Дисертації з теми "Two Wave Missing Data"
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Belen, Rahime. "Detecting Disguised Missing Data." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610411/index.pdf.
Повний текст джерелаChen, Lihan. "Two-stage maximum likelihood approach for item-level missing data in regression." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62724.
Повний текст джерелаArts, Faculty of
Psychology, Department of
Graduate
Kosler, Joseph Stephen. "Multiple comparisons using multiple imputation under a two-way mixed effects interaction model." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1150482904.
Повний текст джерелаBailey, Brittney E. "Data analysis and multiple imputation for two-level nested designs." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531822703002162.
Повний текст джерелаKellermann, Anh Pham. "Missing Data in Complex Sample Surveys: Impact of Deletion and Imputation Treatments on Point and Interval Parameter Estimates." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7633.
Повний текст джерелаDunu, Emeka Samuel. "Comparing the Powers of Several Proposed Tests for Testing the Equality of the Means of Two Populations When Some Data Are Missing." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278198/.
Повний текст джерелаBašić, Edin. "The problem of missing residential mobility information in the german microcensus : an evaluation of two statistical approaches with the socio-economic panel /." Hamburg : Kovač, 2008. http://swbplus.bsz-bw.de/bsz286991586cov.htm.
Повний текст джерелаYang, Hanfang. "Jackknife Emperical Likelihood Method and its Applications." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/9.
Повний текст джерелаMartins, Francisco T. R. França. "Take-two interactive software - risk of missing the growth wave." Master's thesis, 2017. http://hdl.handle.net/10362/25883.
Повний текст джерелаHung, Jui-Chung, and 洪瑞鍾. "Two-Stage Signal Reconstruction under Unknown Parameters and Missing Data." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/43097974010581810123.
Повний текст джерелаHuang, Yu-Liang, and 黃友亮. "Development of an Iterative Filter toRepair a Composite Wave with Missing Data." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26105329344854659199.
Повний текст джерела國立成功大學
航空太空工程學系碩博士班
93
The iterative filter using the Gaussian smoothing method is employed to decompose and repair a tide data string composed of many tidal wave components. Since the iterative filter can ignore the effect of missing data to certain extent, the tidal wave components can be successively decomposed. However, because the employed wave decomposition method cannot decompose a composite wave found by two wave components whose frequencies close to each other, three beats are found. For those wave components whose wavelengths are larger than the square root of two times the drop-out period, the missing data can be satisfactorily achieved by merely applying the filter. For a longer period of missing data, an iterative technique is developed to repair the data. The tide data of the Houbihu harbor in Pingtung at south part of Taiwan during the period of Jan. 1 through Dec. 31/2001 and the Cheng-Kung harbor in Taitung at east part of Taiwan during the period of Aug.1/2002 through Jul.31/2004 are employed to demonstrate the procedure of wave decomposition and data repairing. Finally, the enhanced Morlet transform is employed to examine the repaired composite waves. Results show that the frequencies of all the standard tide waves are precisely captured and even exhibit the frequency variations in some standard tides not understood before. It means that the present data repairing technique is helpful. Moreover, it is proven that the enhanced Morlet is a new powerful tool for time-frequency analysis.
Chen, Yong-Yu, and 陳詠妤. "Robust Designs against Missing Data in Two-Color cDNA Microarray Experiments." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/27437641691836864687.
Повний текст джерела國立臺灣大學
農藝學研究所
97
Microarray has been an increasingly popular biotechnology in measuring gene expression of a biological sample. In contrast to the traditional methods, it enables biologists to evaluate ten thousands of mRNA sequences in expression simultaneously. Statistical methods developed for such large-scale and complex data from microarray experiments have been intensively explored within recent years. On the other hand, the studies on the design issues need further investigation. In the current study, we focus on one of the important design issues in two-color mciroarray experiments. We propose a new criterion to evaluate the robustness of designs against missing data. Furthermore, we construct the robust designs suitable for the two-color microarray experiments. Missing data are frequently confronted during a microarray experiment. Conventionally, one performs analysis either estimating or ignoring missing data. However, we are currently interested in selection of good designs that may provide high robustness against missing data. We first present two linear models in characterizing the data of a two-color microarray experiment according to whether or not take into account the variation between two fluorescent dyes. Then we seek for the robust designs based on the proposed models and the robustness criterion. The criterion we proposed is to compute the proportions of the connected residual designs for the class of block designs of size two and that of row-column designs with two rows. The robust designs are defined as those who have the maximal proportions if a number of blocks (columns) are missing. In specificity, we investigate two kinds of experiments in this thesis, including treatment comparative experiments and test-control experiments. Two classes of practical designs are respectively proposed for these two kinds of experiments using two-color microarrays.
"Multiple Imputation for Two-Level Hierarchical Models with Categorical Variables and Missing at Random Data." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.40705.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Educational Psychology 2016
Yang, Chin-Pin, and 楊志斌. "Application of Neural Network for Wave Data Complement between Two Recording Stations." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/90113568527875639910.
Повний текст джерела國立中興大學
土木工程學系
92
ABSTRACT Accurate prediction for the wave climate is an essential part in the ocean engineering. The precision of forecasting the time series of wave data using the past wave records in the same station is not good in general. Thus this paper attempts to apply the back-propagation neural network (BPN) for the complement of wave data between two recording stations under the same condition of wind field. The model does not only be able to forecast wave, but also be used in supplementing the wave data. The field data used in the testing model were measured in two stations, one is in Keelung Harbor and the other is in Bitoujiao. The performance of the neural network model was first discussed by using two indices, root-mean square error and the correlation coefficient. It is found that the neural network model performs well for the wave complementary when a 45- days wave record was is used in the training process of back-propagation neural network for the situation of season wind. For typhoon waves, it is also found that the neural network model could also be applied well in the complementary of significant wave heights.
"Examining solutions to two practical issues in meta-analysis: dependent correlations and missing data in correlation matrices." 2000. http://library.cuhk.edu.hk/record=b6073284.
Повний текст джерела"August 2000."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2000.
Includes bibliographical references (p. 117-123).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
Liao, Chih-Feng, and 廖志峰. "The Effect of the Missing Data Techniques onCross-Lagged Panel Analysis in Two-wavePaired Data : Case Study Using theimpact of self-determination process andromantic attachment on dating violencer data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/16606479736339805835.
Повний текст джерела國立臺北大學
統計學系
101
In the two-wave paired questionnaire study, the incomplete problems in the second wave usually are more complicated than single questionaire. This the- sis uses ”The impact of self-determination process and romantic attachment on dating violencer” data to explore the missing treatment on the incomple- tion of male or female response in the second way regarding the statistical analysis for Cross-Lagged Panel analysis, analysis of the motivations of love and attachment systems-oriented relations. We, first, used the complete part of two wave pair data as the baseline. Logistic regression was used, then, to find the significant predicted variables based on these significant predictor variables,the respondents were divided into four groups. We then used the random sampling method to construct 30 missing data sets, each of them having the same missing pattern with the original data set. We compare on the effect of the four missing treatments including list-wise deletion, regres- sion, logistic regression, and Monte Carlo Markov Chain, Cross-Lagged Panel analysis best treatment found in this stage is used to process Cross-Lagged Panel analysis on the original data set, and compare the results before the missing treatment. The procedure using use of simulated missing data set to identify the optimal missing treatment, can be used for how to deal with the missing data in two wave pair data as a references.