Morteza Amini

Predicting future returns and modeling return uncertainty are essential yet challenging tasks in portfolio optimization. To address these issues, this study proposes a hybrid approach combining machine learning for return prediction, bootstrapping for uncertainty modeling, and scenario optimization for portfolio selection in the presence of uncertainty. Bootstrap samples are used to generate multiple return trajectories via machine learning, with each trajectory treated as a distinct scenario in a scenario-based mean-variance optimization framework. Empirical results using data from the Tehran Stock Market demonstrate that the proposed model produces optimal portfolios that align more closely with those derived from actual prices, compared to those generated by traditional techniques. Our results show that the combination of bootstrapping, machine learning-based prediction, and scenario optimization is a compelling alternative to classical methods. Indeed, by combining bootstrapping, as a non-parametric method for uncertainty quantification, with machine learning, the model avoids strong distributional assumptions (e.g., normality of returns) and works without assuming a predefined form of uncertainty set. In addition to illustrating the advantages of our approach by implementing it on real-world datasets, we theoretically prove that the resulting scenario optimization problem is a convex program that generates efficient solutions superior to those produced by worst-case robust optimization techniques. Furthermore, the developed framework can accommodate different machine learning models and bootstrapping techniques. Moreover, the use of scenario optimization is computationally tractable and aligns with even large-scale projects.

While the existing literature includes substantial numerical investigations into various shrinkage ridge and Liu estimators, it often lacks a cohesive approach to their construction. This gap signals a need for a unified construction methodology that can provide a clearer framework for understanding and applying Liu estimators in practice. By establishing such a methodology, we aim to simplify the utilization of these estimators and promote their adoption in various statistical applications. This paper will discuss the theoretical underpinnings of shrinkage learners with focus on the seemingly unrelated semiparametric regression model. Through this construction analysis, we ultimately aim to enhance the ongoing discourse in the field of shrinkage learners, offering valuable insights that support researchers and practitioners in choosing suitable techniques for their specific data challenges. Journal of Statistical Research 2025, Vol. 59, No. 1, pp. 131-143

In this note, we propose a parametric inferential procedure for predicting future order statistics, based on record values, which takes inter-record times into account. We utilize the additional information contained in inter-record times for predicting future order statistics on the basis of observed record values from an independent sample. The two parameter exponential distribution is assumed to be the underlying distribution.

This paper proposes a new nonlinear approach for additive functional regression with functional response based on kernel methods along with some slight reformulation and implementation of the linear regression and the spectral additive model. The latter methods have in common that the covariates and the response are represented in a basis and so, can only be applied when the response and the covariates belong to a Hilbert space, while the proposed method only uses the distances among data and thus can be applied to those situations where any of the covariates or the response is not Hilbertian typically normed or even metric spaces with a real vector structure. A comparison of these methods with other procedures readily available in R is performed in a simulation study and in real datasets showing the results the advantages of the nonlinear proposals and the small loss of efficiency when the simulation scenario is truly linear. The comparison is done in the Hilbertian case as it is the only scenario where all the procedures can be compared. Finally, the supplementary material provides a visualization tool for checking the linearity of the relationship between a single covariate and the response and a link to a GitHub repository where the code and data are available.} %and an example considering that the response is not Hilbertian.

Determining the predictor variables that have a non-linear effect as well as those that have a linear effect on the response variable is crucial in additive semi-parametric models. This issue has been extensively investigated by many researchers in the area of semi-parametric linear additive models, and various separation methods are proposed by the authors. A popular issue that might affect both estimation and separation results is the existence of outliers among the observations. In order to address this lack of sensitivity towards extreme observations, robust estimating approaches are frequently applied. We propose a robust method for simultaneously identifying the linear and nonlinear components of a semi-parametric linear additive model, even in the presence of outliers in the observations. Additionally, this model is sparse in that it may be used to determine which explanatory variables are ineffective by giving accurate zero estimates for their coefficients. To assess the effectiveness of the proposed method, a comprehensive Monte Carlo simulation study is conducted along with an application to investigate the dataset, which includes Boston property prices dataset.

This paper introduces the hhsmm R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. These models are flexible models with both Markovian and semi-Markovian states, which are applied to situations where the model involves absorbing or macro-states. The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction of future states and the residual useful lifetime estimation in the predict function. The commercial modular aero-propulsion system simulation (C-MAPSS) data-set is also included in the package, which is used for illustration of the application of the package features. The application of the hhsmm package to the analysis and prediction of the Spain's energy demand is also presented.