model for product mix. In a multiple-vendor market, vendors must be evaluated based on specified criteria. Assuming sourcing from the highest-scoring vendors, annual purchase quantities are then planned. To meet procure- ment needs, we first propose a model to describe the problem. Then, an appropriate multi-criteria decision making （MCDM） technique is se- lected to solve it. We ran the model using commercial software such as LINGO~ and then compared the model results to a real case involving one of the largest particleboard manufacturers in the region. The model run based real data yielded a procurement program that is more efficient and lower in cost than the program currently in use. Use of this procurement modelling approach would yield considerable financial returns.

Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection

The paper describes the necessity of application of intelligent technologies to support decisions of more objective problems in human resource management. In this paper, we describe the methodology for personnel selection problem for the vacancy with regard to the importance and nonequivalence of numerous indicators characterizing the alternatives. The specific features of the selection problem are highlighted, immersing the problem into a fuzzy environment. A fuzzy multicriterial model of the personnel selection problem is proposed. A technique of order preference by similarity to ideal solition (TOPSIS), was applied for evaluation and regulation of alternatives. This technique is based on criteria of qualitative character, which are hierarchically structured by multiple experts to intellectually support decisions made in personnel selection problem. Using TOPSIS method and generated criteria system an experiment was conducted for evaluation of the candidates during solution of hiring problems. The obtained and reviewed results were compared with results obtained using in reality.

An investigation of several typical model selection criteria for detecting the number of signals

Based on the problem of detecting the number of signals,this paper provides a systematic empirical investigation on model selection performances of several classical criteria and recently developed methods(including Akaike’s information criterion(AIC),Schwarz’s Bayesian information criterion,Bozdogan’s consistent AIC,Hannan-Quinn information criterion,Minka’s(MK)principal component analysis(PCA)criterion,Kritchman&Nadler’s hypothesis tests(KN),Perry&Wolfe’s minimax rank estimation thresholding algorithm(MM),and Bayesian Ying-Yang(BYY)harmony learning),by varying signal-to-noise ratio(SNR)and training sample size N.A family of model selection indifference curves is defined by the contour lines of model selection accuracies,such that we can examine the joint effect of N and SNR rather than merely the effect of either of SNR and N with the other fixed as usually done in the literature.The indifference curves visually reveal that all methods demonstrate relative advantages obviously within a region of moderate N and SNR.Moreover,the importance of studying this region is also confirmed by an alternative reference criterion by maximizing the testing likelihood.It has been shown via extensive simulations that AIC and BYY harmony learning,as well as MK,KN,and MM,are relatively more robust than the others against decreasing N and SNR,and BYY is superior for a small sample size.

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

Bi-Criteria Optimization Technique in Stochastic System Maintenance Allocation Problem

In this paper, the problem of optimum allocation of repairable and replaceable components in a system is formulated as a Bi-objective stochastic non linear programming problem. The system maintenance time and cost are random variable and has gamma and normal distribution respectively. A Bi-criteria optimization technique, weighted Tchebycheff is used to obtain the optimum allocation for a system. A numerical example is also presented to illustrate the computational details.

Application of Bayesian Econometrics in Policy Evaluation

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bayesian inference, prior distributions, and posterior distributions. Through systematic analysis, the study constructs a theoretical framework for applying Bayesian methods in policy evaluation. The research finds that Bayesian methods have multiple theoretical advantages in policy evaluation: Based on parameter uncertainty theory, Bayesian methods can better handle uncertainty in model parameters and provide more comprehensive estimates of policy effects;from the perspective of model selection theory, Bayesian model averaging can reduce model selection bias and enhance the robustness of evaluation results;according to causal inference theory, Bayesian causal inference methods provide new approaches for evaluating policy causal effects. The study also points out the complexities of applying Bayesian methods in policy evaluation, such as the selection of prior information and computational complexity. To address these complexities, the study proposes hybrid methods combining frequentist approaches and suggestions for developing computationally efficient algorithms. The research also discusses theoretical comparisons between Bayesian methods and other policy evaluation techniques, providing directions for future research.

基于多准则决策方法的避难场所选址模型研究

针对避难场所规划选址中的现实问题与需求,从方法易操作和可实施角度出发,构建了面向城市规划业务流程与工作实践的防灾避难场所选址模型。该模型基于多准则决策方法(MCDM),以避难场所选址的安全性和均好性为主要目标。模型综合考虑了人口密度、建筑质量、交通系统和避难场所设施配置四项选址影响因素,并采用AHP-熵权法确定影响要素权重。以江阴市为实证案例,套用模型经过两轮评估及编码者测试,最终明确了备选避难场所资源的优先建设排序,验证了模型的可行性与实用性。研究提出的影响要素和量化指标易于取得,打分方法便于操作,适用于城市规划阶段的避难场所规划选址与建设时序决策。

机床优化选择的多级模糊综合评判模型及其算法

在生产中,机床设备的选择涉及到多个指标和多种方案的多目标决策,是一个繁琐复杂的问题。以如何能够从众多的机床设备中选用一台适合生产所用机床为研究对象,从机床的加工性能、经济性能、环境属性和人机属性四个方面进行了分析,建立了多级模糊综合评判模型,经过层次分析法确定了各因素的权重,从而计算得出了最优的机床设备选用方案。最后通过案例证明了该算法的正确性和实用性。

基于DEA-EWM-TOPSIS算法的瓦楞纸箱配纸及楞型选择

对瓦楞纸箱的面纸、芯纸、里纸和楞型进行组合评价,提供科学的纸箱选配依据,解决不合理选配纸箱问题。本课题提出了一个多准则决策模型,采用交叉效率DEA-EWM-TOPSIS组合模型,对瓦楞纸箱进行配纸及楞型方案选择,并开发了相应的纸箱选择软件,帮助决策者选出性能和成本达到最优平衡的纸箱。以内装物质量为2 kg、综合尺寸为460 mm的0201型快递纸箱为例,依据瓦楞纸箱的评价指标数据,通过模型计算进行排序,选出最佳的纸箱方案。本课题将多变量因素耦合后,再进行整体优化,为瓦楞纸箱的选择决策提供了一种有效、便捷的方法。