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 系統識別號 U0026-2107201116465400 論文名稱(中文) 伽瑪分配尺度參數概似比檢定之改進 論文名稱(英文) Improving the likelihood ratio test for scale parameters of Gamma distribution 校院名稱 成功大學 系所名稱(中) 統計學系碩博士班 系所名稱(英) Department of Statistics 學年度 99 學期 2 出版年 100 研究生(中文) 詹嘉豪 研究生(英文) Chia-Hao Chan 學號 R2891101 學位類別 博士 語文別 英文 論文頁數 66頁 口試委員 指導教授-任眉眉口試委員-吳鐵肩口試委員-詹世煌口試委員-劉惠美口試委員-洪明欽 中文關鍵字 交集-聯合式檢定  最大概似比檢定  較強檢力檢定  常態變異數  符號檢定  簡單秩序  雙參數指數分配 英文關鍵字 intersection-union test  likelihood ratio test  more powerful test  normal variance  sign testing  simple-order  two-parameter exponential distribution 學科別分類 中文摘要 本研究論文主要探討伽瑪尺度參數在兩個檢定問題下之概似比改進之研究。 本研究論文假設獨立隨機變數Xij,i=1,…,p,j=1,…,ni,服從伽瑪分配,其中包含未知尺度參數θi和已知形狀參數τi。首先考慮在符號檢定下之概似比改進之研究,檢定H0:θi≤θi0對某些i=1,…,p和H1:θi>θi0 對所有i=1,…,p,其中θi0均為大於零之常數,當p=2時,對任意之α值,0<α<0.4,建構兩個較強檢力檢定 ϕ_{k_1,k_2}^A 和 ϕ_{k_1,k_2}^B\$如同 Liu 和 Berger (1995)和 Saikali 和 Berger (2002)所建構檢定一樣,每一檢定之拒絕域均包含最大概似比之拒絕域和額外之拒絕域,也因此新檢定之檢力比尺度為\$alpha\$的最大概似比檢定強,當p>2時,可藉由ϕ_{k_1,k_2}^A或ϕ_{k_1,k_2}^B\$建構一個齊一較強檢力檢定。之後,考慮簡單秩序檢定之概似比改進之研究,檢定H0:θi+1≤θi對某些i=1,…,p-1和H1:θ1<⋯<θp,針對其檢定問題,當p=3時,對任意之α值,0<α<0.4,建構兩齊一較強檢力檢定ϕ_{k_3^*,k_2^*,k_1^*}^A和ϕ_{k_3^*,k_2^*,k_1^*}^B\$,同樣地,每一檢定之拒絕域均包含最大概似比之拒絕域和額外之拒絕域,因此,其檢力比最大概似比檢定強,當p>3時,也可藉由ϕ_{k_3^*,k_2^*,k_1^*}^A或ϕ_{k_3^*,k_2^*,k_1^*}^B建構一個齊一較強檢力檢定。值得一提的是此建構方法和之前有關齊一較強檢力檢定的建構方法均不同,且到目前為止針對簡單秩序檢定問題無人成功的建構齊一較強檢力檢定。針對此兩檢定問題,本研究論文所建構之齊一較強檢力檢定均是交集-聯集式檢定,且本研究論文成果也可應用在雙參數指數分配中之尺度參數和常態變異數下之檢定問題,呈現一組資料在兩檢定問題之下之檢定結果。 英文摘要 In this study, we consider the sign testing and simple-order testing problems for gamma scale parameters. For each testing problem, we propose two new tests that are uniformly more powerful than the likelihood ratio tests (LRT). For 1,…,p, let Xij, j=1,…,ni, denote independent random samples that have gamma distributions with a unknown scale parameter θi and a known shape parameter τi. Let θ10,…,θp0 be positive constants. We consider the sign testing problem of testing H0:θi≤θi0 for some i=1,…,p versus H1:θi>θi0 for all i=1,…,p. For p=2 and any 0<α<0.4, we propose two new tests, ϕ_{k_1,k_2}^A and ϕ_{k_1,k_2}^B, which are similar to the tests proposed by Liu and Berger (1995) and Saikali and Berger (2002) in that they contain the rejection region for the LRT and the additional sets that touch the LRT rejection region at only a single point. However, the sizes of ϕ_{k_1,k_2}^A and ϕ_{k_1,k_2}^B are still α. Therefore, ϕ_{k_1,k_2}^A and ϕ_{k_1,k_2}^B are uniformly more powerful than the LRT. Then, we propose a uniformly more powerful test constructed by either ϕ_{k_1,k_2}^A or ϕ_{k_1,k_2}^B for a general testing problem (p>2). Subsequently, we consider the simple-order testing problem of testing H_0:θi+1≤θi for some i=1,…,p-1 versus H1:θ1<⋯<θp. For p=3 and any 0<α<0.4, we propose two tests, ϕ_{k_3^*,k_2^*,k_1^*}^A and ϕ_{k_3^*,k_2^*,k_1^*}^B. Again, these new tests contain the rejection region for the LRT and the additional region. Therefore, the new tests are uniformly more powerful than the LRT. Analogically, we propose a uniformly more powerful test constructed by either ϕ_{k_3^*,k_2^*,k_1^*}^A or ϕ_{k_3^*,k_2^*,k_1^*}^B for a general testing problem (p>3). To our knowledge, no one has obtained a uniformly more powerful test as yet. In summary, these new tests contain the rejection region for the LRT and the additional region. These proposed tests are intersection-union tests (IUTs). We apply our results to test the scale parameters of two-parameter exponential distributions and normal variances for the sign testing problem and simple-order testing problem, respectively. Finally, we illustrate the proposed tests with appropriate data. 論文目次 1 Intorduction 1 2 Literature Reviews 4 2.1 Uniformly more powerful tests for normal means 4 2.1.1 Construction method with known covariance matrix 5 2.1.2 Construction method with a common unknown variance 7 2.1.3 Construction method with different unknown variances for independent samples 10 2.2 Uniformly more powerful tests for gamma scale parameters 12 2.3 Concluding Remarks 14 3 Uniformly More Powerful Tests for Sign Testing Problem 16 3.1 Testing problem (3.0.1) when p=2 17 3.1.1 Uniformly more powerful test: ϕ_{k_1,k_2}^A 17 3.1.2 Another uniformly more powerful test: ϕ_{k_1,k_2}^B 21 3.1.2 Power comparison 25 3.2 A more powerful test in the general problem 26 4 Uniformly More Powerful Tests for Simple-Order Testing Problem 32 4.1 Testing problem (4.0.1) when p=3 32 4.1.1 Preliminary definitions and lemmas 32 4.1.2 Uniformly more powerful test: ϕ_{k_3^*,k_2^*,k_1^*}^A 37 4.1.3 Another uniformly more powerful test: ϕ_{k_3^*,k_2^*,k_1^*}^B 40 4.1.4 Power comparison 43 4.2 A more powerful test in the general problem 48 4.3 Selection on d 49 5 Application on Some Distributions with A Data Example 51 5.1 Two-parameter exponential distributions 51 5.2 Normal distributins 52 5.3 A data example 53 6 Conclusions and Future Study 55 Bibliography 57 A Proofs of Lemmas 59 A.1 Proofs of Lemmas 3.1.4 59 A.2 Proofs of Lemmas 4.1.6 61 A.3 Proofs of Lemmas 4.1.8 62 A.4 Proofs of Lemmas 4.1.9 63 A.5 Proofs of Lemmas 4.1.14 64 A.6 Proofs of Lemmas 4.1.15 65 參考文獻 Berger, R. L. (1982). Multiparameter hypothesis testing and acceptance sampling. Technometrics 24 295--300. Berger, R. L. (1989). Uniformly more powerful tests for hypotheses concerning linear inequalities and normal means. J. Am. Statist. Assoc. 84 192--199. Berger, R. L. and Hsu, J. C. (1996). Bioequivalence trials, intersection-union tests and equivalence confidence sets. Statist. Sci. 11(4) 283--319. Berger, R. L. (1997). Likelihood ratio tests and intersection-union tests,In Advances in Statistical Decision Theorey and Applications (S. Panchapakesan and N. Balakrishnan, eds.) 225--237. Birkh"{a}user, Boston. Cohen, A., Gatsonis, C. and Marden, J. I. (1983). Hypothesis tests and optimality properties in discrete multivariate analysis. In Studies in Econometrics, Time Series and Multivariate Statistics (S. Karlin, T. Amemiya and L. A. Goodman, eds.) 379--405. Academic Press, New York. Gutmann, S. (1987). Tests uniformly more powerful than uniformly most powerful monotone tests. J. Statist. Plann. Inference 17 279--292. Iwasa, M. (1991). Admissibility of unbiased tests for a composite hypothesis with a restricted alternative. Ann. Inst. Statist. Math. 43 657--665. Lehmann, E. L. (1952). Testing multiparameter hypotheses. Ann. Math. Statist. 23 541--552. Li, T. and Sinha, B. K. (1995). Tests of ordered hypotheses for gamma scale parameters. J. Statist. Plann. Inference 45 385--397. Liu, H. and Berger, R. L. (1995). Uniformly more powerful tests for one-sided hypotheses about linear inequalities. Ann. Statist. 23 55--72. Liu, W. (1995). On the existence of tests uniformly more powerful than the likelihood ratio test. J. Statist. Plann. Inference 44 19--35. Liu, H. (1999). Linear inequality hypotheses and uniformly more powerful tests. J. Chinese Statist. Assoc. 37 307--331. Liu, H. (2000). Uniformly more powerful, two-sided for hypotheses about linear Inequalities. Ann. Inst. Statist. Math. 52 15--27. McDermott, M. P. and Wang, Y. (2002). Construction of uniformly more powerful tests for hypotheses about linear inequalities. J. Statist. Plann. Inference 107 207--217. Nomakuchi, K. and Sakata, T. (1987). A note on testing two-dimensional normal mean. Ann. Inst. Statist. Math. 39 489--495. Proschan, F. (1963). Theoretical explanation of observed decreasing failure rate. Technometrics 5 375--383. Saikali, K. G. and Berger, R. L. (2002) More powerful tests for the sign testing problem. J. Statist. Plann. Inference 107 187--205. Sasabuchi, S. (1980). A test of a multivariate normal mean with composite hypotheses determined by linear inequalities. Biometrika 67 429--439. Sasabuchi, S. (1988). A multivariate one-sided test with composite hypotheses determined by linear inequalities when the covariance matrix has an unknown scale factor. Memoirs Faculty Sci., Kyushu University A 42 37--46. Sasabuchi, S., Tanaka, K. and Tsukamoto, T. (2003). Testing homogeneity of multivariate normal mean vectors under an order restriction when the covariance matrices are common but unknown. Ann. Statist. 31(5) 1517--1536. Sasabuchi, S. (2007). More powerful tests for homogeneity of multivariate normal mean vectors under order restriction. Sankhya 69(4) 700--716. Shirley, A. G. (1992). Is the minimum of several location parameters positive? J. Statist. Plann. Inference 31 67--79. Sierra-Cavazos, J. H. (1992). Tests of hypotheses defined linear inequalities for elliptically contoured families. Ph. D. dissertation, North Carloina State Univ. Sinha, S. K. (1986). Reliability and Life Testing. Wiley Eastern, New Delhi. 論文全文使用權限 同意授權校內瀏覽/列印電子全文服務，於2020-01-01起公開。 如您有疑問，請聯絡圖書館 聯絡電話：(06)2757575#65773 聯絡E-mail：etds@email.ncku.edu.tw 