Pub Date : 2012-08-31DOI: 10.5923/J.STATISTICS.20120201.01
S. Er
The main aim of this paper is to examine the efficiency of Genetic Algorithm (GA) of Keskinturk and Er (2007)[1], Kozak’s (2004) Random Search[2] and Lavallee and Hidiroglou’s (1988) Iterative Algorithm method[3] on determination of the stratum boundaries that minimize the variance of the estimate. Initial starting boundaries of the mentioned algorithms are obtained randomly. Here, it is aimed to reach better results in a shorter period of time by utilizing the initial boundaries obtained from Gunning and Horgan’s (2004) geometric method[4] compared to the random initial boundaries. Three algorithms are applied on various populations with both random and geometric initial boundaries and their performances are compared. With the stratification of 11 heterogenous populations that have different properties, higher variance of the estimates or infeasible solutions can be observed once the initial boundaries are obtained with geometric method.
本文的主要目的是检验Keskinturk和Er(2007)[1]、Kozak(2004)随机搜索[2]和Lavallee和Hidiroglou(1988)迭代算法[3]的遗传算法(GA)在确定地层边界时的效率，使估计的方差最小。上述算法的初始起始边界是随机得到的。本文的目的是利用Gunning and Horgan(2004)几何方法[4]得到的初始边界与随机初始边界相比，在更短的时间内获得更好的结果。将三种算法分别应用于具有随机初始边界和几何初始边界的不同种群，并比较了它们的性能。在对11个具有不同性质的异质群体进行分层时，用几何方法求得初始边界时，会出现估计方差较大或解不可行的情况。
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Pub Date : 2012-08-09DOI: 10.5923/J.STATISTICS.20120204.03
R. Arunachalam, V. Balakrishnan
The present investigation was carried out to study the trends in area, production and productivity of wheat crop grown during the period 1950-1951 to 2009-2010 in India. Different non-linear models were employed to study the trends in area, production and productivity. When none of the non-linear models were found suitable to fit the trends nonparametric regression model was employed. None of the non-linear model was found suitable to fit the trends in area data. The Sinusoidal model was found suitable to fit the trends in production as well as productivity of wheat crop grown in India. The results indicated that area, production and productivity of wheat crop grown in India, had been shown in the increasing trend. The area of cultivation had played a major role in increasing the trend in production.
{"title":"Statistical Modeling for Wheat (Triticum Aestivum) Crop Production","authors":"R. Arunachalam, V. Balakrishnan","doi":"10.5923/J.STATISTICS.20120204.03","DOIUrl":"https://doi.org/10.5923/J.STATISTICS.20120204.03","url":null,"abstract":"The present investigation was carried out to study the trends in area, production and productivity of wheat crop grown during the period 1950-1951 to 2009-2010 in India. Different non-linear models were employed to study the trends in area, production and productivity. When none of the non-linear models were found suitable to fit the trends nonparametric regression model was employed. None of the non-linear model was found suitable to fit the trends in area data. The Sinusoidal model was found suitable to fit the trends in production as well as productivity of wheat crop grown in India. The results indicated that area, production and productivity of wheat crop grown in India, had been shown in the increasing trend. The area of cultivation had played a major role in increasing the trend in production.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":"90 1","pages":"40-46"},"PeriodicalIF":0.0,"publicationDate":"2012-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84468339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-08-09DOI: 10.5923/J.STATISTICS.20120204.02
A. C. Kelechi
This paperdiscusses a comparat ive analysis on balanced incomp lete block designs by using the classical analysis of variance (ANOVA ) method. Fortunately, the data co llected for the analysis were in t wo groups of the balanced incomp lete-block designs (BIBD's), that is, symmetric, and unsymmetric (BIBD's). In this paper, the basic interest is to apply classical ANOVA on the two types of BIBD's and check whether they are significant and also minimizes error. A secondary data fro m N.R.C.R.I, Umud ike, Abia State was used. To ach ieve this, we shall consider treat ment (adjusted), b lock (adjusted) treatment (not adjusted) in the classical ANOVA method on the available data. Though, symmetric balanced incomp lete block design (SBIBD) and unsymmetric balanced incomp lete block design (USBIBD) are significant, it is pertinent to note that the SBIBD classical A NOVA method is found to be preferable to the USBIBD with reference to their variances at different level of significance.
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