Multiple Model Estimator Research Articles

The Effect of Corporate Social Responsibility on Financial Performance of Large Manufacturing Firms in Addis Ababa, Ethiopia

Abstract
 Corporate Social Responsibility (CSR) is a concurrent issue in the world. It is the integration of social, economic and environmental aspects in business operations. The main aim of the investigation was to examine the effect of CSR practice on financial performance of large manufacturing firms in Addis Ababa, Ethiopia. In the study, to measure CSR, the dimensions such as, employee, customer, supplier, community, government and environmental-related practices of firms were used. Financial performance of large manufacturing firms was measured using ROA. Additionally, firm size was employed as a control variable to clearly understand the effect of CSR on ROA. The research was utilized explanatory research design and a quantitative research approach. The data obtained from 81 large manufacturing companies in Addis Ababa, Ethiopia. Stratified sampling method was applied to select the sample form 102 large manufacturing firms. Questionnaire and document analysis of audited financial statements served as data collection instruments. The study used multiple regression analysis model and Ordinary Least Square (OLS) parameters estimator. Based on the regression result, employee, community and government related CSR practice and firm size positively and significantly affect ROA. Suppliers’ related CSR practice has negative relationships and significant effect on ROA. Customers and environment related CSR activities have an insignificant effect on ROA. In general, large manufacturing firms are recommended to apply CSR initiatives to increase their productivity.
 Key Words: CSR, Financial Performance, ROA, Large Manufacturing Firms

The Interacting Multiple Model Smooth Variable Structure Filter for Trajectory Prediction

An autonomous vehicle would benefit from being able to predict trajectories of other vehicles in its vicinity for improved safety. In order for the self-driving car to plan safe trajectories, paths of nearby vehicles are required to be predicted for risk assessment, decision making, and motion planning. In this study, a trajectory prediction algorithm based on the Interacting Multiple Model (IMM) estimation strategy is proposed to predict paths involving lane-changing, lane-keeping, and turning motion. More specifically, the Interacting Multiple Model estimation technique is used with models defined in curvi-linear coordinates to predict a vehicle’s trajectory based on prior behavioral maneuvers. The road geometry is used to help facilitate behavior identification and prediction. Moreover, the combination of a more recently developed estimation technique known as the Generalized Variable Boundary Layer-Smooth Variable Structure Filter and the Interacting Multiple Model Estimator is applied to track, identify behaviors, and predict trajectories of a vehicle. The performance of this technique is compared with a Kalman Filter based formulation using synthetic and experimental data. This model-based strategy is also compared with machine learning-based strategies for trajectory prediction.

Neural activity and network analysis for understanding reasoning using the matrix reasoning task.

Reasoning requires the ability to manipulate mental representations and understand relationships between objects. There is a paucity of research regarding the functional connections between multiple brain areas that may interact during commonly used reasoning tasks. The present study aimed to examine functional activation and connectivity of frontoparietal regions during a Matrix Decision Making Task, completed by twenty-one right-handed healthy participants while undergoing fMRI. Voxel-wise whole brain analysis of neural response to the task revealed activation spanning dorsal and lateral prefrontal, occipital, and parietal regions. Utilizing Group Iterative Multiple Model Estimation, a data-driven approach that estimates the presence and direction of connectivity between specific ROIs, connectivity between prefrontal and sensory processing regions were revealed. Moreover, the magnitude of connectivity strength between the left precentral gyrus and left dorsal cingulate (dACC) was positively correlated with MR behavioral performance. Taken together, results are consistent with earlier work demonstrating involvement of regions comprising the central executive network in relational reasoning. These data expand existing knowledge regarding communication of key brain regions during the task and demonstrate that understanding how key brain regions are interconnected can effectively predict the quality of behavioral output.

FACTORS THAT AFFECT FIRM VALUE IN MANUFACTURING FIRMS

This research aims to obtain empirical evidence from the effect of profitability, leverage, liquidity, firm size, and activity ratio on firm value. Totals of 198 observational data originated from 66 manufacturing companies listed on Indonesia Stock Exchange (IDX) throughout 2018-2020. The sampling technique used in this research is purposive sampling. The hypothesis testing method in this research uses the multiple linear regression model. The multiple linear regression model estimation that suits this research is Fixed Effect Model (FEM). The statistical tool used for data processing in this research is EViews 12. This research uses Tobin's Q as a parameter to measure firm value. Based on the outcome of the processed data, profitability, leverage, and firm size all have significant effects on firm value, while liquidity and activity ratio both have insignificant effect on firm value.

FACTORS THAT AFFECT FIRM VALUE IN MANUFACTURING FIRMS

This research aims to obtain empirical evidence from the effect of profitability, leverage, liquidity, firm size, and activity ratio on firm value. Totals of 198 observational data originated from 66 manufacturing companies listed on Indonesia Stock Exchange (IDX) throughout 2018-2020. The sampling technique used in this research is purposive sampling. The hypothesis testing method in this research uses the multiple linear regression model. The multiple linear regression model estimation that suits this research is Fixed Effect Model (FEM). The statistical tool used for data processing in this research is EViews 12. This research uses Tobin's Q as a parameter to measure firm value. Based on the outcome of the processed data, profitability, leverage, and firm size all have significant effects on firm value, while liquidity and activity ratio both have insignificant effect on firm value.

The Influence of Participatory Technology and Institutional Participation of Farmer Groups on Farmer Business Development

Janti Village, Jogoroto, Jombang has a corn productivity of 2020 reaching 84.5 Kw/Ha, this achievement exceeds national average corn productivity. However, this is inversely proportional to the unstable price of corn, which can even be far from the normal price. Besides, the lack of farmer participation makes the problem drag on longer. The existence of farmer group institutions is very important to solve this problem. In addition, the application of participatory technology to farmers also provides clear goals for future agricultural production activities. Therefore, it is necessary to be active and participate in the Janti Village farmer group members. The purpose of this study is to describe the influence of participatory technology (discussions, workshops, and action planning) and institutional participation of farmer groups (participation in planning, implementation, utilization of results, and evaluation) on farming development (social, economic, and environmental) in Janti Village. Jogoroto District, Jombang Regency. The research method uses descriptive quantitative data collection techniques namely interviews, observation, and questionnaires. Previously, the questionnaire had to be tested for validity and reliability tests to support the validity of the research data obtained. The sampling method used is a simple random sample. Then the data analysis used is multiple linear regression analysis using the ordinary least square method including data tabulation, estimation of multiple linear regression models, classical assumption tests, regression feasibility tests, and interpretation of multiple linear regression models. Results showed that there was no significant effect if independent variables moved independently on dependent variables. Meanwhile, if independent variables move simultaneously, there will be a significant influence between participatory technology and the institutional participation of farmer groups in the development of farming businesses.

Switch-Constrained Multiple-Model Algorithm for Maneuvering Target Tracking

In most of the existing state estimation algorithms for maneuvering target tracking, the formulation of the mode switching process fails to accurately capture the deterministic law that the target mode will persist for a period of time once it turns to be in effect. In this paper, two strategies are presented to describe this characteristic. First, a constraint that model switching occurs no more than once over three consecutive time steps is proposed to provide a deterministic description that mode does not switch continuously. Second, considering target mode always lasts for some time while mode switching is completed instantly, the transition probabilities from one model to itself are fixed at extremely large values to enhance the certainty in mode sojourn segments. Based on these strategies, two recursive multiple model (MM) filters are derived in the framework of generalized pseudo-Bayesian (GPB) estimation and interacting multiple model (IMM) estimation, respectively. In both algorithms, although the large probability of model staying unchanged causes high peak errors at mode switching instants, the introduction of the switching constraint prevents the input to the mode-matched filter from being affected by the model transition probability, thereby obtaining a near-optimal performance in mode sojourn segments. Additionally, in order to reduce the peak estimation errors, a mode switching detector based on likelihood function is presented to work in parallel with the recursive MM filters. Once the detector declares that a mode switch occurs, the final estimate is adjusted from the output of the recursive MM filters to the estimate of the corresponding elemental filter, performance of which is guaranteed by the switching constraint. Simulation results demonstrate the capability of the proposed recursive MM filters to obtain performance close to that of a filter without model uncertainty in mode sojourn segments and the effectiveness of the mode switching detector to reduce peak errors at the same time.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework—such as the vector autoregression (VAR)—have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system’s dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes.

Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.

Daily Gender and Cognition: A Person-Specific Behavioral Network Analysis

Gender is person-specific, and it influences and is influenced by a breadth of multidimensional psychological factors, including cognition. Directionality is important for research on gender and cognition, as debate surrounds, for instance, whether masculine self-concepts precede spatial skills, or whether the reverse is true. In order to provide novel insights into the individualized nature of these relations, a person-specific network approach devised by Peter Molenaar and the first author – group iterative multiple model estimation for multiple solutions (GIMME-MS) – was applied to 75-day intensive longitudinal data on gender self-concept (i.e., femininity-masculinity, instrumentality, and expressivity) and cognition (i.e., mental rotations and verbal recall) from 103 young adults. GIMME-MS estimates individualized networks that contain same-day and next-day directed relations, prioritizing relations common across participants. It is ideal for analyzing behavioral time series with unclear directionality, as it generates multiple solutions from which an optimal one is selected. GIMME-MS revealed notable heterogeneity in the presence, direction, and nature of relations from gender self-concept to cognition (∼26% of participants) and vice versa (∼21% of participants). Findings are wholly novel in revealing the person-specific nature of gender and its cognitive dynamics, yet somehow, unsurprising given the revolutionary corpus of Peter Molenaar.

A Static Reduced-Order Multiple-Model Adaptive Estimator for Noise Identification

A reduced-order multiple-model (MM) estimator for noise identification in dynamic stochastic systems is developed. The unknown noise statistics are assumed to be static. While a standard static MM estimator does not grow exponentially over time, its computational complexity grows exponentially with the number of modes. The proposed algorithm reduces the computational complexity of MM estimation from exponential to polynomial by constructing a significantly smaller set of mode models which are updated every time step. It is assumed that the constructed mode models do not change significantly between time steps, which in turn holds if the smoothness of the mode probabilities is guaranteed. It is shown that in the case where there is “enough” statistical distinction between the noise modes, the proposed reduced-order MM estimator converges to the standard MM estimator. The proposed reduced-order MM estimator is evaluated using Monte Carlo simulations, showing that it performs nearly similar to the standard MM estimator with a fraction of its complexity. The numerical example shows less than a 2% increase in the root mean-squared errors (RMSEs) of the reduced-order MM estimator from the standard one, while the reduction in the number of filters in the reduced-order MM estimator is 300%. To further validate the proposed filter, experimental results are presented of an unmanned aerial vehicle (UAV) navigating with terrestrial signals of opportunity. Opportunistic navigation serves a relevant application for MM-based estimation, as system parameters, namely the statistics of the clock error dynamics of opportunistic sources, are unknown and must be adaptively estimated. The experimental results show a UAV navigating for more than 5 minutes over a trajectory of more than 3 km, achieving a final position error of 6.21 m obtained using the standard MM estimator versus a final position error of 6.25 m obtained using the proposed reduced-order MM estimator. A standard extended Kalman filter (EKF) was implemented for comparative analysis, showing a final error of 40.03 m. In the experiments, the reduced-order MM estimator was implemented with 16 filters, while the standard MM was implemented with 256 filters.

The influence of autoregressive relation strength and search strategy on directionality recovery in group iterative multiple model estimation.

Unified structural equation modeling (uSEM) implemented in the group iterative multiple model estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: (a) estimating autoregressive relations by default in the null model (GIMME-AR) and (b) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best-and were roughly equivalent in performance-when autoregressive relations were strong (e.g., β = .60). When they were weak (β ≤ .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state functional MRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies can disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific scenarios in which researchers might or might not be confident in directionality results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Shrinkage estimation of multiple threshold factor models

This paper proposes a multiple threshold factor model to enhance the flexibility in modeling the underlying regime switching mechanism for high dimensional time series. The factor loadings are assumed to switch between different regimes according to the value of a threshold variable. A novel estimation procedure is proposed to consistently estimate the multiple thresholds with the aid of sorting operation, principal component analysis and shrinkage estimation, which is practically easy-to-implement and computationally efficient. Furthermore, asymptotic properties for the multiple threshold estimators are established, together with other theoretical results. Monte Carlo simulations demonstrate that the procedure works well in finite samples. The U.S. data sets are analyzed with the proposed model to illustrate the threshold effect of economy policy uncertainty on the financial market.

Suspect resistance, police use of force, and officer injuries in a post-Floyd era: an analysis of two large police departments

ABSTRACT While the events following the death of Michael Brown in Ferguson, Missouri, undoubtedly brought about anti-police sentiment, the death of George Floyd may have brought about even greater hostility towards police. Utilizing data from two large police departments, we test whether there were significant increases in the incidence of civilian resistance, Black civilian resistance, use of force, and officer injuries. Estimation of multiple interrupted time-series models revealed that except for officer injuries in one agency, all outcomes significantly increased post-Floyd. Implications for policy and practice are discussed.

Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation.

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sequential Spatiotemporal Bias Compensation and Data Fusion for Maneuvering Target Tracking

In multisensor tracking systems, bias compensation is essential for accurate data fusion. It is challenging to obtain correct fusion results in the presence of both spatial and temporal biases, as well as target maneuver. In this article, a sequential spatiotemporal bias compensation and data fusion method is proposed for maneuvering target tracking with asynchronous multisensor measurements. The spatiotemporal biases of the sensors are augmented into the state vector to be estimated. The state equations are presented to formulate the nearly coordinated turn motion and the nearly constant acceleration motion, respectively, without exactly known measurement interval. In each target motion, the measurements are formulated as functions of both spatiotemporal bias and target state based on the time difference between the measurements and the states to be estimated. Furthermore, the interacting multiple model estimator is incorporated with the unscented Kalman filter to achieve simultaneous sequential estimation of spatiotemporal biases and target states in the presence of target maneuvers. Finally, the posterior Cramer–Rao lower bound for spatiotemporal bias and state estimation is provided. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.

Interacting Multiple Model Filter with a Maximum Correntropy Criterion for GPS Navigation Processing

In order to deal with the uncertainty of measurement noise, particularly for outlier types of multipath interference and non-line of sight (NLOS) reception, this paper proposes a novel method for processing the navigation states of the Global Positioning System (GPS) that combines the maximum correntropy criterion (MCC) and the interacting multiple model (IMM), with an extended Kalman Filter (EKF). Multipath mitigation is essential for increased positioning accuracy since multipath interference is one of the primary sources of errors. Nonlinear filtering with IMM configuration uses filter structural adaptation. In processing time-varying satellite signal standards for GPS navigation, it offers an alternative for creating the adaptive filter. A collection of switching models built on a method of multiple model estimation can be used to characterize the uncertainty of the noise. Even though most noise in real life is non-Gaussian, time-varying, and of fluctuating strength, the standard EKF operates effectively when the noise is Gaussian. The performance of EKF will drastically decline if the signals appear non-Gaussian. The underlying system disrupted by heavy-tailed, non-Gaussian impulsive sounds could be better since the EKF employs second-order statistical information. The MCC is a method for comparing two random variables based on higher-order signal statistics. The maximum correntropy-extended Kalman filter (MCEKF), which uses the MCC rather than the minimal mean square error (MMSE) as the optimization criterion, is used to enhance performance in non-Gaussian situations. Finally, a performance evaluation will be conducted to compare the effectiveness of the suggested strategy in improving positioning to alternative system designs.

Estimation for a Feedback System With a Desired Final State and Intermittent Stochastic Inputs

The system considered in this article operates with a feedback that is characterized by a gain and a desired final state (DFS), which is the main parameter of interest in this article. The system is, however, subjected intermittently to stochastic inputs according to a Markov process. Since the system operates in two modes—under the feedback to the DFS and under a stochastic input—an interacting multiple model estimator is designed to handle the unknown DFS to be estimated (mode <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{1}$</tex-math></inline-formula> ) and the random inputs (mode <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{2}$</tex-math></inline-formula> ). Simulation results explore several scenarios and investigate the degree of observability of this stochastic problem

Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks

This study investigates methods for autonomous navigation of a deep-space spacecraft where one-way radiometric and on-board optical information are fused to create a fully informed state estimate. The specific focus is on using filter bank methods (i.e., Multiple Model Estimation [MME] and Mixture of Experts [MoE]) to detect when measurement and/or dynamical mis-modeling occurs. We develop a new χ2-based gating network for a filter bank that may be used to identify poorly performing filters (i.e., those with low weights), which may be used as a signal for mis-modeling in the system. In addition to defining and deriving this new weighting scheme, numerical simulations based on NASA’s InSight mission demonstrate this new algorithm’s performance with and without measurement and dynamical mis-modeling present.

Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar.

Multi-target tracking (MTT) generally needs either a Doppler radar network with spatially separated receivers or a single radar equipped with costly phased array antennas. However, Doppler radar networks have high computational complexity, attributed to the multiple receivers in the network. Moreover, array signal processing techniques for phased array radar also increase the computational burden on the processing unit. To resolve this issue, this paper investigates the problem of the detection and tracking of multiple targets in a three-dimensional (3D) Cartesian space based on range and 3D velocity measurements extracted from dual-orthogonal baseline interferometric radar. The contribution of this paper is twofold. First, a nonlinear 3D velocity measurement function, defining the relationship between the state of the target and 3D velocity measurements, is derived. Based on this measurement function, the design of the proposed algorithm includes the global nearest neighbor (GNN) technique for data association, an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation, and a rule-based M/N logic for track management. Second, Monte Carlo simulation results for different multi-target scenarios are presented to demonstrate the performance of the algorithm in terms of track accuracy, computational complexity, and IMM mean model probabilities.