本文選題：列聯(lián)表 + 確切推斷�。� 參考：《山西醫(yī)科大學(xué)》2005年博士論文

【摘要】：本次研究在對(duì)確切推斷有關(guān)文獻(xiàn)、算法的全面收集、整理和分析基礎(chǔ)上,指出了目前確切推斷理論與算法方面存在的主要問題,其一是確切檢驗(yàn)方法體系及相關(guān)軟件尚不完整,主要表現(xiàn)在大多數(shù)專業(yè)統(tǒng)計(jì)軟件中提供的確切檢驗(yàn)方法基本局限于列聯(lián)表資料的假設(shè)檢驗(yàn);其二是算法和程序?qū)崿F(xiàn)手段單一,主要表現(xiàn)在算法選擇問題上,大多數(shù)專業(yè)統(tǒng)計(jì)軟件單純選擇網(wǎng)絡(luò)算法。由此,對(duì)應(yīng)用于其它數(shù)據(jù)資料和模型的確切檢驗(yàn)方法以及效率更高、更具特色的算法進(jìn)行研究,具有重大的意義。本次研究即在這一背景下,結(jié)合醫(yī)學(xué)領(lǐng)域數(shù)據(jù)特點(diǎn),對(duì)列聯(lián)表?xiàng)l件確切檢驗(yàn)、Hardy-Weinberg平衡條件確切檢驗(yàn)和列聯(lián)表對(duì)數(shù)線性模型擬合優(yōu)度確切檢驗(yàn)的有關(guān)假設(shè)檢驗(yàn)方法進(jìn)行了較為全面的分析,并系統(tǒng)地提出了相應(yīng)的基于遞歸技術(shù)和數(shù)據(jù)庫技術(shù)的完全排列算法。 在列聯(lián)表?xiàng)l件確切檢驗(yàn)方面,分別對(duì)2×2、2×c、r×c列聯(lián)表資料,及s ( 2×2)、s ( 2×c)、s ( r×c)分層列聯(lián)表資料的數(shù)據(jù)結(jié)構(gòu)特點(diǎn)、適用檢驗(yàn)方法、參照系及排列表確切概率等內(nèi)容進(jìn)行了系統(tǒng)的分析與討論。對(duì)于2×2列聯(lián)表,不同的資料收集方式,即完全隨機(jī)設(shè)計(jì)和配對(duì)設(shè)計(jì),對(duì)應(yīng)了兩類不同的檢驗(yàn)方法,由于其確切檢驗(yàn)參照系不同,對(duì)應(yīng)的完全排列算法也不相同;對(duì)于2×c列聯(lián)表,盡管列分類變量可為有序或無序變量,相應(yīng)的假設(shè)檢驗(yàn)方法也比較多,但其確切檢驗(yàn)參照系是相同的,從而僅需設(shè)計(jì)一種完全排列算法,通過構(gòu)造不同的檢驗(yàn)統(tǒng)計(jì)量,即可實(shí)現(xiàn)不同的確切檢驗(yàn); r×c列聯(lián)表的情形與2 ×c列聯(lián)表類似,不同的資料收集方式和不同的分類變量屬性,可分別對(duì)應(yīng)不同的假設(shè)檢驗(yàn)方法,但其確切檢驗(yàn)參照系是相同的,從而通過構(gòu)造不同的檢驗(yàn)統(tǒng)計(jì)量,使用共同的完全排列算法,即可實(shí)現(xiàn)不同的確切檢驗(yàn);對(duì)于各種分層列聯(lián)表資料,一般來說,首先應(yīng)進(jìn)行各層之間的齊性檢驗(yàn),如滿足齊性要求,可進(jìn)一步就行列分類變量之間關(guān)聯(lián)關(guān)系進(jìn)行相應(yīng)的假設(shè)檢驗(yàn),此兩類檢驗(yàn)分別對(duì)應(yīng)了兩種不同的完全排列算法,齊性檢驗(yàn)對(duì)應(yīng)于三維列聯(lián)表對(duì)數(shù)線性模型齊性關(guān)聯(lián)模型的擬合優(yōu)度確切檢驗(yàn),而關(guān)聯(lián)關(guān)系檢驗(yàn)對(duì)應(yīng)于三維列聯(lián)表對(duì)數(shù)線性模型條件獨(dú)立模型的擬合優(yōu)度確切檢驗(yàn)。在算法的構(gòu)造與實(shí)現(xiàn)方面,本研究從一維列聯(lián)表
[Abstract]:Based on the comprehensive collection, arrangement and analysis of the relevant documents and algorithms, this study points out the main problems existing in the theory and algorithm of exact inference at present. One is that the exact testing method system and related software are not complete. This is mainly manifested in the fact that the exact testing methods provided by most professional statistical software are basically limited to the hypothesis testing of the data in the column tables. The second is the single method of algorithm and program implementation, which is mainly manifested in the problem of algorithm selection. Most professional statistical software simply select network algorithm. Therefore, it is of great significance to study the exact test methods and more efficient and characteristic algorithms applied to other data and models. In this context, this study combines the characteristics of medical data, In this paper, the hypothesis testing methods of Hardy-Weinberg equilibrium condition and fitting goodness test of logarithmic linear model are analyzed. The corresponding complete arrangement algorithm based on recursive technology and database technology is proposed systematically. In terms of the exact test of the condition of the column table, the data structure characteristics of 2 脳 2 脳 2 脳 cr 脳 c column data and s (2 脳 2 / 2) (2 脳 2 / s (r 脳 c) stratified table data are respectively applied to the data structure of the data of the 2 脳 2 脳 2 脳 cr 脳 c column, and the method of testing is applied to the data structure of the data. The exact probability of reference frame and permutation table are systematically analyzed and discussed. For the 2 脳 2 column table, different data collection methods, that is, complete random design and pairing design, correspond to two different kinds of inspection methods, and the corresponding complete arrangement algorithms are different because of the difference of the exact test reference frame, and for the 2 脳 c column coupling table, Although column classification variables can be ordered or disordered variables, and the corresponding hypothesis testing methods are more numerous, the exact test reference frame is the same, so it is only necessary to design a complete arrangement algorithm and construct different test statistics. The case of r 脳 c column coupling table is similar to that of 2 脳 c column coupling table. Different data collection methods and different attributes of classification variables can correspond to different hypothesis testing methods, but the exact test frame is the same. Therefore, by constructing different test statistics and using common complete arrangement algorithm, different exact tests can be realized. If the homogeneity requirement is satisfied, we can further test the correlation relationship between column and column classification variables. The two kinds of tests correspond to two different complete permutation algorithms, respectively. The homogeneity test corresponds to the exact test of the goodness of fit of the homogeneous correlation model of the three dimensional list logarithmic linear model, while the correlation relation test corresponds to the exact test of the fit degree of the conditional independent model of the three dimensional list logarithmic linear model. In the construction and implementation of the algorithm, this study starts with the one dimensional list.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2005
【分類號(hào)】：R181.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 黃代新,楊慶恩;卡方檢驗(yàn)和精確檢驗(yàn)在HWE檢驗(yàn)中的應(yīng)用[J];法醫(yī)學(xué)雜志;2004年02期

2 張巖波,何大衛(wèi);對(duì)數(shù)線性模型的IPF算法及其軟件實(shí)現(xiàn)[J];中國(guó)衛(wèi)生統(tǒng)計(jì);1999年05期

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本文編號(hào)：1869843