Akaike Information Criterion Criterion Research Articles

Modelling Mixed Traffic Time Headway Distribution Induced by Bus Rapid Transit (BRT) in Cape Town, South Africa

Background: The safety and capacity of roadways are immensely influenced by time headways, a major traffic flow characteristic. Time headways are frequently utilized in various aspects of traffic and transportation engineering studies, including capacity analyses, studies of safety, modelling of lane-changing and car-following behaviour, and level of service assessment. Time headway, measured in seconds, refers to the distance between two subsequent passing cars moving over a single spot on the road. This paper reports the statistical modelling of the time headway distribution of a mixed traffic flow scenario caused by BRT via statistical headway distribution models. The paper posits that the introduction of BRT dedicated lane and its adopted design configuration induces anomalous traffic flows on its adjoining lanes with attendant time headway differentials, irrespective of the type of adopted design configuration for the corridor. The objectives of this study were to fit the BRT-induced time headway distribution to probability distribution models and determine the most appropriate distribution model that fits the data derived using the goodness of fit tests. Consequently, this contribution, therefore, fills the gap in research with respect to determining the precise model that fits the time headway distribution of mixed traffic flow scenarios induced by BRT. Methods: The time headway distribution data collected at four designated road segments on a multilane provincial route R27 caused by BRT dedicated lane in Cape Town, South Africa, were fitted to five probability distribution models viz, Lognormal, Inverse Gaussian, Log-logistic, Generalized Extreme Value (GEV), and Burr using MATLAB software. The fitted headway data were with respect to the traffic flows on the adjoining lanes to BRT or traffic flow ‘without BRT’. Precisely, the empirical data were collected over a three-month period using an Automatic Traffic Counter (ATC). Using ModelRisk software, the goodness of fit of the probability models was assessed by the Akaike Information Criterion (AIC), the Schwartz or Bayesian Information Criterion (SIC or BIC), the Hannan Quinn Information Criterion (HQIC), and the loglikelihood (LLH) model performance criteria respectively. The distribution model with the lowest AIC criterion and largest loglikelihood (LLH) values describe the model that best fits the headway data across the four sites. Results: Results showed that the models fitted the headway data well at each site by visual inspection of the attendant Probability Density Function (PDF) and the Probability plots (P-P). However, the Burr distribution provided the overall best fit based on the AIC and LLH values at 95% confidence and 0.05 significance levels across the four sites. At sites 02, 03, and 04, it ranked first with the lowest AIC values of 4025.99, 2595.56, 3815.36, and corresponding largest LLH values of -2008.98, -1293.76, and -1903.66, respectively, while the lognormal distribution performed best at site 01, with AIC value of 4445.14 and LLH value of -2220.57, closely followed by the Burr distribution with AIC and LLH values of 4453.05 and -2222.51, respectively. The P-values, which ranged between 0.65 and 0.80, showed the likelihood of the occurrence of the data sets under the null hypothesis. Hence the null hypothesis was accepted. Conclusion: The study concluded that the introduction of BRT dedicated lanes affects the adjoining lanes’ mixed traffic time headways’ distribution, and the headways are continuously distributed, hence fitting continuous probability distributions.

Modeling of the land surface temperature as a function of the soil-adjusted vegetation index

Land surface temperature is a physical-environmental variable that is the target of studies in climatology and heat island phenomena resulting from the urbanization model of the 21st century. It is known that each object has a different thermal capacity, which results in higher or lower temperatures. The modeling of temperature as a function of objects on the land surface can allow understanding between these variables. It can corroborate temperature forecasts with the alteration of objects in an area. The objects on the Earth's surface can be computed with geoprocessing techniques that aim to detail Land Use and Occupation. This paper evaluates the Linear, Exponential, and Sinusoidal models to determine which of these models is more expressive for the study of the land surface temperature as a function of land surface objects. For this purpose, images from the Landsat 8 satellite were used to calculate the Earth's surface temperature and determine the scene's objects. To determine the scene's objects, the Soil Adjusted Vegetation Index (SAVI) was used, which is one of the techniques for obtaining Land Use and Occupation. For model analysis, Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sum of Square of Residuals (SSR). According to the AIC, BIC, and SSR criteria, the sinusoidal model presented better performance when compared to the other models. However, there are large variations in SSR between classes, especially for the pasture class, which makes the models not highly explanatory.

New penalty in information criteria for the ARCH sequence with structural changes

For change point models and autoregressive conditional heteroscedasticity (ARCH) models, which have long been important especially in econometrics, we develop information criteria that work well even when considering a combination of these models. Since the change point model does not satisfy the conventional statistical asymptotics, a formal Akaike information criterion (AIC) with twice the number of parameters as the penalty term would clearly result in overfitting. Therefore, we derive an AIC‐type information criterion from its original definition using asymptotics peculiar to the change point model. Specifically, we suppose time series data treated in econometrics and derive Takeuchi information criterion (TIC) as the main information criterion allowing for model misspecification. It is confirmed that the penalty for the change point parameter is almost three times larger than the penalty for the regular parameter. We also derive the AIC in this setting from the TIC by removing the consideration of the model misspecification. In numerical experiments, the derived TIC and AIC are compared with the formal AIC and Bayesian information criterion (BIC). It is shown that the derived information criteria clearly outperform the others in light of the original purpose of AIC, which is to give an estimate close to the true structure. We also ensure that the TIC seems to be superior to the AIC in the presence of model misspecification.

Is the Area Under Curve Appropriate for Evaluating the Fit of Psychometric Models?

In the literature of modern psychometric modeling, mostly related to item response theory (IRT), the fit of model is evaluated through known indices, such as χ2, M2, and root mean square error of approximation (RMSEA) for absolute assessments as well as Akaike information criterion (AIC), consistent AIC (CAIC), and Bayesian information criterion (BIC) for relative comparisons. Recent developments show a merging trend of psychometric and machine learnings, yet there remains a gap in the model fit evaluation, specifically the use of the area under curve (AUC). This study focuses on the behaviors of AUC in fitting IRT models. Rounds of simulations were conducted to investigate AUC's appropriateness (e.g., power and Type I error rate) under various conditions. The results show that AUC possessed certain advantages under certain conditions such as high-dimensional structure with two-parameter logistic (2PL) and some three-parameter logistic (3PL) models, while disadvantages were also obvious when the true model is unidimensional. It cautions researchers about the dangers of using AUC solely in evaluating psychometric models.

Noise Source Identification Method for a Carpet Tufting Machine Based on CEEMDAN-AIC

In recent years, the noise reduction research of the carpet tufting machine has been developing slowly. The research gaps of the existing work mainly focus on the noise source identification for the carpet tufting machine. MEEMD (EEMD) has been proposed to apply to source recognition on textile machinery. Due to the uniqueness of the MEEMD/EEMD, it is difficult to set suitable white noise control parameters. MEEMD (EEMD) has only been tested via simulation; however, it has not been mathematically proven or evaluated. This leads to inevitable flaws in the research conclusions, and even some conclusions are wrong. The contribution of this paper is twofold. First, in order to recognize the noise source of a carpet tufting machine, a method based on complete ensemble empirical mode decomposition (CEEMDAN) and Akaike information criterion (AIC) is proposed. The CEEMDAN-AIC method is applied to measure the noise signal of a carpet tufting machine and analyzed every single effective component selected. Noise source identification is realized by combining the vibration signal characteristics of the main parts of the carpet tufting machine. CEEMDAN is used to decompose the measured noise signal of the carpet tufting machine into a finite number of intrinsic mode functions (IMFs). Then, singular value decomposition (SVD) is performed on the covariance matrix of the IMF matrix to obtain the eigenvalue. Next, the number of effective IMFs is estimated based on the AIC criterion, and the effective IMFs are selected by combining the energy characteristic index and the Pearson correlation coefficient method. Furthermore, reconstruction and comparison of the decomposed signals of MEEMD and CEEMDAN proved that CEEMDAN is effective and accurate in source recognition. The results show that the noise signal of the carpet tufting machine is a mixture of multiple noise source signals. The main noise sources of the carpet tufting machine include shock caused by the impact of the tufted needle and looped hook and vibration of the hook-driven shaft and pressure plate. It provides theoretical support for the noise reduction of the carpet tufting machine.

Monte Carlo methods for estimating Mallows's Cp and AIC criteria for PLSR models. Illustration on agronomic spectroscopic NIR data

AbstractMallows's Cp and Akaike information criterion (AIC) are common criteria for selecting the dimensionality of regression models, as an alternative to cross‐validation (CV) and nonparametric bootstrap. A key parameter in the calculation of Cp and AIC is the effective number of degrees of freedom of the model, or model complexity (d). Parameter d is generally easy to calculate for linear smoothers, that is, models for which the prediction of the training response y is given by = S y where S is a projector matrix that does not involve y. Nevertheless, d is more difficult to estimate for nonlinear smoothers, such as partial least squares regression (PLSR). In this article, we present two algorithms for estimating d for PLSR based on Monte Carlo simulation methods (parametric bootstrap and perturbation analysis) and with the particular case of high dimensional data. We compare these Monte Carlo methods to three other algorithms already published. We used the d estimates to compute Cp and AIC and select PLSR model dimensionalities that we then compare to CV. Two real and heterogeneous agronomic near infrared (NIR) datasets were considered as examples.

Predicting the Trace Element Levels in Caspian Kutum (Rutilus kutum) from South of the Caspian Sea Based on Locality, Season and Fish Tissue.

Elements are the shared result of the erosion of rocks in the catchment area and human activities. Nutritional habits, ecological needs, heavy metal concentrations in water and sediment, duration of fishing in the aquatic environment, fishing season, and physicochemical properties of water (salinity, pH, hardness, and temperature) are among the effective factors in the accumulation of heavy metals in various fish organs. In this study, 150 specimens of Rutilus kutum were collected from the southern shores of the Caspian Sea including Astara, Anzali, and Kiashahr in Guilan Province, Farahabad in Mazandaran Province, and Bandar Torkaman in Golestan Province from December 2018 through October 2019. It is possible to predict the metal concentrations using the variables such as fish tissue, sampling region, and season. Akaike information criterion (AIC) was used to select the best regression model. We used fish muscle tissue and Anzali sampling site which were considered reference variables in the regression model. For some elements, a better model is obtained by considering all variables (AIC criterion is its lowest value). The best model obtained for Cu, Mn, and Si was only with region (as a variable). The best model obtained for Sn and Sr only concerns the region and tissue variables. The best model obtained for Sb only related to tissue variable. Using these models, environmental monitoring becomes easier and cheaper. We suggest further studies to be carried out in the shortest possible time along with the least laboratory cost.

USE OF AIC AND BIC IN DESORPTION ISOTHERMS OF TAMARIND SEEDS (Tamarindus indica L.)

ABSTRACT The knowledge of hygroscopicity is essential for the storage of tamarind seeds, but there is a limitation of a judicious statistical parameter to define the best mathematical model to adjust the isotherms of plant products. Therefore, this study aimed to determine the desorption isotherms of tamarind seeds and test the Akaike information criterion (AIC) and Schwarz Bayesian information criterion (BIC) for choosing the best mathematical model. Seeds with an initial moisture content of 21.00 ± 0.10% dry basis (db) were dried at 45 °C until they reached moisture contents of 17.27 ± 0.10, 15.04 ± 0.16, 14.14 ± 0.06, 12.41 ± 0.17, and 10.52 ± 0.12% db. The water activity of the product was determined by the static-indirect method at temperatures of 10, 20, 30, and 40 °C. Mathematical models frequently used to predict the isotherms of plant products were adjusted to the experimental data. The Cavalcanti Mata model is recommended to estimate the desorption isotherms of tamarind seeds because it presents better adjustments. The AIC and BIC criteria contribute to the choice of the model to predict the desorption isotherms of tamarind seeds.

Noise Source Identification Method for a Warp Machine Based on MEEMD_AIC

In order to recognise the noise source of a warp knitting machine, a method based on Modified Ensemble Empirical Mode Decomposition (MEEMD) and Akaike Information Criterion (AIC) is proposed. The MEEMD_AIC method is applied to measure the noise signal of a warp knitting machine and analyse every single effective component selected. Noise source identification is realised by combining the vibration signal characteristics of the main parts of the warp knitting machine. Firstly, MEEMD is used to decompose the measured noise signal of the warp knitting machine into a finite number of intrinsic mode function (IMF) components. Then, singular value decomposition (SVD) is performed on the covariance matrix of the component matrix to get the eigen value of the matrix. Next, the number of effective components is estimated based on the AIC criterion, and the effective components are selected by combining the energy characteristic index and the Pearson correlation coefficient method. The results show that the noise signal of the warp knitting machine is a mixture of multiple noise source signals. The main noise sources of the warp knitting machine, including the vibration of the pulling roller, the main shaft of the loop forming mechanism and the push rod of the guide bar traverse the mechanism, provide theoretical support for recognition of the active noise reduction of the warp knitting machine using the MEEMD_AIC method.

Neural network structure identification in inflation forecasting

AbstractNeural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non‐normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand‐pull and cost‐push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017. Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in‐the‐sample and out‐of‐sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the ‘best’ NN because the optimal NN in‐the‐sample, based on MSE and/or AIC criteria, often has high out‐of‐sample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN. Moreover, JNN has a better forecasting performance than FNN.

基于数据包络分析方法的城市水资源利用效率研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于数据包络分析方法的城市水资源利用效率研究 DOI: 10.5846/stxb201903020385 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院前沿项目（QYZDY-SSW-DQC004）；北京市科技计划项目（Z171100004417025）；广东省省级科技计划项目（2016B02024007） Efficiency assessment of water resource utilization cities based on data envelopment analysis Author: Affiliation: Fund Project: Frontier Project of Chinese Academy of Sciences (QYZDY-SSW-DQC004); Beijing Science and Technology Plan Project (Z171100004417025); Guangdong Provincial Science and Technology Plan Project (2016B02024007) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了对我国城市水资源利用效率问题进行分析和评价，本文基于数据包络分析方法，从农业、工业、生活、社会等几个方面共选取了5个输入指标及6个输出指标，利用AIC信息准则（Akaike information criterion）进行了变量选择，构建了较为科学合理的用水效率评价指标体系.在此基础上，采用香农熵指数提升了传统CCR（由Charnes A，Cooper W W，Rhodes E提出）模型的识别能力，选取我国31个省会城市为研究对象，给出了省会城市水资源效率的完整排名。结果表明：①绝大部分城市综合效率得分（CES，Comprehensive efficiency score）普遍不高，投入产出比仍有较大的进步空间；②拉萨、北京、天津、银川、海口、上海等城市CES得分相对较高，这说明一个城市水资源利用效率的高低与经济发展水平可能没有必然的联系，其他城市应结合自身情况向CES得分靠前的城市进行学习；③重庆、南宁、南昌、长沙等水资源较丰富的城市CES得分反而较低，表明这些城市可能存在大量水资源被浪费，应建立起节水机制，同时优化产业结构。 Abstract:Improving efficiency of water resources utilization is the key to alleviate the contradiction between water supply and demand while achieving sustainable economic and social development. In order to analyze and evaluate the water consumption efficiency of the provincial capital cities in China, this paper selected five input indicators and six output indicators from agriculture, industry, life and society based on data envelopment analysis method. Then we used Akaike Information Criterion (AIC) for variable selection and constructed a scientific and rational water-use efficiency indicator system. On this basis, the Shannon Entropy Index is used to enhance the recognition ability of the traditional CCR model, and the complete ranking of water resources efficiency in the provincial capitals is given. The results show that (1) the comprehensive efficiency scores (CES) of most cities are not high. Their input-output ratio still has a lot of room for improvement. (2) The scores of CES in Lasa, Beijing, Tianjin, Yinchuan, Haikou, Shanghai etc. are relatively high, which indicate that there may be no necessary connection between the level of water use efficiency of a city and the level of economic development. At the same time, other cities should combine their own conditions to learn from cities with higher scores in CES. (3) Cities such as Chongqing, Nanning, Nanchang, and Changsha are rich in water resources, but the CES is not ideal. This shows that a large amount of water resources may be wasted and these cities should optimize their industrial structures while establishing a water-saving mechanism. In addition, due to the lack of variable selection system in most literatures and the inadequacy of traditional models in the ability of efficiency identification, this paper innovatively combined the AIC variable selection method, data envelopment analysis method, and Shannon Entropy Index method. The combination of the three methods can achieve the advantages and disadvantages of complementarity, not only fully consider a large number of input and output variables, make the analysis closer to reality, the variable indicator system obtained by the AIC criterion also perfectly conforms to the meaning of actual water resource utilization efficiency. 参考文献 相似文献 引证文献

Surgical Outcomes in Acute Mesenteric Ischemia: Has Anything Changed Over the Years?

Despite increases in knowledge and advances in the management of acute mesenteric ischemia syndrome (AMI), there have been no significant improvements in mortality in recent years. The objective of this study was to assess the changes in clinical characteristics and surgical outcomes in patients who underwent AMI over time. A total of 323 consecutive patients who underwent acute mesenteric ischemia at our institution between 1990 and 2015 were examined. The occurrence of significant changes over this 25-year period in demographic data, comorbidity, clinical characteristics, laboratory results, operative findings, etiology of the AMI, and operative mortality were evaluated. The evolution mortality rates for the studied period were analyzed using the additive logistic regression, and the significant effect was determined using the Akaike Information Criterion (AIC). A significant increasing linear trend was observed in recent years in Charlson score values (p = 0.008), antiplatelet drug intake (p < 0.001), use of CT scan (p < 0.001), arterial thrombosis (p < 0.001), and intestinal resection (p = 0.047), while a decreasing linear trend was observed in digoxin intake (p < 0.001), angiography use (p = 0.004), and embolia (p < 0.001). The rest of the parameters did not present changes over time. Regarding the evolution of the adjusted surgical mortality, a significant decrease according the AIC criterion was observed. In recent years, the characteristics of patients with AMI requiring surgery have changed. Changes in operative mortality have also been detected, showing a tendency toward a progressive and significant decrease.

Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"

The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain model of appropriate order for analyzing wet and dry days is determined. This is done using the Akaike Information Criterion (AIC) by checking the minimum of AIC estimate. Markov chain of order one is found to be superior to the majority of the stations in comparison to the other order Markov chains. More precisely, first order Markov chain model is an adequate model for the stations North Bank, Tocklai, Silcoorie, Mohanbari and Guwahati. Further, it is observed that second order and third order Markov chains are competing with first order in the stations Cherrapunji and Imphal, respectively. A fore-knowledge of rainfall pattern is of immense help not only to farmers, but also to the authorities concerned with planning of irrigation schemes. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

Adaptive-Cox model averaging for right-censored data

ABSTRACTIn medical studies, Cox proportional hazards model is a commonly used method to deal with the right-censored survival data accompanied by many explanatory covariates. In practice, the Akaike's information criterion (AIC) or the Bayesian information criterion (BIC) is usually used to select an appropriate subset of covariates. It is well known that neither the AIC criterion nor the BIC criterion dominates for all situations. In this paper, we propose an adaptive-Cox model averaging procedure to get a more robust hazard estimator. First, by applying AIC and BIC criteria to perturbed datasets, we obtain two model averaging (MA) estimated survival curves, called AIC-MA and BIC-MA. Then, based on Kullback–Leibler loss, a better estimate of survival curve between AIC-MA and BIC-MA is chosen, which results in an adaptive-Cox estimate of survival curve. Simulation results show the superiority of our approach and an application of the proposed method is also presented by analyzing the German Breast Cancer Study dataset.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

Background: Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. Methods: TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Results: Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. Conclusions: The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

Parameter-free modelling of 2D shapes with ellipses

Our goal is to represent a given 2D shape with an automatically determined number of ellipses, so that the total area covered by the ellipses is equal to the area of the original shape without any assumption or prior knowledge about the object structure. To solve this interesting theoretical problem, first we employ the skeleton of the 2D shape which provides important information on the parameters of the ellipses that could approximate the original shape. For a given number of such ellipses, the hard Expectation-Maximisation (EM) algorithm is employed to maximise the shape coverage under the Equal Area constraint. Different models (i.e., solutions involving different numbers of ellipses) are evaluated based on the Akaike Information Criterion (AIC). This considers a novel, entropy-based shape complexity measure that balances the model complexity and the model approximation error. In order to minimise the AIC criterion, two variants are proposed and evaluated: (a) the augmentative method that gradually increases the number of considered ellipses starting from a single one and (b) the decremental method that decreases the number of ellipses starting from a large, automatically defined set. The obtained quantitative results on more than 4000 2D shapes included in standard as well as in custom datasets, quantify the performance of the proposed methods and illustrate that their solutions agree with human intuition.

RVM Kernel Parameters Selection Method based on AIC Applied in Gold Prices

The relationship between the nuclear parameters and model performance is complex, which is from relevance vector machine (RVM) regression model based on Gaussian radial basis kernel function. Aiming at the problem of how to determine the kernel parameters of RVM, a method to selecting kernel parameter of RVM based on AIC criterion is proposed. Firstly, a novel of statistic Q is proposed based on “Akaike” Information Criterion (AIC), while the Q is as a fitness function. Secondly, we use the differential evolution algorithm (Differential Evolution Algorithm, DE) to find the best kernel parameter, in order to choose determine the kernel parameters. Finally, a RVM regression model mode is established and it is used in predicting gold price. Experimental results show that the prediction model has higher precision and better fitting the generalization ability than the traditional method, which demonstrates the AIC-based criteria for selecting RVM kernel parameter method is effective and feasible.

Model selection of copulas: AIC versus a cross validation copula information criterion

Akaike Information Criterion (AIC) is frequently employed in the semiparametric setting of selection of copula models, even though as a model selection tool it was developed in a parametric setting. Recently a Copula Information Criterion (CIC) has been especially designed for copula model selection. In this paper we examine the two approaches and present a simulation study where the performance of a cross-validated version of CIC is compared with the AIC criterion. Only minor differences are observed.

Predicting Emergence of 23 Summer Annual Weed Species

First- and second-year seedbank emergence of 23 summer annual weed species common to U.S. corn production systems was studied. Field experiments were conducted between 1996 and 1999 at the Iowa State University Johnson Farm in Story County, Iowa. In the fall of 1996 and again in 1997, 1,000 seeds for most species were planted in plastic crates. Seedling emergence was counted weekly for a 2-yr period following seed burial (starting in early spring). Soil temperature at 2 cm depth was estimated using soil temperature and moisture model software (STM2). The Weibull function was fit to cumulative emergence (%) on cumulative thermal time (TT), hydrothermal time (HTT), and day of year (DOY). To identify optimum base temperature (Tbase) and base matric potential (ψbase) for calculating TT or HTT, Tbaseand ψbasevalues ranging from 2 to 17 C and −33 to −1,500 kPa, respectively, were evaluated for each species. The search for the optimal model for each species was based on the Akaike's Information Criterion (AIC), whereas an extra penalty cost was added to HTT models. In general, fewer seedlings emerged during the first year of the first experimental run (approximately 18% across all species) than during the second experimental run (approximately 30%). However, second-year seedbank emergence was similar for both experimental runs (approximately 6%). Environmental effects may be the cause of differences in total seedling emergence among years. Based on the AIC criterion, for 17 species, the best fit of the model occurred using Tbaseranging from 2 to 15 C with four species also responding to ψbase= −750 kPa. For six species, a simple model using DOY resulted in the best fit. Adding penalty costs to AIC calculation allowed us to compare TT and HTT when both models behaved similarly. Using a constant Tbase, species were plotted and classified as early-, middle-, and late-emerging species, resulting in a practical tool for forecasting time of emergence. The results of this research provide robust information on the prediction of the time of summer annual weed emergence, which can be used to schedule weed and crop management.

An Evaluation of Information Criteria Use for Correct Cross-Classified Random Effects Model Selection

The authors assessed correct model identification rates of Akaike's information criterion (AIC), corrected criterion (AICC), consistent AIC (CAIC), Hannon and Quinn's information criterion (HQIC), and Bayesian information criterion (BIC) for selecting among cross-classified random effects models. Performance of default values for the 5 indices used by SAS PROC MIXED for estimating a 2-level cross-classified random effects model were compared with modifications to the sample size used in the AICC, CAIC, HQIC, and BIC formulations. The sample sizes explored included the number of level 1 units (N), the average number of classification units (m), and the number of nonempty classification cells (c). The authors also assessed performance of the χ2 diff test for testing the difference in fit between 2 nested cross-classified random effects models. The χ2 diff exhibited a slightly inflated Type I error rate with high power. The modified information criteria performed better than did the default values. Pairing of N with the HQIC, BIC, and CAIC and of m with the AICC worked best. Results and suggestions for future research are discussed.