Bias Parameters Research Articles

Likelihood approach to dynamic panel models with interactive effects

This paper studies dynamic panel models with a factor error structure that is correlated with the regressors. Both short panels (small T) and long panels (large T) are considered. A dynamic panel forms a simultaneous-equation system, and under the factor error structure, there exist constraints between the mean and the covariance matrix. We explore the constraints through a quasi-FIML (full information maximum likelihood) approach. The quasi-FIML approach does not estimate individual effects, even if they are fixed constants, thus circumventing the incidental parameters problem in the cross-sectional dimension.The factor process is treated as parameters and it can have arbitrary dynamics. We show that there is no incidental parameters bias, for fixed or large T, and that the estimator is centered at zero even when scaled by the fast convergence rate of root-NT. We also study the efficiency of the quasi-FIML estimator. Finally, we develop a feasible and fast algorithm for computing the quasi-FIML estimators under interactive effects.

Adaptive linear inversion of Moho topography in the Tibetan Plateau by combining gravity and seismic data

The Tibetan Plateau is a region where the Indian and Asian-European plates collide. A high-precision Moho topography model is important for the study of plate motion and internal tectonics of the Tibetan Plateau. However, previous gravity-based models failed in representing adequately the Moho topography because crustal effects and biases in the inversion parameters were not fully considered. To address these issues, we extracted the gravity effect caused by the crust using wavelet multi-scale analysis, and used an adaptive linear inversion method with available gravity and seismic data to estimate more accurate inversion parameters. With these two improvements, we inverted for a high-precision Moho topography model of the Tibetan Plateau. The results show that the majority of the Tibetan Plateau has a Moho depth of 60–70 km. The Moho depth in the center of the Qaidam Basin is about 50 km, and the Moho topography between the east and west of the basin has obvious undulations. The Moho depth of the Tarim Basin ranges from 38–50 km. While the undulation of Moho is limited in the center of the basin, obvious uplifts are shown in northern and southern basin. The plate driving force between the Tibetan Plateau and the Tarim Basin may be the primary cause of this phenomenon. By comparing the difference with the seismic data, we found that the RMS of our model is 2.8 km smaller than that of the CRUST 1.0 model, which shows that our model is more accurate.

Galaxy bias renormalization group

The effective field theory of large-scale structure allows for a consistent perturbative bias expansion of the rest-frame galaxy density field. In this work, we present a systematic approach to renormalize galaxy bias parameters using a finite cutoff scale Λ. We derive the differential equations of the Wilson-Polchinski renormalization group that describe the evolution of the finite-scale bias parameters with Λ, analogous to the β-function running in QFT. We further provide the connection between the finite-cutoff scheme and the renormalization procedure for n-point functions that has been used as standard in the literature so far; some inconsistencies in the treatment of renormalized bias in current EFT analyses are pointed out as well. The fixed-cutoff scheme allows us to predict, in a principled way, the finite part of loop contributions which is due to perturbative modes and which, in the standard renormalization approach, is absorbed into counterterms. We expect that this will allow for the robust extraction of (a yet-to-be-determined amount of) additional cosmological information from galaxy clustering, both when using field-level techniques and n-point functions.

Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes

When a robot executes a task, it is necessary to model the relationships among its body, target objects, tools, and environment and to control its body to realize the target state. However, it is difficult to model a relationship using classical methods if it is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes of the model. In this study, we have developed the Deep Predictive Model with Parametric Bias (DPMPB) as a more human-like adaptive intelligence to deal with these modeling difficulties and temporal model changes. We categorize and summarize the theory of the DPMPB and various task experiments on the actual robots and discuss the effectiveness of the DPMPB.

Memory loss can prevent chaos in games dynamics.

Recent studies have raised concerns on the inevitability of chaos in congestion games with large learning rates. We further investigate this phenomenon by exploring the learning dynamics in simple two-resource congestion games, where a continuum of agents learns according to a simplified experience-weighted attraction algorithm. The model is characterized by three key parameters: a population intensity of choice (learning rate), a discount factor (recency bias or exploration parameter), and the cost function asymmetry. The intensity of choice captures agents' economic rationality in their tendency to approximately best respond to the other agent's behavior. The discount factor captures a type of memory loss of agents, where past outcomes matter exponentially less than the recent ones. Our main findings reveal that while increasing the intensity of choice destabilizes the system for any discount factor, whether the resulting dynamics remains predictable or becomes unpredictable and chaotic depends on both the memory loss and the cost asymmetry. As memory loss increases, the chaotic regime gives place to a periodic orbit of period 2 that is globally attracting except for a countable set of points that lead to the equilibrium. Therefore, memory loss can suppress chaotic behaviors. The results highlight the crucial role of memory loss in mitigating chaos and promoting predictable outcomes in congestion games, providing insights into designing control strategies in resource allocation systems susceptible to chaotic behaviors.

Constraining the mass and redshift evolution of the hydrostatic mass bias using the gas mass fraction in galaxy clusters

The gas mass fraction in galaxy clusters is a quantity which can be used as a robust cosmological probe. It is however subject to various effects from the baryonic physics inside galaxy clusters, which may bias the obtained cosmological constraints. Among different aspects of the baryonic physics, in these proceedings we focus on the impact of the hydrostatic equilibrium assumption. From X-ray measurements of cluster gas fraction in the Planck-ESZ sample, we analyse the hydrostatic mass bias B, constraining a possible mass and redshift evolution of this quantity and its impact on the cosmological constraints. We show a degeneracy between the redshift dependence of the bias and cosmological parameters. In particular we find a 3.8σ evidence for a redshift dependence of the bias when assuming a Planck prior on Ωm. On the other hand, assuming a constant mass bias would lead to the extreme large value of Ωm > 0.860. We however show these results to be depending on the mass and redshift selections inside the main sample. Nevertheless, in all the analyses we find a value for the amplitude of the bias that is consistent with B ∼ 0.8, as expected from hydrodynamical simulations and local measurements, but still in tension with the low value of B ∼ 0.6 derived from the combination of cosmic microwave background primary anisotropies with cluster number counts. We also discuss cosmological constraints obtained from gas fraction data, combined with other probes like cluster number counts.

Surface Parameter Bias Disturbance in Radar Backscattering From Bare Soil Surfaces

Surface Parameter Bias Disturbance in Radar Backscattering From Bare Soil Surfaces

내부표정요소 편의가 Multi-light Ray Intersection에서 지상 정확도에 미치는 영향과 인자분석

내부표정요소 편의가 Multi-light Ray Intersection에서 지상 정확도에 미치는 영향과 인자분석

Detectability of dense-environment effects on black-hole mergers: The scalar field case, higher-order ringdown modes, and parameter biases

Detectability of dense-environment effects on black-hole mergers: The scalar field case, higher-order ringdown modes, and parameter biases

Increasing the accuracy of electrostatic fields strength measurement by using an improved differential transimpedance amplifier circuit

An electrostatic field mill (EFM) is widely used to measure the strength of electrostatic fields, the main drawback of which is the occurrence of large measurement errors (up to 15 % in the range from 0 to 1 kV/m). This paper examines the aspects of using transimpedance amplifiers (TIAs) for the tasks of converting the current received from the EFM sensor into voltage, which will make it possible to reduce the instrumental error and ensure the linearity of the atmospheric electrostatic field strength measurement. In the general case, for the functional circuits of the electrostatic field mill, which include a differential transimpedance amplifier, there is the use of two TIA circuits, which are connected in parallel. Despite the simplicity of implementation, such a configuration contains a number of disadvantages and is not optimal. In the paper, a comparative analysis of a typical circuit of a differential TIA and a circuit of an ungrounded differential transimpedance amplifier with zero voltage drop proposed by the authors is carried out. As a result of the analysis, it was established that the designed authentic circuit of the ungrounded differential transimpedance amplifier with zero voltage drop has better parameters of linearity and interference resistance, in contrast to the generally accepted one. The value of the signal-to-noise ratio for the proposed scheme improved by 42 % on average compared to the typical one. The main difference of the proposed scheme is that the stability of the amplification factor is ensured, the influence of the bias parameters of the operational amplifier is leveled, and the overall noise level is reduced. The use of the designed scheme of an ungrounded differential transimpedance amplifier with zero voltage drop could make it possible to increase the accuracy of the measurement of the electrostatic field strength

Examining Antarctic sea ice bias sensitivity in the multi-variate parameter space using a global coupled climate modelling system

Coupled global numerical climate models (GCMs) typically underestimate mean Antarctic sea ice area and extent, particularly during the austral summer months, contributing to uncertainties in climate prediction. This study examines the climatological behaviour of Antarctic sea ice in a coupled GCM in the multivariate sea ice model parameter space. Individual parameters dominate the ice response in different seasons and regions, with a compensatory effect in some parameter combinations and an amplified effect in others; however, certain parameter combinations are found to improve aspects of Antarctic sea ice climatology well beyond the limitations of a univariate approach. For example, the disparity between observed and simulated summer sea ice extent and area is halved, and the tendency towards very low-concentration ice (<15 %) reduced in favour of a more compact summer and autumn ice pack. Regardless, clear limitations in the extent to which a coupled GCM can be calibrated with sea-ice model parameters also emerge. Relatively unconsolidated winter ice cover persists and, in some experiments, becomes looser still, exacerbating the already overestimated maximum sea ice extent. Furthermore, the seasonal evolution of sea ice and the exaggerated asymmetry of the seasonal cycle, with the onset of ice advance too slow and maximum sea ice reached too late, sees negligible improvements. We note that, even with the large gains under certain parameter combinations, bias and other deficiencies still remain. Using coupled data assimilation to optimise parameters in both sea-ice and ocean models will likely assist in contributing to further model improvements.

Effects of the Quantity and Magnitude of Cross-Loading and Model Specification on MIRT Item Parameter Recovery.

In real-world situations, multidimensional data may appear on large-scale tests or psychological surveys. The purpose of this study was to investigate the effects of the quantity and magnitude of cross-loadings and model specification on item parameter recovery in multidimensional Item Response Theory (MIRT) models, especially when the model was misspecified as a simple structure, ignoring the quantity and magnitude of cross-loading. A simulation study that replicated this scenario was designed to manipulate the variables that could potentially influence the precision of item parameter estimation in the MIRT models. Item parameters were estimated using marginal maximum likelihood, utilizing the expectation-maximization algorithms. A compensatory two-parameter logistic-MIRT model with two dimensions and dichotomous item-responses was used to simulate and calibrate the data for each combination of conditions across 500 replications. The results of this study indicated that ignoring the quantity and magnitude of cross-loading and model specification resulted in inaccurate and biased item discrimination parameter estimates. As the quantity and magnitude of cross-loading increased, the root mean square of error and bias estimates of item discrimination worsened.

A sensitivity analysis for temporal bias in cross-sectional mediation.

For over three decades, methodologists have cautioned against the use of cross-sectional mediation analyses because they yield biased parameter estimates. Yet, cross-sectional mediation models persist in practice and sometimes represent the only analytic option. We propose a sensitivity analysis procedure to encourage a more principled use of cross-sectional mediation analysis, drawing inspiration from Gollob and Reichardt (1987, 1991). The procedure is based on the two-wave longitudinal mediation model and uses phantom variables for the baseline data. After a researcher provides ranges of possible values for cross-lagged, autoregressive, and baseline Y and M correlations among the phantom and observed variables, they can use the sensitivity analysis to identify longitudinal conditions in which conclusions from a cross-sectional model would differ most from a longitudinal model. To support the procedure, we first show that differences in sign and effect size of the b-path occur most often when the cross-sectional effect size of the b-path is small and the cross-lagged and the autoregressive correlations are equal or similar in magnitude. We then apply the procedure to cross-sectional analyses from real studies and compare the sensitivity analysis results to actual results from a longitudinal mediation analysis. While no statistical procedure can replace longitudinal data, these examples demonstrate that the sensitivity analysis can recover the effect that was actually observed in the longitudinal data if provided with the correct input information. Implications of the routine application of sensitivity analysis to temporal bias are discussed. R code for the procedure is provided in the online supplementary materials. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Q-LEARNING, POLICY ITERATION AND ACTOR-CRITIC REINFORCEMENT LEARNING COMBINED WITH METAHEURISTIC ALGORITHMS IN SERVO SYSTEM CONTROL

This paper carries out the performance analysis of three control system structures and approaches, which combine Reinforcement Learning (RL) and Metaheuristic Algorithms (MAs) as representative optimization algorithms. In the first approach, the Gravitational Search Algorithm (GSA) is employed to initialize the parameters (weights and biases) of the Neural Networks (NNs) involved in Deep Q-Learning by replacing the traditional way of initializing the NNs based on random generated values. In the second approach, the Grey Wolf Optimizer (GWO) algorithm is employed to train the policy NN in Policy Iteration RL-based control. In the third approach, the GWO algorithm is employed as a critic in an Actor-Critic framework, and used to evaluate the performance of the actor NN. The goal of this paper is to analyze all three RL-based control approaches, aiming to determine which one represents the best fit for solving the proposed control optimization problem. The performance analysis is based on non-parametric statistical tests conducted on the data obtained from real-time experimental results specific to nonlinear servo system position control.

Bayesian posterior density estimation reveals degeneracy in three-dimensional multiple emitter localization

Single-molecule localization microscopy requires sparse activation of emitters to circumvent the diffraction limit. In densely labeled or thick samples, overlap of emitter images is inevitable. Single-molecule localization of these samples results in a biased parameter estimate with a wrong model of the number of emitters. On the other hand, multiple emitter fitting suffers from point spread function degeneracy, which increases model and parameter uncertainty. To better estimate the model, parameters and uncertainties, a three-dimensional Bayesian multiple emitter fitting algorithm was constructed using Reversible Jump Markov Chain Monte Carlo. It reconstructs the posterior density of both the model and the parameters, namely the three-dimensional position and photon intensity, of overlapping emitters. The ability of the algorithm to separate two emitters at varying distance was evaluated using an astigmatic point spread function. We found that for astigmatic imaging, the posterior distribution of the emitter positions is multimodal when emitters are within two times the in-focus standard deviation of the point spread function. This multimodality describes the ambiguity in position that astigmatism introduces in localization microscopy. Biplane imaging was also tested, proving capable of separating emitters up to 0.75 times the in-focus standard deviation of the point spread function while staying free of multimodality. The posteriors seen in astigmatic and biplane imaging demonstrate how the algorithm can identify point spread function degeneracy and evaluate imaging techniques for three-dimensional multiple-emitter fitting performance.

Better performance for right-skewed data using an alternative gamma model

BackgroundThe Maximum Likelihood Estimator (MLE) for parameters of the gamma distribution is commonly used to estimate models of right-skewed variables such as costs, hospital length of stay, and appointment wait times in Economics and Healthcare research. The common specification for this estimator assumes the variance is proportional to the square of the mean, which underlies estimation and specification tests. We present a specification in which the variance is directly proportional to the mean.MethodsWe used simulation experiments to investigate finite sample results, and we used United States Department of Veterans Affairs (VA) healthcare cost data as an empirical example comparing the fit and predictive ability of the models.ResultsSimulation showed the MLE based on a correctly specified alternative has less parameter bias, lower standard errors, and less skewness in distribution than a misspecified standard model. The application to VA healthcare cost data showed the alternative specification can have better R square, smaller root mean squared error, and smaller mean residuals within deciles of predicted values.ConclusionsThe alternative gamma specification can be a useful alternative to the standard specification for estimating models of right-skewed continuous variables.

Accurate and Intelligent Early Warning Method of Debris Flow Formation Based on IGWO-LSTM Algorithm

To improve the accuracy of debris flow forecasts and serve as disaster prevention and mitigation, an accurate and intelligent early warning method of debris flow initiation based on the IGWO-LSTM algorithm is proposed. First, the entropy method is employed to screen the early warning indicators. Then, the improved grey wolf algorithm (IGWO) is obtained by optimizing the grey wolf algorithm by combining elite reverse learning and adaptive convergence factors. Finally, the IGWO-LSTM algorithm is obtained by using IGWO to improve the total connection layer weight and bias parameters of LSTM, which takes the screened early warning indicators as input and outputs the early warning results of the debris flow formation risk level. In comparison with the methods introduced in earlier studies, the results demonstrate that the proposed method achieves superior outcomes in terms of assessing a single warning of multiple debris flow gullies, a multi-year warning of a single debris flow gully, and a multi-year warning of multiple debris flow gullies. The mean absolute error and root mean square error of the early warning results of the ANN model and PEEM method show low values, while the early warning hit rate shows high values, surpassing 90%. Also, the other two methods developed in the previous studies show low values of the early warning coverage rate, reaching 90% at most. Moreover, the triggered traffic model and MLPG method show high values in the early warning coverage rate, exceeding 90%, and low values in the early warning hit rate of less than 90%, and the average absolute error and root mean square error are high. On the other hand, the results of the proposed method show that the overall early warning hit rate is higher than 95%, the coverage rate is close to 100%, and the error is less than 1.5. Thus, the comprehensive analysis results show that the proposed method has better performance and higher reliability than other studied methods.

Kibria–Lukman estimator for the zero inflated negative binomial regression model: theory, simulation and applications

The zero inflated negative binomial model is an appropriate choice to model count response variables with excessive zeros and over-dispersion simultaneously. This article addresses the parameter estimation for the zero-inflated negative binomial model when there are many predictors and the problems of multicollinearity are present. Since, under multicollinearity, the widely used maximum likelihood estimator becomes unstable, this article proposes an alternative estimator, called Kibria–Lukman estimator for the zero inflated negative binomial model and provided some new biasing parameters. Then compared the performance of Kibria–Lukman estimator with some of the traditional biased estimators, that is, ridge and Liu estimators. The superiority of the proposed estimator is systematically scrutinized both theoretically and numerically. For numerical assessment, we conduct a Monte Carlo simulation study under different controlled factors. A numerical example is also applied to appraise the performance of proposed estimator. Based on the simulation and example results, we observed that the performance of the proposed estimator under different biasing parameters was better than that of the maximum likelihood estimator and other involved biased estimation methods when there exists a high but imperfect multicollinearity.

Latent Profile Transition Analysis with Random Intercepts (RI-LPTA)

The primary objective of this investigation is the formulation of random intercept latent profile transition analysis (RI-LPTA). Our simulation investigation suggests that the election between LPTA and RI-LPTA for examination has negligible impact on the estimation of transition probability parameters when the population parameters are generated based on the LPTA model. However, an estimation bias in the transition probabilities is observed in all simulated conditions when LPTA is used for analysis and the generated population parameters follow the RI-LPTA model. Researchers constructing empirical longitudinal models are advised to prioritize the use of RI-LPTA in model construction, regardless of whether LPTA exhibits random intercept effect. This measure helps to reduce the possible bias in the estimation parameters which may occur if the random intercept effect of LPTA is not taken into account during model specification.

Adverse effects of receiver clock bias and drift on NavIC user position coordinates and their mitigation

We investigated the effects of receiver clock bias and drift on NavIC receiver position coordinates at a low latitude station and proposed a mitigation technique. For this, Five weeks data of Accord IRNSS-GPS-SPS (AIGS) receiver are considered. Error in each position coordinates (X, Y and Z) is estimated and observed that the maximum error occurred in user position Z-coordinate. CEP for 2D position is analyzed and observed that the Circular Error Probability (CEP) values are high in Z coordinate of YZ and ZX planes. Further, it is noticed that the timing of sudden enhancement of both clock bias and drift parameters coincides with the periodic anomaly of the X, Y and Z coordinates. The error in individual position coordinates is minimized by applying 6th order Butterworth low pass filter resulting in improved position accuracy. If such errors are not mitigated, the position estimation of the receiver becomes highly erroneous.