We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude improvements in prediction accuracy on real data...

Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional settings, where the problem dimension can grow with and possibly exceed the sample size. We consider the problem of robust estimation of sparse functionals, and provide a computationally and statistically efficient algor...

an analysis of the stages of crop growth presents an important step in the improvement of production management. through growth analysis, planning for planting systems, fertilization, pruning operations, harvest time as well as obtaining economical yield can be more accessible. therefore, a development of mathematical models simulating plant growth would be helpful in improving crop management,...

Fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. Several methods for evaluating fuzzy coefficients in linear regression models have been proposed. The first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. In this the...

the kinetic study of methylene blue (mb) adsorption using acid-activated spent tea (aast) as an adsorbent from aqueous solution with the aim of comparing linear and non-linear regression analysis methods was performed at varying initial mb concentrations (10-100 mg/l). hence, spent tea leaves, which were activated using concentrated sulfuric acid, were prepared. the physicochemical characterist...

The general linear model (glm) provides a general framework for a large set of models whose common goal is to explain or predict a quantitative dependent variable by a set of independent variables which can be categorical of quantitative. The glm encompasses techniques such as Student’s t test, simple and multiple linear regression, analysis of variance, and covariance analysis. The glm is adeq...

introduction: time series models are generally categorized as a data-driven method or mathematically-based method. these models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. on the other hand, a better understanding of the river flow process is vital for appropri...

in this paper, we consider an  i.e., multiple step-stress accelerated life testing (alt) experiment with unequal duration of time . it is assumed that the time to failure of a product follows rayleigh distribution with a log-linear relationship between stress and lifetime and also we assume a generalized khamis-higgins model for the effect of changing stress levels. taking into account that the...

this paper studies the hybrid flow shop scheduling where the optimization criterion is the minimization of total tardiness. first, the problem is formulated as a mixed integer linear programming model. then, to solve large problem sizes, an artificial immune algorithm hybridized with a simple local search in form of simulated annealing is proposed. two experiments are carried out to evaluate th...

Citations are increasingly used for research evaluations. It is therefore important to identify factors affecting citation scores that are unrelated to scholarly quality or usefulness so that these can be taken into account. Regression is the most powerful statistical technique to identify these factors and hence it is important to identify the best regression strategy for citation data. Citati...