Dissertations / Theses on the topic 'Two Wave Missing Data'
To see the other types of publications on this topic, follow the link: Two Wave Missing Data.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 16 dissertations / theses for your research on the topic 'Two Wave Missing Data.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Belen, Rahime. "Detecting Disguised Missing Data." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610411/index.pdf.
Full textAbstract:
In some applications, explicit codes are provided for missing data such as NA (not available) however many applications do not provide such explicit codes and valid or invalid data codes are recorded as legitimate data values. Such missing values are known as disguised missing data. Disguised missing data may affect the quality of data analysis negatively, for example the results of discovered association rules in KDD-Cup-98 data sets have clearly shown the need of applying data quality management prior to analysis. In this thesis, to tackle the problem of disguised missing data, we analyzed embedded unbiased sample heuristic (EUSH), demonstrated the methods drawbacks and proposed a new methodology based on Chi Square Two Sample Test. The proposed method does not require any domain background knowledge and compares favorably with EUSH.
2
Chen, 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.
Full textAbstract:
Psychologists often use scales composed of multiple items to measure underlying constructs, such as well-being, depression, and personality traits. Missing data often occurs at the item-level. For example, participants may skip items on a questionnaire for various reasons. If variables in the dataset can account for the missingness, the data is missing at random (MAR). Modern missing data approaches can deal with MAR missing data effectively, but existing analytical approaches cannot accommodate item-level missing data. A very common practice in psychology is to average all available items to produce scale means when there is missing data. This approach, called available-case maximum likelihood (ACML) may produce biased results in addition to incorrect standard errors. Another approach is scale-level full information maximum likelihood (SL-FIML), which treats the whole scale as missing if even one item is missing. SL-FIML is inefficient and prone to bias. A new analytical approach, called the two-stage maximum likelihood approach (TSML), was recently developed as an alternative (Savalei & Rhemtulla, 2017b). The original work showed that the method outperformed ACML and SL-FIML in structural equation models with parcels. The current simulation study examined the performance of ACML, SL- FIML, and TSML in the context of bivariate regression. It was shown that when item loadings or item means are unequal within the composite, ACML and SL-FIML produced biased estimates on regression coefficients under MAR. Outside of convergence issues when the sample size is small and the number of variables is large, TSML performed well in all simulated conditions, showing little bias, high efficiency, and good coverage. Additionally, the current study investigated how changing the strength of the MAR mechanism may lead to drastically different conclusions in simulation studies. A preliminary definition of MAR strength is provided in order to demonstrate its impact. Recommendations are made to future simulation studies on missing data.Arts, Faculty of
Psychology, Department of
Graduate
3
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.
Full text
4
Bailey, 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.
Full text
5
Kellermann, 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.
Full textAbstract:
The purpose of this simulation study was to evaluate the relative performance of five missing data treatments (MDTs) for handling missing data in complex sample surveys. The five missing data methods included in this study were listwise deletion (LW), single hot-deck imputation (HS), single regression imputation (RS), hot-deck-based multiple imputation (HM), and regression-based multiple imputation (RM). These MDTs were assessed in the context of regression weight estimates in multiple regression analysis in complex sample data with two data levels. In this study, the multiple regression equation had six regressors without missing data and two regressors with missing data. The four performance measures used in this study were statistical bias, RMSE, CI width, and coverage probability (i.e., 95%) of the confidence interval.
The five MDTs were evaluated separately for three types of missingness: MCAR, MAR, and MNAR. For each type of missingness, the studied MDTs were evaluated at four levels of missingness (10%, 30%, 50%, and 70%) along with complete sample conditions as a reference point for interpretation of results. In addition, ICC levels (.0, .25, .50) and high and low density population were also manipulated as studied factors.
The study’s findings revealed that the performance of each individual MDT varied across missing data types, but their relative performance was quite similar for all missing data types except for LW’s performance in MNAR. RS produced the most inaccurate estimates considering bias, RMSE, and coverage of confidence interval; RM and HM were the second poorest performers. LW as well as HS procedure outperformed the rest on the measures of accuracy and precision in MCAR; however LW’s measures of precision decreased in MAR and MNAR, and LW’s CI width was the widest in MNAR data. In addition, in all three missing data types, those poor performers were less accurate and less precise on variables with missing data than they were on variables without missing data; and the degree of accuracy and precision of these poor performers depended mostly on the level of data ICC. The proportion of missing data only noticeably affected the performance of HM such that in higher missing data levels, HM yielded worse performance measures. Population density factor had negligible effects on most of the measures produced by all studied MDTs except for RMSE, CI width, and CI coverage produced by LW which were modestly influenced by population density.
6
Dunu, 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/.
Full textAbstract:
In comparing the means .of two normally distributed populations with unknown variance, two tests very often used are: the two independent sample and the paired sample t tests. There is a possible gain in the power of the significance test by using the paired sample design instead of the two independent samples design.
7
Bašić, 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.
Full text
8
Yang, Hanfang. "Jackknife Emperical Likelihood Method and its Applications." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/9.
Full textAbstract:
In this dissertation, we investigate jackknife empirical likelihood methods motivated by recent statistics research and other related fields. Computational intensity of empirical likelihood can be significantly reduced by using jackknife empirical likelihood methods without losing computational accuracy and stability. We demonstrate that proposed jackknife empirical likelihood methods are able to handle several challenging and open problems in terms of elegant asymptotic properties and accurate simulation result in finite samples. These interesting problems include ROC curves with missing data, the difference of two ROC curves in two dimensional correlated data, a novel inference for the partial AUC and the difference of two quantiles with one or two samples. In addition, empirical likelihood methodology can be successfully applied to the linear transformation model using adjusted estimation equations. The comprehensive simulation studies on coverage probabilities and average lengths for those topics demonstrate the proposed jackknife empirical likelihood methods have a good performance in finite samples under various settings. Moreover, some related and attractive real problems are studied to support our conclusions. In the end, we provide an extensive discussion about some interesting and feasible ideas based on our jackknife EL procedures for future studies.
9
Martins, Francisco T. R. França. "Take-two interactive software - risk of missing the growth wave." Master's thesis, 2017. http://hdl.handle.net/10362/25883.
Full text
10
Hung, 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.
Full text
11
Huang, 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.
Full textAbstract:
碩士國立成功大學
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.
12
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.
Full textAbstract:
碩士國立臺灣大學
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.
13
"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.
Full textAbstract:
abstract: Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of missing data, and the type(s) of data collected. If analysis models are used which a) do not accurately capture the structure of relationships in the data such as clustered/hierarchical data, b) do not allow or control for missing values present in the data, or c) do not accurately compensate for different data types such as categorical data, then the assumptions associated with the model have not been met and the results of the analysis may be inaccurate. In the presence of clustered/nested data, hierarchical linear modeling or multilevel modeling (MLM; Raudenbush & Bryk, 2002) has the ability to predict outcomes for each level of analysis and across multiple levels (accounting for relationships between levels) providing a significant advantage over single-level analyses. When multilevel data contain missingness, multilevel multiple imputation (MLMI) techniques may be used to model both the missingness and the clustered nature of the data. With categorical multilevel data with missingness, categorical MLMI must be used. Two such routines for MLMI with continuous and categorical data were explored with missing at random (MAR) data: a formal Bayesian imputation and analysis routine in JAGS (R/JAGS) and a common MLM procedure of imputation via Bayesian estimation in BLImP with frequentist analysis of the multilevel model in Mplus (BLImP/Mplus). Manipulated variables included interclass correlations, number of clusters, and the rate of missingness. Results showed that with continuous data, R/JAGS returned more accurate parameter estimates than BLImP/Mplus for almost all parameters of interest across levels of the manipulated variables. Both R/JAGS and BLImP/Mplus encountered convergence issues and returned inaccurate parameter estimates when imputing and analyzing dichotomous data. Follow-up studies showed that JAGS and BLImP returned similar imputed datasets but the choice of analysis software for MLM impacted the recovery of accurate parameter estimates. Implications of these findings and recommendations for further research will be discussed.Dissertation/Thesis
Doctoral Dissertation Educational Psychology 2016
14
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.
Full textAbstract:
碩士國立中興大學
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.
15
"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.
Full textAbstract:
Cheung Shu Fai."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.
16
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.
Full textAbstract:
碩士國立臺北大學
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.