Research ArticleOpen accessEvery word in the titleMeasuring Refugee’s and Host Community Vulnerability to Idiosyncratic and Covariate Causes of Poverty Shocks in UgandaJulius OkelloThe analysis reveals diverse patterns of change in poverty levels and inequality dynamics within different settlements. This study specifically provides insights into the impact of idiosyncratic and covariate poverty shocks on poverty and inequality across various refugee and host community settlements in Uganda. The data shows that in Bidi Bidi Settlement, poverty decreased by 1.7 units while inequality increased by … …American Journal of Economics and Business Administration, Vol. 16, Issue 1 · 16 Aug 2024 · 8,186 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleApplication of a Beta Regression Model for Covariate Adjusted ROCXing Meng, J.D. TubbsThe Receiver Operating Characteristic (ROC) curve and the area under the ROC (AUC) are widely used in determining the diagnostic capability of a binary classification procedure. Since the test performance is affected by covariates, the ROC and AUC have been utilized in a Generalized Linear Regression (GLM) setting. In this study, we revisit a problem where the AUC regression model … …Current Research in Biostatistics, Vol. 10, Issue 1 · 18 Jun 2020 · 13,966 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBayesian Models for Time Series with Covariates, Trend, Seasonality, Autoregression and OutliersPitsanu Tongkhow, Nantachai KantananthaBayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the … …Journal of Computer Science, Vol. 9, Issue 3 · 16 Apr 2013 · 6,653 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHIDDEN MARKOV MODELS WITH COVARIATES FOR ANALYSIS OF DEFECTIVE INDUSTRIAL MACHINE PARTSPornpit Sirima, Premysl PokornyMonthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM) in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a "defective" or "non-defective" state. The number of machine parts errors were collected … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 26 Jul 2014 · 6,859 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAdaptive Cross-Validation Under Concept Drift for Time Series ForecastingKunjira Kingphai, Prapakorn Kanjina, Kamol Sanittham, Wacharong WongsanurakTime-series forecasting often involves non-stationary data, making i.i.d. validation unreliable and fixed-window protocols vulnerable to leakage and biased error estimates. We propose Adaptive Time-Series Cross-Validation (ATSCV), a drift-aware evaluation framework that uses statistical change-point detection to partition each series into contiguous, approximately stationary regimes, followed by forward-chaining folds that respect those boundaries. By aligning train–validation splits with distributional changes (with … …Journal of Computer Science, Vol. 22, Issue 7 · 7 Jul 2026 · 1,488 accessesView PDFCite
Research ArticleOpen accessIn the keywordsCOX’S PROPORTIONAL HAZARD MODEL AND CONSTRUCTION OF LIFE TABLE FOR UNDER-FIVESalam Shantikumar SinghA primary data of 836 eligible women in the age group of 15-49 years is used to determine the causal effects of covariates on under-five mortality. The eight covariates viz., Number of family Members (NHM), Type of Toilet Facility (TTF), Total Children ever Born (TCB), Parity (PAR), Duration of Breastfeeding (DBF), use Contraceptive (CMT), DPT and Ideal Number of Girl … …Current Research in Biostatistics, Vol. 4, Issue 1 · 18 Jul 2014 · 8,266 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAN EFFECTIVE TECHNIQUE OF MULTIPLE IMPUTATION IN NONPARAMETRIC QUANTILE REGRESSIONYanan Hu, Qianqian Zhu, Maozai TianIn this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, which consists of initial imputation … …Journal of Mathematics and Statistics, Vol. 10, Issue 1 · 4 Jan 2014 · 6,813 accessesView PDFCite
Research ArticleOpen accessIn the abstractGait Analysis Combining Sports Biomechanics and ImagingChao ZhuAiming at the problems of marker positioning error, soft tissue interference, and insufficient personalized modeling ability in traditional gait analysis, this study proposes a gait assessment method that integrates sports biomechanics and multimodal imaging analysis. Firstly, based on the public OU-ISIR gait database, a Convolutional Neural Network (CNN) is used to extract the key gait features in the human silhouette … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 2 · 20 Jul 2026 · 850 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluation of Subset Matching Methods: Evidence from a Monte Carlo Simulation StudyLateef Amusa, Temesgen Zewotir, Delia NorthIn the absence or infeasibility of experiments, matching methods have increasingly been used in making causal claims using observational data. This paper conducts a Monte Carlo simulation study, based on a household panel survey, to compare the performance of some widely used subset matching methods. The methods include the propensity score caliper matching, Mahalanobis distance matching, and coarsened exact matching. … …American Journal of Applied Sciences, Vol. 16, Issue 3 · 11 Apr 2019 · 7,740 accessesView PDFCite
Research ArticleOpen accessIn the abstractGeneral Linear Models in a Missing Outcome Environment of Clinical Trials Incorporating with Splines for Time-Invariant Continuous AdjustmentMinjeong Park, Dejian Lai, Xianglin L. Du et al.Missing data is a common occurrence in longitudinal studies of health care research. Although many studies have shown the potential usefulness of current missing analyses, e.g., (1) Complete Case (CC) analysis; (2) imputation methods such as Last Observation Carried Forward (LOCF), multiple imputations, Expectation-Maximization algorithm approach; and (3) methods using all available data such as linear mixed model and generalized … …Current Research in Biostatistics, Vol. 5, Issue 1 · 18 Aug 2015 · 7,401 accessesView PDFCite