Recursive Step Research Articles

Multiperiod Dynamic Portfolio Choice: When High Dimensionality Meets Return Predictability

Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state predictors. Specifically, in the first step, we propose the new Risk-Premium Projected-PCA (RP-PPCA) method to reduce the dimension of tradable assets. This method achieves Dimension Reduction (DR) by estimating latent factors with explanatory power in both time series variation and expected return in high-dimension-low-sample-size data. In the second step, we use dynamic programming to solve the multiperiod portfolio choice problem, and in each recursive step, we adopt an Adjusted semiparametric Model Averaging (AMA) method to avoid the curse of dimensionality associated with a large set of state variables while remaining computationally efficient. Thus, we name this two-step approach DRAMA, which stands for a combination of a new dimension reduction method and an adjusted semiparametric model averaging method. Analytically, we show that the portfolios constructed by the DRAMA are approximately optimal under mild assumptions. Moreover, our numerical results based on empirical data from US stock markets show that the proposed portfolios have excellent out-of-sample performances.

Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)-mass spectroscopy (MS) and flow injection analysis (FIA)-MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.

A Novel Approach to Sensor Placement: Recursive Exhaustive Search and Stochastic Optimization Parameter Impact Analysis

This study presents a comprehensive approach for single sensor placement optimization in two-dimensional and three-dimensional spaces. A traditional exhaustive search technique and a novel method called recursive exhaustive search are used to place a sensor in a way that maximizes the area coverage metric. Exhaustive search provides a baseline by methodically evaluating all potential placements, while recursive exhaustive search innovates by segmenting the search process into more manageable, recursive steps. Our findings highlight the significant impact of two key parameters, the number of evaluations and the rasterization value, on the achieved coverage and computation time. The results show that the right choice of parameters can significantly reduce the computational effort without compromising the quality of the solution. This underlines the critical need for a balanced approach that considers both computational complexity and placement efficacy. We show that exhaustive search is not feasible for three-dimensional environment models and propose to establish a modified exhaustive search method as a ground truth for the single sensor placement problem. We then explore nature-inspired genetic algorithms and the impact of the number of evaluations of the optimization function for these algorithms on both accuracy and computational cost.

Adaptive stabilization for a class of wave equations with uncertain nonlinear boundary actuator dynamics

This paper investigates the stabilization of a wave equation with uncertain boundary actuator dynamics. The remarkable characters of the system under investigation are revealed by the essential nonlinearities involved in the boundary actuator and the serious parametric unknowns coming from anti-damping coefficient at uncontrolled boundary and those in the actuator, which result into the incapability of the traditional control methods on this topic. For this, a novel adaptive control strategy is proposed in this paper. Specifically, a Riemann variables based state transformation is first introduced to change the original system into a new one whose stabilization implies that of the original system. Then, a state-feedback controller joint with some adaptive laws which are smartly chosen for the compensation of unknown parameters is explicitly designed by a finite-dimensional backstepping procedure. Owing to the compensation of boundary actuator dynamics, the performance analysis of the closed-loop system is more complicated than that of the related literature. For this, a rigorous recursive step is given to show the stability of the resulting closed-loop system. Finally, a numerical example is provided to verify the effectiveness of the proposed theoretical results.

A bias-correction modeling method of Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements

Modeling of Hammerstein–Wiener nonlinear systems has received a lot of attention in the signal processing community. However, all existing model identification methods may fail to provide a consistent parameter estimate for Errors-In-Variables (EIV) Hammerstein–Wiener systems, where both input–output data are contaminated by measurement white noises. In this paper, a bias-correction Least-Squares (LS) algorithm for consistent identification of EIV Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements is proposed. Firstly, the analytic expression for the estimated bias of the LS algorithm using noisy measurements for EIV Hammerstein–Wiener systems with polynomial nonlinearities is derived, which is caused by the correlation between the input–output signals and measurement noises. Secondly, a consistent estimation method for the bias-correcting term, including a recursive step and a cross-validation step based on the available noisy measurements only, is then proposed to estimate the unknown terms of noises variances and noise-free measurements in the estimated bias. The effectiveness of the proposed algorithm is demonstrated through a simulated example and a robot arm system.

Solving unconstrained binary polynomial programs with limited reach: Application to low autocorrelation binary sequences

Unconstrained Binary Polynomial Programs (UBPs) are a class of optimization problems relevant in a broad array of fields. In this paper, we examine an example from communication engineering, namely low autocorrelation binary sequences and propose a new dynamic programming approach that is particularly effective on UBP instances that have a limited so-called reach, which is a metric that states the maximum difference between any two variable indices across all monomials in the UBP. Based on the reach, the dynamic programming approach decomposes the problem into a number of overlapping stages that can be solved in parallel. On a set of publicly available low autocorrelation binary sequence problems, we demonstrate the superiority of the approach by showing that the method solves to optimality for the first time several previously unsolved instances. In particular, we provide a direct comparison between the proposed method and a modern version of a previously proposed dynamic program for UBPs. We give a detailed analysis of the connection between the two different algorithms and demonstrate that the advantage of the proposed dynamic program is in its ability to implicitly identify the multilinear polynomials that are required in the recursive steps of the two dynamic programs. For perspective, a comparison to several other methods is also provided.

Finite-Time Adaptive Neural Control for a Class of Nonlinear Systems With Asymmetric Time-Varying Full-State Constraints.

In this article, an adaptive finite-time tracking control scheme is developed for a category of uncertain nonlinear systems with asymmetric time-varying full-state constraints and actuator failures. First, in the control design process, the original constrained nonlinear system is transformed into an equivalent "unconstrained" one by using the uniform barrier function (UBF). Then, by introducing a new coordinate transformation and incorporating it into each recursive step of adaptive finite-time control design based on the backstepping technique, more general state constraints can be handled. In addition, since the nonlinear function in the system is unknown, neural network is employed to approximate it. Considering singularity, the virtual control signal is designed as a piecewise function to guarantee the performance of the system within a finite time. The developed finite-time control method ensures that all signals in the closed-loop system are bounded, and the output tracking error converges to a small neighborhood of the origin. At last, the simulation example illustrates the feasibility and superiority of the presented control method.

A Novel Adaptive Square Root UKF with Forgetting Factor for the Time-Variant Parameter Identification

The unscented Kalman filter (UKF) serves as an efficient estimator widely utilized for the recursive identification of parameters. However, the UKF is not well suited for tracking time-variant parameters. Moreover, the unscented transformation (UT) used in the UKF typically relies on Cholesky decomposition to perform the square root operation of the covariance matrix. This method necessitates the matrix to maintain symmetry and positive definiteness. Due to the adverse influence of rounding error and noise, it becomes challenging to guarantee the positive definiteness of the matrix in each recursive step for practical engineering. The square root UKF (SRUKF) eliminates the need for the square root operation in the UT by directly updating the square root of the covariance matrix during each recursion. However, the SRUKF still relies on the rank 1 update to the Cholesky factorization to perform the recursive process, which also necessitates the matrix to be positive definite. Furthermore, the SRUKF is ineffective in the identification of time-variant parameters. Therefore, this paper proposes a modification to the SRUKF that ensures unconditional numerical stability by utilizing QR decomposition. Subsequently, the modified square root UKF (MSRUKF) method is enhanced by incorporating an adaptive forgetting factor that can be adjusted based on the residual information from each recursive step. This adaptation leads to the development of the adaptive SRUKF with forgetting factor (ASRUKF-FF) method, which significantly improves the tracking capability for time-variant parameters. To validate the effectiveness of the proposed method, this paper demonstrates its application in identifying the time-variant stiffness and damping parameters of a three-story frame structure. In addition, the method is employed to estimate the time-variant stiffness of the bridge excited by vehicles. The simulation results show that the proposed method has the superiority of high accuracy, strong robustness, and widespread applicability, even with incomplete measurements and inappropriate parameter settings.

Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach

A novel method of estimating the silica (SiO 2 ) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to first calculate the minimum SiO 2 , water (H 2 O) and carbon dioxide (CO 2 ) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO 2 . In a recursive step, a ‘consensus’ SiO 2 is then established as the average between the two aforementioned SiO 2 estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S and Ti elemental concentrations + ‘consensus’ SiO 2 + reported trace element concentrations converted to wt% + ‘normative’ H 2 O + ‘normative’ CO 2 ) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H 2 O and CO 2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO 2 is linear, strong ( R 2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO 2 concentrations with a sparser, publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO 2 values measured on the ground over the conterminous USA. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO 2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to always validate results.

HBIM MODELLING FOR THE ARCHITECTURAL VALORISATION VIA A MAINTENANCE DIGITAL ECO-SYSTEM

Abstract. The present contribution falls within the framework of the actions envisaged by the "SMART Bethlehem – Sustainable Management And Renewal of Technology in the City of Bethlehem" (2022 – ongoing) research project, scientifically coordinated by the University of Pavia and co-founded by the Italian Agency for Cooperation and Development (AICS). Among the main objectives of the cooperation project is the sustainable urban and territorial development of the Bethlehem area by means of multi-scalar activities, mainly focusing on refurbishment and energy optimisation of a few relevant municipality buildings. A test case implementation of the HBIM methodology was then carried out on the Peace Center, an iconic building facing the "Manger Square" in close proximity to the "Nativity Church", aiming to validate the proposed systematisation of a scan-to-BIM approach focused on the development of an optimised data repository for the planned operation and maintenance activities. An ECO-Systemic workflow is presented, organised in five recursive steps. Starting from a SLAM-TLS integrated digital survey (3DS), the federated sub-models that comprise the overall project are geo-referenced (GEO) within a shared coordinate system (FSC) environment, thereby providing the basis for the subsequent architectural modelling of the built asset (ARC) and the associated enhancement of the level of information (LOI).

One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves

Summary This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two overall contributions of our work. First, our methods can be used to obtain simultaneous inference for treatment effects on multiple absolute risks in competing risk settings. Second, our methods can be used to achieve inference for the full survival curve, or a full absolute risk curve, across time. The one-step targeted maximum likelihood procedure is based on a one-dimensional universal least favourable submodel for each cause-specific hazard that we implement in recursive steps along a corresponding nonuniversal multivariate least favourable submodel. Our empirical study demonstrates the practical use of the methods.

Is recursive "mindreading" really an exception to limitations on recursive thinking?

The ability to mindread recursively-for example, by thinking what person 1 thinks person 2 thinks person 3 thinks-is a prime example of recursive thinking in which one process, representation, or idea becomes embedded within a similar one. It has also been suggested that mindreading is an exceptional example, with five recursive steps commonly observed for mindreading, in comparison with just one or two in other domains. However, conceptual analysis of existing recursive mindreading tasks suggests that conclusions about exceptional mindreading are insecure. Revised tasks were devised to provide a more rigorous test of recursive mindreading capacity. Study 1 (N = 76) found significantly worse performance at level-5 recursive mindreading on the revised tasks (17% correct) compared with the original tasks (80% correct), and no effect of moderate financial bonuses for good performance. Study 2 (N = 74) replicated poor performance at level-5 recursive mindreading on the revised tasks (15% correct) in the absence of bonuses, but found better performance (45% correct) when participants were offered large bonuses for accuracy, encouraged to take as much time as needed, and assisted with a strategy for recursive reasoning. These findings suggest that, like recursive thinking in other domains, recursive mindreading is effortful and limited. We discuss how the proposed role for high levels of recursive mindreading in communication, culture, and literature might be reconciled with these limitations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Nonsmooth Dynamic Tracking Control for Nonlinear Systems With Mismatched Disturbances: Algorithm and Practice

This article addresses a dynamic tracking control issue for a class of nonlinear systems subject to mismatched disturbances. As an alternative control design algorithm for practitioners, a simple nonsmooth tracking scheme with a self-tuning scaling gain is proposed under a nonrecursive synthesis framework. In reference to the existing related works, a main distinguishable feature is that the scaling gain of the proposed regulation law can be adaptively adjusted subject to different perturbation levels; therefore, the control system is able to achieve an improved transient-time control performance while an accurate tracking objective is guaranteed. In addition, the proposed nonrecursive design philosophy is able to obtain the simplest state-feedback expression of the controller through essentially detaching the stability analysis with controller design procedure, i.e., without going through recursive determination steps of classical virtual controllers. The efficacy of the proposed method is validated by an illustrative numerical example and a real-life application to a dc–dc boost converter system.

Adaptive fuzzy finite-time tracking control of nonlinear systems with unmodeled dynamics

This paper considers an adaptive fuzzy finite-time tracking control issue for uncertain nonlinear systems in the presence of unmodeled dynamics via the backstepping technique. In the design procedure, the traditional dynamic signal structure is improved firstly such that it is suitable for the controller design within finite-time interval universally for the controlled systems and it can be utilized to dominate the unmodeled dynamics. The dynamic disturbances are compensated by nonlinear damping terms. The fuzzy logic systems (FLSs) are used to package the unknown nonlinearities. By constructing the appropriate Lyapunov functions in recursive step and employing the finite-time stability theorem, the developed FLS-based adaptive tracking control strategy within finite-time interval, which ensures the boundedness of all the closed-loop signals and the convergence of tracking error. In the end, simulation results are provided to test the availability of the developed strategy.

The recursive nucleolus for partition function form games

To solve the problems about the emptiness and nonexistence of the recursive core (r-core) as introduced by Huang and Sjöström (2003), this paper considers a new recursive solution concept for partition function form games: the recursive nucleolus (r-nucleolus). In each recursive step, the prediction of a coalition about the partition of outsiders is consistent with the nucleolus in characteristic function form games. We show that the r-nucleolus is always nonempty, and it is a singleton in fully cohesive partition function form games. A sufficient condition is then provided to show that the r-nucleolus is included in the r-core. Additionally, some desirable properties that the r-nucleolus satisfies are presented. Moreover, we discuss applications of the r-nucleolus in Cournot oligopoly and Bertrand competition.

Autopraxeography: a method to step back from vulnerability

PurposeStudies on vulnerability in the workplace, although relevant, are rare because it is difficult to access. This article aims to focus on the benefits of using autopraxeography to study and step back from vulnerability at work.Design/methodology/approachAutopraxeography uses researchers' experience to build knowledge.FindingsAutopraxeography provides a better understanding of vulnerability and the opportunity to step back from the difficulties experienced. Instead of ignoring experiences related to vulnerability, this method makes it possible to transform them into new avenues of knowledge. Moreover, it enables researchers to step back from experiences of vulnerability, thus making them feel more secure.Originality/valueThe main differences from other self-studies stem from the epistemological paradigm in which this method is anchored: pragmatic constructivism. The most important difference is the production of generic knowledge in three recursive steps: writing in a naïve way, developing the epistemic work and building generic knowledge.

Adaptive Neural Network Finite-Time Control of Uncertain Fractional-Order Systems with Unknown Dead-Zone Fault via Command Filter

In this paper, the adaptive finite-time control problem for fractional-order systems with uncertainties and unknown dead-zone fault was studied by combining a fractional-order command filter, radial basis function neural network, and Nussbaum gain function technique. First, the fractional-order command filter-based backstepping control method is applied to avoid the computational complexity problem existing in the conventional recursive procedure, where the fractional-order command filter is introduced to obtain the filter signals and their fractional-order derivatives. Second, the radial basis function neural network is used to handle the uncertain nonlinear functions in the recursive design step. Third, the Nussbaum gain function technique is considered to handle the unknown control gain caused by the unknown dead-zone fault. Moreover, by introducing the compensating signal into the control law design, the virtual control law, adaptive laws, and the adaptive neural network finite-time control law are constructed to ensure that all signals associated with the closed-loop system are bounded in finite time and that the tracking error can converge to a small neighborhood of origin in finite time. Finally, the validity of the proposed control law is confirmed by providing simulation cases.

Improved multi-layer binary firefly algorithm for optimizing feature selection and classification of microarray data

Gene microarray technology can detect many gene expressions simultaneously, which is essential for disease diagnosis. However, microarray data are usually characterized by small samples and high dimensionality, which requires obtaining the most matching genes to the disease before building a classification model. In this paper, we propose an improved multilayer binary firefly-based method that is divided into two phases. The first stage reduces the dimensionality space by improved Max-Relevance and Min-Redundancy (mRMR). The second stage refines the feature space from coarse-grained level to fine-grained level by improved multilayer binary firefly algorithm (MBFA) at each recursive step. We built a stacking classifier model based on attention mechanism in publicly available gene microarray data and evaluated them compared to advanced hybrid feature selection methods. The experimental results show that compared with the classical chi square test, variance, logistic regression and decision tree methods, the proposed method can achieve higher classification accuracy with fewer features. In addition, the proposed method also shows excellent results in comparison with different published hybrid feature selection methods.

Modeling PM2.5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm

The machine learning algorithm based on multiple-input multiple-output linear regression models has been developed to describe PM2.5 and PM10 concentrations over time. The algorithm is fact-acting and allows for speedy forecasts without requiring demanding computational power. It is also simple enough that it can self-update by introducing a recursive step that utilizes newly measured values and forecasts to continue to improve itself. Starting from raw data, pre-processing methods have been used to verify the stationary data by employing the Dickey–Fuller test. For comparison, weekly and monthly decompositions have been achieved by using Savitzky–Golay polynomial filters. The presented algorithm is shown to have accuracies of 30% for PM2.5 and 26% for PM10 for a forecasting horizon of 24 h with a quarter-hourly data acquisition resolution, matching other results obtained using more computationally demanding approaches, such as neural networks. We show the feasibility of using multivariate linear regression (together with the small real-time computational costs for the training and testing procedures) to forecast particulate matter air pollutants and avoid environmental threats in real conditions.

Model‐guided boosting for image denoising

Boosting algorithms have demonstrated their effectiveness in improving the restoration quality of existing image denoising methods by extracting the residual signal or removing the noise leftover iteratively. Unlike existing boosting algorithms that focus on designing an ingenious recursive step by making use of the residual signal or the noise leftover, in this paper, we propose a novel model-guided boosting framework. Specifically, we derive the recursive step from an overall restoration model constructed with the technique of Regularization by Denoising (RED) towards an interpretable, extensible and flexible boosting mechanism. By using the RED, we can apply explicit regularization equipped with powerful image denoising engine to establish the global minimization problem, making the obtained model is clearly defined and well optimized. The framework enjoys the advantage of easily extending to the case of composite denoising via superadding a regularization term. As such, we develop a simultaneous model through the joint use of deep neural network and low-rank regularization to fully utilize both external and internal image properties. The resulting restoration models are capable of being flexibly solved with fixed-point strategy and steepest-descent method, leading to two types of denoising boosters. It is shown that the proposed schemes have promise results due to the improvement in signal-to-noise ratio of input signal, and are guaranteed to converge. Experiments verify the validity of the boosters for several denoising algorithms, and show that combining the power of internal and external denoising based on our framework achieves enhancement in denoising performance.