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This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications. [ABSTRACT FROM AUTHOR]

This research presents a model called the 'Gaussian negative binomial mixture with a latent class choice model', which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and latent class choice modeling to create an approach that captures the complexities of decision-making processes. We explain how the model is formulated and estimated, showcasing its effectiveness in analyzing and predicting choices in scenarios. Through the use of a dataset, we demonstrate the performance of this method, marking a significant advancement in choice modeling. Our results highlight the applications of this model and point towards promising directions for future research, especially in exploring symmetrical patterns and structures, within decision-making processes. [ABSTRACT FROM AUTHOR]

Missing data is a common problem in sample surveys and statisticians have recognized that statistical inference can be spoiled in the presence of non-response. Kadilar and Cingi built up a class of estimators for assessing the population mean under simple random sampling scheme when there are missing observations in the data set. This article firstly, proposes a class of estimators in light of Zaman and Bulut work, and after that defines another class of regression type estimators utilizing robust regression tools, robust variance–covariance matrices and supplementary information. The use of robust techniques in Zaman and Bulut ratio type estimators enable us to estimate the population mean in several cases of missing observations. The hypothetical mean square error equations are also derived for adapted and proposed estimators. These hypothetical discoveries are assessed by the numerical illustration, in support of present work. [ABSTRACT FROM AUTHOR]

Recently, Irfan et al. developed ratio-type estimators for population mean estimation with simple random sampling (SRS) scheme, based on known median of a study variable and an auxiliary (supplementary) variable. In this study, adapting the structure of Koyuncu, we propose a new class of exponential-type estimators, based on the known median of study variable with the SRS scheme. The theoretical mean square error (MSE) expressions of the new proposals up to the first order of approximation are determined. A numerical study is done in support of the present work by finding percentage gain in efficiency with the graphical presentation of MSE results. [ABSTRACT FROM AUTHOR]