影响我国大豆进口量的因素分析

当前我国大豆出现严重的产需不平衡问题,大豆对外依存度很高,我国粮食问题在大豆方面存在较大的安全隐患。本文通过建立向量自回归模型,对我国人口总数、大豆种植面积和大豆进口量之间的动态关系进行分析。结果表明:我国人口总数对大豆进口量有一个长期的正向贡献作用,我国的大豆播种面积对大豆进口量有一个长期的负向作用。本文根据研究结果提出相关建议,为政府部门制定相关政策保障大豆安全提供一定的依据。     

大豆的生化成分和生理功效

对大豆蛋白、大豆多肽、大豆油脂、大豆异黄酮、大豆皂甙、大豆低聚糖等活性成分与生理功效作了系统地阐述。     

大豆的生化成分和生理功效

对大豆蛋白、大豆多肽、大豆油脂、大豆异黄酮、大豆皂甙、大豆低聚糖等活性成分与生理功效作了系统地阐述。     

2004年全球转基因作物播种面积年比增长了20%

据总部设在美国的农业生物技术应用国际服务组织(ISAAA)公布的一份报告显示,2004年全球转基因玉米、大豆、棉花和其它作物播种面积达到了2亿英亩,比2003年的1.67亿英亩增长了20％。 ISAAA称,尽管美国仍是最大的转基因作物生产国,但是在过去一年中发展中国家的转基因农作物播种     

大豆的生化成分和生理功效

对大豆蛋白、大豆多肽、大豆油脂、大豆异黄酮、大豆皂甙、大豆低聚糖等活性成分与生理功效作了系统地阐述.     

经济运行开局良好

今年以来,各地区、各部门认真贯彻落实党的十六大精神和中央关于经济工作的部署,聚精会神搞建设,一心一意谋发展,努力做好各方面工作,取得了积极成效。据国家统计局初步测算,一季度国内生产总值达23562亿元,增长9.9%,增幅同比提高2.3个百分点,是1997年以来同期增长最快的。一、农业种植结构继续调整,农村经济平稳发展。据国家统计局种植意向调查,受市场供求变化影响,2003年全国粮食播种面积为15.25亿亩,比上年调减2.2%。其中,夏粮播种面积3.96亿亩,调减3.8%;大豆种植面积有所恢复,预计为1.38亿亩,增长5.8%。由于市场棉价上扬,棉花生产收益回…     

腐竹主要有哪些营养价值

腐竹的生产原料是大豆,大豆起源于中国,距现在已有4500多年的历史。大豆营养丰富,堪称大地乳汁。大豆蛋白质含量一般为37％～42％,优质大豆蛋白质含量可达45％,甚至48％以上。大豆除含丰富的营养成分外,还含有大豆多肽、大豆皂苷、大豆低聚糖、大豆     

17.5%精喹禾灵乳油大豆田杂草药效示范试验

结合工作实践,对精喹禾灵乳油大豆田杂草药效示范试验作以简要阐述。     

如何食用和贮存酱油

优质酱油一般是用大豆制成(高级酱油还添加蘑菇作原料)，豆类中的蛋白质经发酵、水解后可形成多种氨基酸等营养物质，因而酱油能产生鲜味。     

浅谈大豆高产栽培技术

大豆是我国当前供求矛盾最为突出的粮油兼用作物,是人们生活的重要植物蛋白质来源。开展大事高效栽培对提高我国大豆的产量、改善品质、降低成本、提高市场竞争力、促进大豆产区的经济发展、增加农民收入具有十分重要的意义。现针对大豆的高产栽培技术进行了探讨,提出了有效的高产和稳产的方案,对提高我国的大豆栽培技术及大豆产量、质量贡献一份力量。     

Ridge regression revisited

In general ridge (GR) regression p ridge parameters have to be determined, whereas simple ridge regression requires the determination of only one parameter. In a recent textbook on linear regression, Jürgen Gross argues that this constitutes a major complication. However, as we show in this paper, the determination of these p parameters can fairly easily be done. Furthermore, we introduce a generalization of the GR estimator derived by Hemmerle and by Teekens and de Boer. This estimator, which is more conservative, performs better than the Hoerl and Kennard estimator in terms of a weighted quadratic loss criterion.     

Standardization of Variables and Collinearity Diagnostic in Ridge Regression

Ridge estimation (RE) is an alternative method to ordinary least squares when there exists a collinearity problem in a linear regression model. The variance inflator factor (VIF) is applied to test if the problem exists in the original model and is also necessary after applying the ridge estimate to check if the chosen value for parameter k has mitigated the collinearity problem. This paper shows that the application of the original data when working with the ridge estimate leads to non‐monotone VIF values. García et al. (2014) showed some problems with the traditional VIF used in RE. We propose an augmented VIF, VIF_R(j,k), associated with RE, which is obtained by standardizing the data before augmenting the model. The VIF_R(j,k) will coincide with the VIF associated with the ordinary least squares estimator when k = 0. The augmented VIF has the very desirable properties of being continuous, monotone in the ridge parameter and higher than one.     

Ridge regression estimation of the Rotterdam model

The best guesses of unknown coefficients specified in Theil's model of introspection are like predictions and not like de Finetti's prevision and therefore not the values taken by random variables. Constrained least squares procedures can be formulated which are free of these difficulties. The ridge estimator is a simple version of a constrained least squares estimator which can be made operational even when little prior information is available. Our operational ridge estimators are nearly minimax and are not less stable than least squares in the presence of high multicollinearity. Finally, we have presented the ridge estimates for the Rotterdam demand model.     

探讨磷脂专利技术与应用

对大豆浓缩磷脂的间歇球罐脱水工艺、半连续立式薄膜蒸发工艺的特点分别进行了分析。介绍了大豆浓缩磷脂在脱水前进行流化、漂白、均质和脱水中添加“快速脱水剂”脱水后进行急冷、脱臭、再均质的磷脂生产新工艺。新工艺较传统工艺不仅提高了磷脂产品质量,还使脱水时间降低一半以上。     

A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies

Justifying ridge regression from a geometrical perspective is one of the main contributions of this paper. To the best of our knowledge, this question has not been treated previously. This paper shows that ridge regression is a particular case of raising procedures that provide greater flexibility by transforming the matrix X associated with the model. Thus, raising procedures, based on a geometrical idea of the vectorial space associated with the columns of matrix X , lead naturally to ridge regression and justify the presence of the well-known constant k on the main diagonal of matrix X ^′ X . This paper also analyses and compares different alternatives to raising with respect to collinearity mitigation. The results are illustrated with an empirical application.     

Ridge estimation of house value determinants

This study considers structural and lot characteristics which are determinants of equilibrium prices of homes. Previous studies which have attempted this estimation have been plagued by problems of multicollinearity. Micro data on detailed house characteristics are used with a biased estimation technique called ridge regression to obtain estimated characteristic prices with more correct signs and increased stability. The ridge prices give a more precise indication of how characteristics influence house prices. As a method of finding house value determinants ridge regression is shown to be superior to ordinary least-squares regression.     

Ridge regression and its degrees of freedom

For ridge regression the degrees of freedom are commonly calculated by the trace of the matrix that transforms the vector of observations on the dependent variable into the ridge regression estimate of its expected value. For a fixed ridge parameter this is unobjectionable. When the ridge parameter is optimized on the same data, by minimization of the generalized cross validation criterion or Mallows \(\hbox {C}_{L}\) , additional degrees of freedom are used however. We give formulae that take this into account. This allows of a proper assessment of ridge regression in competitions for the best predictor.     

Anomalies in the Foundations of Ridge Regression

Errors persist in ridge regression, its foundations, and its usage, as set forth in Hoerl & Kennard (1970) and elsewhere. Ridge estimators need not be minimizing, nor a prospective ridge parameter be admissible. Conventional estimators are not LaGrange's solutions constrained to fixed lengths, as claimed, since such solutions are singular. Of a massive literature on estimation, prediction, cross–validation, choice of ridge parameter, and related issues, little emanates from constrained optimization to include inequality constraints. The problem traces to a misapplication of LaGrange's Principle, unrecognized singularities, and misplaced links between constraints and ridge parameters. Alternative principles, based on condition numbers, are seen to validate both conventional ridge and surrogate ridge regression to be defined. Numerical studies illustrate that ridge regression as practiced often exhibits pathologies it is intended to redress.     

Market reactions to trade friction between China and the United States: Evidence from the soybean futures market

In March 2018, the US used an immense trade deficit as an excuse to provoke trade friction with China. This study uses the EGARCH model and event study methods to study the impact of the major risk event of Sino-US trade friction on soybean futures markets in China and the United States. Results indicate that the Sino-US trade friction weakened the return spillover effect between the soybean futures markets in China and the US, and significantly increased market volatilities. As the scale of additional tariffs increased, the volatility of the Chinese soybean futures market declined; however, the volatility of the US soybean futures market did not weaken. In addition, expanding the sources of soybean imports helped ease the impact of tariffs on China’s soybean futures market, while the decline in US soybean exports to China intensified the volatility of the US soybean futures market. In addition, while the release of multiple tariff increases has had a short-term impact on the returns of soybean futures markets, the impact of trade friction has gradually decreased.     

The generalized ridge estimator and improved adjustments for regression parameters

Summary The generalized ridge estimator, which considers generalizations of mean square error, is presented, and a mathematical rule of determining the optimalk-value is discussed. The generalized ridge estimator is examined in comparison with the least squares, the pseudoinverse, theJames-Stein-type shrinkage, and the principal component estimators, especially focusing their attention on improved adjustments for regression coefficients. An alternative estimation approach that better integrates a priori information is noted. Finally, combining the generalized ridge and robust regression methods is suggested.