Goodness-of-fit tests are commonly used to assess the adequacy of multinomial logistic regression models. This study proposes a hybrid > goodness-of-fit test that combines covariate-space partitioning with grouping based on predicted probabilities. However, the adherence of the proposed hybrid test statistic to its hypothesized null distribution has not yet been established. Thus, this study evaluates its adherence to the hypothesized null distribution for three different numbers of groups, \(G=8, G=10\), and \(G=12\). A simulation study was conducted using Ward's hierarchical clustering technique with the Euclidean distance measure, sample sizes of 100 and 400, and various covariate distributions, including uniform, normal, and chi-square distributions. The test was evaluated based on empirical rejection rates and sample means compared with the theoretical means of the corresponding chi-square distributions, given by \((G-2)(c-1)\). The simulation results indicate that the three choices of group numbers ( \(G=8,10\), and 12) show similar patterns, with rejection rates generally close to the nominal 5% significance level and mean teststatistic values generally close to their theoretical expectations. These findings suggest that the choice among \(G=8, G=10\), and \(G=12\) has little effect on the null distribution of the hybrid test statistic.
Principal component analysis (PCA) is a widely used multivariate statistical technique for reducing the dimensionality of large datasets while retaining most of the original information. In this study, PCA was applied to identify the underlying socioeconomic components of household characteristics using data from 368 randomly selected households in Villanueva, Misamis Oriental, drawn from a population of 8,293 households. The suitability of the dataset was assessed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Re- Bartlett's test of sphericity prior to analysis. The overall KMO value of 0.827 and a significant Bartlett's test ( \(\chi^{2}=1457, p
Post-baccalaureate guidance has a lasting impact on learners’ trajectories. To address this challenge, the multinomial logistic regression model developed by previous authors recommends a post-baccalaureate track based on academic grades and transversal competencies, with high accuracy. Despite this performance, the RLM has three structural limitations: socio-contextual blindness, the absence of predictive uncertainty quantification, and temporal rigidity. To overcome them, this article proposes a Contextual Bayesian Model (MBC) that enriches the input vector with five socio-educational determinants (family income, parents’ socio-professional category and educational attainment, geographic area, digital access), treats the parameters as Gaussian random variables conditioned on context, introduces a confidence index based on Shannon entropy, and enables sequential -learning updates. Evaluated under conditions identical to those of the multinomial logistic regression model (RLM), on the same synthetic dataset, the MBC markedly improves accuracy and stability, eliminates cases of high predictive uncertainty, and its superiority is confirmed by a paired t-test. A first empirical validation on 209 candidates admitted to ESATIC, restricted to a binary SM/SP choice, confirms its operation beyond the synthetic setting. To our knowledge, it constitutes the first contextual Bayesian extension of an AI-based post-baccalaureate guidance system in the Ivorian context.
The interval-valued analysis has several inherent drawbacks, including the use of the uniform distribution over the interval, neglecting the degree of indeterminacy when data are recorded under uncertainty, and its convergence to the special case of classical statistics. Neutrosophic analysis, also known as generalized interval statistics, extends the framework of interval-valued analysis and is employed in this study for constructing the control chart. The degree of indeterminacy for the interval-valued data is calculated, and the uniform, beta, and log-logistic distributions are fitted to this degree, with the mean of these distributions used in the plotting statistic. From the analysis, it is evident that the uniform distribution does not fit well to the degree of indeterminacy. It is also clear from the analysis that simulation using interval-valued analysis cannot be recommended since in this case, the control chart results do not converge to those of the Shewhart control chart when uncertainty is absent. We also find that simulations based on the uniform distribution do not fully capture the uncertainty in the recorded interval-valued data and provide incomplete information about the process. From the analysis based on the average run length and power of the control chart, it is again evident that both the uniform distribution and interval-valued analysis cannot be recommended due to several drawbacks. Based on the findings, the neutrosophic analysis, which is a generalization and a more informative and flexible alternative to the interval-valued analysis, is recommended for control charting using interval-valued data.
Our aim is to classify gender using machine learning by analyzing user behavior on the Internet. With approximately 65% of the global population online, understanding users their preferences has become necessary. Online experiences are shaped by behavioral analysis, which enables platforms to tailor content by highlighting user interests and filtering out irrelevant information. Gender identification plays a significant role in user profiling for optimizing search results, recommending products or services, detecting fake connections, and improving social network suggestions. This study investigates user feedback on information-searching preferences and practices, treating them as indicators of online behavior, and trains a Ridge classifier to predict gender. The model achieves 70% accuracy on test data, demonstrating measurable differences in how males and females seek and disseminate information online.
