Model Assisted Statistics and Applications - Volume 12, issue 3

Authors: Lipovetsky, Stan | Kim, Jong-Min

Article Type: Other

DOI: 10.3233/MAS-170404

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 193-194, 2017

Authors: Kim, Jong-Min | Lee, Kee-Jae | Kim, Wonkuk

Article Type: Research Article

Abstract: In this paper, we consider variance estimation of the sample mean when the missing data have been imputed with multivariate imputation methods. Modern multivariate imputation methods to missing data are complicated and computationally expensive. These multivariate imputation methods do not require the normality assumption to impute the missing values. Under this assumption free condition, we compare the performance of variance estimation of six modern multivariate imputation methods including copula imputation, random forest imputation, principal component analysis imputation, and k-nearest neighbors imputation methods in complex sampling designs such as stratified sampling, cluster sampling and resampling approach to variance estimation by jackknife …and bootstrap methods in stratified sampling. We conducted simulation studies using National Health and Nutrition Survey data considering 5% and 15% missing completely at random (MCAR) rates. Based on our 500 times resampling simulation study of the mean squares errors of the sample mean in complex survey designs, the percent relative efficiency (RE(%)) of the random forest (RF) imputation method appears to outperform other imputation methods overall when the data has high skewness at the 5% missing rate and when the data has high excessive kurtosis at the 15% missing rate whereas the principal component analysis (PCA) imputation method appears to outperform other imputation methods when the data has high skewness at the 5% and 15% missing rates. Especially, the RE(%) of the multivariate imputation methods appears to be efficient in the cluster sampling design when the data has high skewness or excessive kurtosis at the 15% missing rate. Show more

Keywords: Missing at random (MAR), copula imputation, jackknife, bootstrap

DOI: 10.3233/MAS-170394

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 195-207, 2017

Authors: Sohn, Insuk | Kim, Sujong | Lee, Jae Won | Koo, Ja-Yong | Ko, Junsu

Article Type: Research Article

Abstract: NF-kB is a transcription factor (TF) which is an important mediator of immune responses. To understand human immune systems and related diseases, identifying novel NF-kB-responsive genes in the human genome is important. For this purpose, we developed a Discrete Kernel Structured Support Vector Machine (DSSVM) and applied this method to the promoters of 58 known NF-kB-target genes to find characteristic patterns of transcription factor binding sites (TFBSs) in their promoters. We searched these TFBS patterns against the promoters of 6440 genes previously identified in the human genome and identified 80 genes with the similar TFBS patterns in their promoters. Among …them, 21 genes are well known NF-kB-target genes and 49 are putative NF-kB target genes associated with the immune system or inflammatory response. We confirmed that the selected genes function as NF-kB-target genes from a biological point of view. Our method of assigning functions to genes, based on distinctive patterns of TFBSs in their promoters, could be applicable to identification of, not only genes associated with a specific function, but genes with unique expression patterns. Show more

Keywords: Structured Support Vector Machine, NF-kB, transcription factor binding site, transcription factor

DOI: 10.3233/MAS-170395

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 209-216, 2017

Authors: Shim, Jooyong | Hwang, Changha

Article Type: Research Article

Abstract: Quantile regression models with errors in variables have received a great deal of attention in the social and natural sciences. Some efforts have been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose a kernel-based orthogonal quantile regression model that effectively considers the errors on both input and response variables. We also provide a generalized cross validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed models. The proposed method is evaluated through simulations.

Keywords: Errors-in-variables, generalized cross validation, kernel, measurement error, orthogonal residual, quantile regression, support vector machine, support vector quantile regression

DOI: 10.3233/MAS-170396

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 217-226, 2017

Authors: Lee, Hye-Seung | Krischer, Jeffrey P.

Article Type: Research Article

Abstract: When prediction is a goal, validation utilizing data outside of the prediction effort is desirable. Typically, data is split into two parts: one for a development and one for validation. But this approach becomes less attractive when predicting uncommon events, as it substantially reduces power. When predicting uncommon events within a large prospective cohort study, we propose the use of a nested case-control design, which is an alternative to the full cohort analysis. By including all cases but only a subset of the non-cases, this design is expected to produce a result similar to the full cohort analysis. In our …framework, variable selection is conducted and a prediction model is fit on those selected variables in the case-control cohort. Then, the fraction of true negative predictions (specificity) of the fitted prediction model in the case-control cohort is compared to that in the rest of the cohort (non-cases) for validation. In addition, we propose an iterative variable selection using random forest for missing data imputation, as well as a strategy for a valid classification. Our framework is illustrated with an application featuring high-dimensional variable selection in a large prospective cohort study. Show more

Keywords: Nested case-control, high dimensional variable selection, validation, penalized regression, random forest imputation

DOI: 10.3233/MAS-170397

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 227-237, 2017

Authors: Kim, Jong-Min | Ryu, Jea-Bok | Lee, Seung-Joo | Jun, Sunghae

Article Type: Research Article

Abstract: Technology analysis is important work in management of technology. Most companies make plans for research and development (R&D) policy, new product development, or technological innovation using the results of technology analysis. In this paper, we propose a methodology of technology analysis using penalized regression models. We analyze the patent keywords extracted from the patent documents using ridge regression, least absolute shrinkage and selection operator, elastic net, and random forest. In addition, to show how our research could be applied to real problem efficiently, we carry out a case study of Apple technology. Our study contributes to perform R&D planning in …technology management. Show more

Keywords: Technology analysis, patent data analysis, zero-inflated problem, zero-inflated poisson model, zero-inflated negative binomial model, apple patent

DOI: 10.3233/MAS-170398

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 239-244, 2017

Authors: Park, Soyeon | Kim, Wonkuk

Article Type: Research Article

Abstract: Multifactor dimensionality reduction (MDR) method is a machine learning algorithm to detect nonlinear interactions. This multifactor dimensionality reduction analysis is a combination of factor selection by classification accuracy, model selection by prediction accuracy and cross-validation consistency of classification accuracy, and statistical significance by the permutation testing. In this paper, we compare the performances of the standard multifactor dimensionality reduction method and a modified method in which the best model is selected by the area under receiver operating characteristic curve and cross-validation consistency of the area under the receiver operating characteristic curve. We conducted simulation studies based on 1,000 replicates per …each parameter setting. The proposed MDR shows 1–8% increase of power to detect nonlinear interactions while false discovery rates remain the same as the original MDR. As an illustration, we applied these methods to pharmacogenomics data of antiepileptic drugs. Show more

Keywords: AUC, epistasis, MDR, multifactorial disease, ROC curve

DOI: 10.3233/MAS-170399

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 245-253, 2017

Authors: Mitra, Dipankar | Paul, Ranjit Kumar

Article Type: Research Article

Abstract: Agricultural price forecasting has become a promising area of research in recent times. ARIMA model has been most widely used technique during the last few decades for this purpose. When the assumption of homoscedastic error variance is violated then ARCH/GARCH models are applied in order to capture the changes in the conditional variance of the time-series data. The ANN approach can also be applied in the field of forecasting of real time-series data successfully as an alternative to the traditional forecasting models. Real-world time-series data are rarely pure linear or nonlinear in nature, sometimes contain both the pattern together. In …this situation a hybrid approach of combining the forecasts from a linear time-series model (ARIMA) and from a nonlinear time-series model (GARCH, ANN) has the better forecasting performance. The hybrid methodology namely ARIMA-GARCH and ARIMA-ANN have been applied for modelling and forecasting of wholesale potato price in Agra market of India. A comparative assessment has also been made in terms of Mean absolute percentage error (MAPE) and Root mean square error (RMSE) among the hybrid and their individual counterpart as far as forecasting is concerned. It is observed that ARIMA-ANN hybrid model outperforms the other combinations and individual counterpart for the data under consideration. R software package has been used for the data analysis. Show more

Keywords: ARIMA, ANN, GARCH, hybrid model, potato price

DOI: 10.3233/MAS-170400

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 255-264, 2017

Authors: Choi, Yoonha | Babiarz, Joshua | Tom, Ed | Kennedy, Giulia C. | Huang, Jing

Article Type: Research Article

Abstract: BACKGROUND AND OBJECTIVES : Kinship coefficients measure relatedness between two individuals and have wide usage in genetic applications. In this study, we repurpose the kinship coefficient to directly facilitate sample tracking to identify potential sample swaps. Such sample integrity metrics are particularly important for the following two scenarios in large-scale clinical studies: First, multiple biological samples from the same individual were routinely processed as unique samples or technical replicates. Querying the relatedness of genomic data of two samples can identify sample swaps prior to inappropriate inclusion in data analysis. In the second scenario, different biological analytes from the same samples …were run across multiple platforms and it is critical to establish the correct mapping for each individual sample, linking genomic information derived from multiple platforms to the same sample. For both cases, all downstream inferences rely on such correct mapping. Kinship coefficients can directly measure the mapping accuracy and ensure the required sample integrity. MATERIALS AND METHODS: We first describe the general concept of kinship coefficients and focus on the novel adaptations on feature (i.e. variants and/or SNPs) selection utilizing expressed variants to make it suitable for the clinical setting. RESULTS: We illustrate the adapted kinship coefficients estimate in two studies: one for lung fibrosis where multiple samples were routinely collected from each patient and one for thyroid cancers where a cohort of samples was run on different platforms. CONCLUSION: We demonstrate the effectiveness of using kinship coefficients to improve sample integrity and discuss potential improvements in the methodology. Show more

Keywords: Kinship, sample integrity, clinical, next generation sequencing

DOI: 10.3233/MAS-170401

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 265-273, 2017

Authors: Bogie, Kath | Xu, Yifan | Ma, Junheng | Zhang, Adah | Wang, Yuanyuan | Zanotti, Kristine | Sun, Jiayang

Article Type: Research Article

Abstract: Ovarian cancer (OvCa) is the fifth leading cause of cancer deaths in women and remains the deadliest gynecological cancer. Our study goal is to examine associations between diagnostic patterns and OvCa stages. We used the data from a web-based survey in which more than 500 women diagnosed with OvCa provided both free text responses and staging information. We employed text mining and natural language processing (NPL) to extract information on clinical diagnostic characteristics, together with 21 dichotomous symptomatic variables, patient-centered advocacy, and polytomous disease severity, with internal validation. We conducted multivariate analyses and developed tree-based classification models with the confirmation …of Random Forest to determine important factors in the relationships of the clinical diagnostic characteristics with OvCa stages. Models including the symptoms, patient advocacy tendency, disease severity and doctors’ responses as predictors, had a much better predictive power than those limited to doctors’ responses alone, indicating that OvCa stage at diagnosis depends on more than just doctors’ responses. Although effective early stage diagnosis and treatment remains a challenge, our analysis of patient-centered clinical diagnostic characteristics and symptoms shows that self-advocacy is essential for all women. The frontline physician is critically important in ensuring effective follow-up and timely treatment before diagnosis. Show more

Keywords: Ovarian cancer, diagnosis, survey, follow-up, multivariate analysis, text mining, data mining, tree-based classification, random forest, structural and non-structural missing data, patient advocacy, symptoms, doctor’s responses

DOI: 10.3233/MAS-170402

Citation: Model Assisted Statistics and Applications, vol. 12, no. 3, pp. 275-285, 2017