Preliminary Comparison of Several Statistical Models for Sugarcane Variety Stability Analysis
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Several statistical analysis models for estimating the sugarcane variety stability were compared in this paper using the data of sugarcane varietal regional trial in Guangdong province in 2009.The results showed that Finlay and Wilkinson regression model was easier,but both the analysis of the other two models were more comprehensive,and there was a bit difference between AMMI model and LR-PCA model.In practice,while the proper statistical method was usually considered according to the different data,it should be also considered that the same data be analyzed with different statistical methods in order to get a more reasonable result by comparison.Keywords:
Statistical Analysis
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The choice of the appropriate linear model before this can be used for planning and decision making, has been the concern of many statistical workers. Most of the methods in the literature aim at evaluating the descriptive ability of the candidate models.
In the present paper an evaluation scheme of the predictability of a linear model based on a function of the discrepancy of the observed and the corresponding predicted values of the dependent variable is studied. Based on this statistical function, the predictability of a linear model is tested. Considering the ratio of such functions for two linear models, the predictability of these models is compared. Applications on real and simulated data are also presented
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The mixed treatment comparison (MTC) method has been proposed to combine results across trials comparing several treatments. MTC allows coherent judgments on which of the treatments is the most effective. It produces estimates of the relative effects of each treatment compared with every other treatment by pooling direct and indirect evidence. In this article, we explore how this methodological framework can be used to rank a large number of agricultural crop species from yield data collected in field experiments. Our approach is illustrated in a meta‐analysis of yield data obtained in 67 field studies for 36 different bioenergy crop species. The considered dataset defines a network of comparisons of crop species. We introduce several Bayesian MTC models based on baseline treatment contrasts and evaluate the practical advantages of these models to produce yield ratio estimates. We explore the consistency of some estimates by node‐splitting and compare our results to those obtained with a classical two‐way linear mixed model. Results reveal that the model showing the lowest deviance information criterion (DIC) includes both study random effects and study‐specific residual variances. But all the tested models including study random effects lead to similar yield ratio estimates. The proposed Bayesian framework allows an in‐depth analysis of the uncertainty in the species ranking.
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According to the linear model concept,this paper proposed the method for improving the variance estimate of the new crop variety performances by using a covariate.The norm for assessing usefulness of different estimators is pointed out.Then the possibility and the condition for improvement of the variance estimate of the new crop variety performances by using a covariate are theoretically discussed.As an application example,the yield data from regional wheat trial in irrigation area of Middle Shaanxi are analyzed.
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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.
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Crop simulation model
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The purpose of this study focused on modeling the population of Ethiopia using different models and estimating the models parameters via least square method. The models, that were applied for the population growth, were Malthus growth model, Logistic growth model and General growth model. To identify the models which performed effectively in prediction of the actual population, the measure accuracy has been used, such that the models satisfying the criteria of the measure of accuracy is the best statistical model. The results of the analysis were presented using tables and graphical form which are very good to perform comparison for the effectiveness of the models. In this study, MAPE, RSE , MAD and R2 which are considered to measure the accuracy of the models. Malthus growth model, Logistic growth model and General growth model used the population of Ethiopia from 1980 to 2020 inclusive, the data was obtained from international data base(IDB). R studio 3.6.3 were used to estimate the models parameters using simple codes. The study proposed to project the population of Ethiopia via General growth model which performed best in measure of accuracies that makes it effective and efficient as compare with the other models. The model had the smallest RSE ( 492,155 ), MAPE (0.75%) and MAD ( 379,942 ) as well as the highest R2(99.97%) relative to the other models. Keywords: Logistic model, parameter estimation, Malthus model, General model, selection methods DOI: 10.7176/JNSR/12-1-05 Publication date: January 31 st 2021
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It is necessary for statistical producer to develop some effectual and reliable evaluation methods of statistical data accuracy.Referring to the evaluation techniques of measurement error effects adopted in social survey programs and taking the structure characteristics of Chinese government statistics into account,an error effects model for statistical data has been constructed.Using the model,statistical data accuracy can be defined based on individual and total value two levels,and aiming at system bias and variance two types of error effects.After identification and estimation of parameters in the model,the systematic bias degree in statistical data can be evaluated,the effects of those factors such as statistical institution,field operation,and units' attribute on statistical data accuracy determined,significant statistical error sources detected,and statistical institution developed consequently.
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Statistical Analysis
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