Conventional Maximum Likelihood Research Articles

The GMM estimation of semiparametric spatial stochastic frontier models

In this paper, we consider the estimation of semiparametric spatial stochastic frontier (SF) models, in which the spatial dependence is modeled by adding the spatially lagged dependent variable as an additional independent variable (following the convention of spatial autoregressive models) and specifying various spatial structures on the inefficiency and/or idiosyncratic error. Thus, the proposed models are inclusive compared with most previous studies. Efficiency modeling in the SF literature has not given enough attention to the spatial interaction of inefficiency, primarily because the estimation of inefficiency in spatial models is challenging using the conventional maximum likelihood method. Therefore, we propose a two-step estimation procedure for our spatial frontier models under the framework of the generalized method of moments (GMM). The proposed method is easy to implement. Further, Monte Carlo Simulations show that our GMM estimators have good finite-sample performance. We apply our GMM estimators to examine the technical efficiency of 41 European countries and the effects of spatial dependence on production (GDP).

Accuracy enhancement in indoor positioning system based on visible light communication

The travelled signals that used to estimate the distances between LEDs and the target undergo from non-line-of-sight (NLOS) link in indoor positioning system (IPS) utilizing visible light communication (VLC) technology. This could present a significant errors in identifying their positions. In this paper, we design an IPS based on a new hybrid technique using VLC technology for accuracy enhancing. To begin, the target's position is determined using a weighted least squares positioning method. Next, a maximum likelihood positioning approach is used to relocate the target's position, starting with the estimated position as an initial point. Simulations present that the created algorithm performs better than weight least squares and conventional maximum likelihood methods.

Federated Edge Learning With Misaligned Over-the-Air Computation

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.

Non-Euclidean estimation of acoustic cross-spectral density matrices: Evaluation by matched-field localization in an ocean waveguide.

A set of non-Euclidean cross-spectral density matrix (CSDM) estimators based on the Frechet mean and median is applied to spatially localize an acoustic source in a stochastic shallow water environment driven by internal gravity waves. The resulting geometric-based matrix estimates are incorporated into matched-field processors defined through steering matrices using intrinsic distance measures between pairs of CSDMs on a Riemannian manifold for each replica source location. Their performance is evaluated using the probability of correct localization to determine any improvement in source localization over the conventional (maximum likelihood) statistical approach for CSDM estimation.

Analysis of Modified Kies Exponential Distribution with Constant Stress Partially Accelerated Life Tests under Type-II Censoring

This study investigates, for the first time, the product of spacing estimation of the modified Kies exponential distribution parameters as well as the acceleration factor using constant-stress partially accelerated life tests under the Type-II censoring scheme. Besides this approach, the conventional maximum likelihood method is also considered. The point estimates and the approximate confidence intervals of the unknown parameters are obtained using the two methods. In addition, two parametric bootstrap confidence intervals are discussed based on both estimation methods. Extensive simulation studies are conducted by considering different censoring schemes to examine the efficiency of each estimation method. Finally, two real data sets for oil breakdown times of insulating fluid and minority electron mobility are analyzed to show the applicability of the different methods. Moreover, the reliability function and the mean time-to-failure under the normal use condition are estimated using both methods. Based on Monte Carlo simulation outcomes and real data analysis, we recommend using the maximum product of spacing to evaluate both the point and interval estimates for the modified Kies exponential distribution parameters in the presence of constant-stress partially accelerated Type-II censored data.

RSS-Based Localization of Multiple Radio Transmitters via Blind Source Separation

This letter proposes a methodology for counting and locating the nodes of an uncooperative wireless network using power measurements collected by sensors. The approach is blind, allowing the detection and localization of the nodes without knowing the network&#x2019;s specific features (i.e., the number of nodes, modulation type, and medium access control (MAC)). Because the signals captured by the radio-frequency (RF) sensors are additively mixed, blind source separation (BSS) is used to separate transmitted power profiles. Then, received signal strength (RSS) is extracted from the reconstructed signals and localization is performed through conventional least square (LS) and maximum likelihood (ML) techniques. Numerical results reveal that the BSS-ML approach reaches a rather low localization error in mild shadowing regimes, even when the ratio between the number of RF sensors and nodes, <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>, is close to 1. Finally, it is shown how the performance degradation introduced by the imperfect BSS is slight and that the root mean square error (RMSE) approaches the Cram&#x00E9;r-Rao lower bound (CRLB) when increasing <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>.

Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application

The linear regression model becomes unsuitable when the response variable is expressed as percentages, proportions, and rates. The beta regression (BR) model is more appropriate for the variable of this form. The BR model uses the conventional maximum likelihood estimator (BML), and this estimator may not be efficient when the regressors are linearly dependent. The beta ridge estimator was suggested as an alternative to BML in the literature. In this study, we developed the Dawoud–Kibria estimator to handle multicollinearity in the BR model. The properties of the new estimator are derived. We compared the performance of the estimator with the existing estimators theoretically using the mean squared error criterion. A Monte Carlo simulation and a real-life application were carried out to show the benefits of the proposed estimator. The theoretical comparison, simulation, and real-life application results revealed the superiority of the proposed estimator.

Computationally Efficient Maximum Likelihood Channel Estimation for Coarsely Quantized Massive MIMO Systems

We consider computationally efficient maximum likelihood (ML) channel estimation for massive multiple-input multiple-output (MIMO) systems using coarsely quantized measurements obtained from low-resolution analog-to-digital converters (ADCs) at the receivers. We first devise a computationally efficient ML estimator (referred to as CQML) by using the cyclic optimization and majorization-minimization (MM) techniques. Then, we show the connections between CQML and the conventional unquantized ML estimator. Numerical examples are provided to demonstrate the effectiveness and computational efficiency of the proposed channel estimation algorithm.

Spiking Neural Networks-Inspired Signal Detection Based on Measured Body Channel Response

Spiking neural networks (SNNs) are inspired by biological behavior in the neural system processing information by the rate or delay components of discrete spiking signals in a massively parallel manner. Sparse and asynchronous spikes allow event-driven information processes, leading to low power consumption and fast inference. By exploiting these advantageous features of the SNNs, this paper presents a signal detection method for human body communication (HBC), which has recently emerged as an innovative alternative for wireless body area networks using the human body as a signal transmission medium. In particular, binary spike signaling in the SNNs is highly appropriate for application in the digital signal transmission-based HBC systems. The experiments of body channel response measurements using digital training signals show that the body channel characteristics vary with changes in body posture and device location, especially in wearable environments requiring small-sized devices powered by batteries. The proposed SNN structures can enhance communication performance from signal distortions, stemming from the effects of the time-dispersive body channel and bandwidth-limited receive-filter. The proposed SNN-based transmission symbol code detector (STD) can improve about 3.53 dB carrier-to-noise ratio (CNR) at a bit-error-rate (BER) of 10^-6 for a data rate of 1.3125 Mbps, compared to that of a conventional maximum likelihood detector. In addition, the proposed SNN-based preamble detector can secure an approximately 150 wider threshold range than that of a conventional correlator to achieve a detection probability higher than 99% of the frame existence at a CNR of approximately 0dB required for achieving a BER of 10^-6 by the STD.

Bayesian and Maximum-Likelihood Modeling and Higher-Level Scores of Interpersonal Problems With Circumplex Structure.

Difficulties in interpersonal behavior are often measured by the circumplex-based Inventory of Interpersonal Problems. Its eight scales can be represented by a three-factor structure with two circumplex factors, Dominance and Love, and a general problem factor, Distress. Bayesian confirmatory factor analysis is well-suited to evaluate the higher-level structure of interpersonal problems because circumplex loading priors allow for data-driven adjustments and a more flexible investigation of the ideal circumplex pattern than conventional maximum likelihood confirmatory factor analysis. Using a non-clinical sample from an online questionnaire study (N = 822), we replicated the three-factor structure of the IIP by maximum likelihood and Bayesian confirmatory factor analysis and found great proximity of the Bayesian loadings to perfect circumplexity. We found additional support for the validity of the three-factor model of the IIP by including external criteria-Agreeableness, Extraversion, and Neuroticism from the Big Five and subclinical grandiose narcissism-in the analysis. We also investigated higher-level scores for Dominance, Love, and Distress using traditional regression factor scores and weighted sum scores. We found excellent reliability (with Rtt ≥ 0.90) for Dominance, Love, and Distress for the two scoring methods. We found high congruence of the higher-level scores with the underlying factors and good circumplex properties of the scoring models. The correlational pattern with the external measures was in line with theoretical expectations and similar to the results from the factor analysis. We encourage the use of Bayesian modeling when dealing with circumplex structure and recommend the use of higher-level scores for interpersonal problems as parsimonious, reliable, and valid measures.

A robust method for the assessment of average bioequivalence in the presence of outliers and skewness.

In this paper, we propose a robust Bayesian method for the assessment of average bioequivalence based on data from conventional crossover studies. We evaluate and motivate empirically the need for robust methods in bioequivalence studies by comparing the results of robust and conventional statistical methods in a large data pool of bioequivalence studies. Robustness of the statistical methodology is achieved by replacing the normal distributions for residuals in the linear mixed model with skew-t distributions. In this way, the statistical model can accommodate skew and heavy-tailed data, particularly outliers, yielding robust statistical inference without the need for excluding outliers from the analysis. We performed a simulation study to investigate and compare the performance of the robust and conventional models. Our study shows that in some trials, the distribution of residuals is skew and heavy-tailed. In the presence of outliers, the 90% confidence intervals for the ratio of geometric means tend to be narrower for the robust methods than for the conventional method. Our simulation study shows that the robust method has suitable frequentist properties and yields more precise confidence intervals and higher statistical power than the conventional maximum likelihood method when outliers are present in the data. As a sensitivity analysis, we recommend the fit of robust models for handling outliers that are occasionally encountered in crossover design bioequivalence data.

Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics

The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.

CT Image Reconstruction Using NLMfuzzyCD Regularization Method.

To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used. The conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from optimal smoothing as the number of iterations increases. For solving this problem, this paper presents a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means of fuzzy complex diffusion as a regularization term for noise reduction and edge preservation. The proposed model was evaluated on four test cases phantoms. Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compared with the state-of-the-art techniques.

A novel JFG detection-decoding approach of a vertical shuffle scheduling for iterative MIMO receivers with NB-LDPC codes

MIMO systems exploit multi-path propagation in a rich broadcast environment to improve throughput and performance of digital communication systems. The correspondent demappers require additional computational complexity. In this paper, a new detection-decoding approach based on vertical shuffle scheduling is proposed. Space exploration for the maximum likelihood detection is reduced to a dynamic region that surrounds the received symbol. Thus, a dynamic approach is proposed to detect the closest point of the channel observation without calculating all the candidate symbols in a constellation. In addition, a novel method based on EXIT chart study is proposed to find the sufficient number of neighboring points to search around a local minimum and then compute by recursion their Euclidean distances. Compared to the conventional Maximum Likelihood detector, the proposed algorithm allows a reduction in the computational complexity, in terms of number of multiplication, reaching 98%. The simulation results show that the proposed algorithm outperforms the state-of-the-art algorithms like MMSE, sphere decoding, DBTC and DaVinci codes.

Predicting Individual Effects in Fixed Effects Panel Probit Models

Abstract Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization.

Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics.

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.

Frequency estimation of a single real-valued sinusoid: An invariant function approach

An invariant function approach for the computationally efficient (non-iterative and gridless) maximum likelihood (ML) estimation of unknown parameters is applied on the real-valued sinusoid frequency estimation problem. The main attraction point of the approach is its potential to yield a ML-like performance at a significantly reduced computational load with respect to conventional ML estimator that requires repeated evaluation of an objective function or numerical search routines. The numerical results indicate that the suggested estimator closely tracks the Cramer-Rao bound in the asymptotic region and performs very close to the ML estimator in other regions.

Maximum-likelihood direction of arrival estimation under intermittent jamming

Multiple-snapshot maximum-likelihood (ML) direction of arrival (DOA) estimation problem is studied for the intermittent jamming scenario. The intermittent jamming modality is based on the assumption that only a subset of the collected snapshots are contaminated by the jammer while the others are jammer-free; but the receiver does not know which is which. This type of jamming is frequently encountered in practice either inadvertently, say due to the sporadic activity of a non-hostile system; or intentionally, say due to the activity of an adversary sweeping the operational bandwidth of the receiver. Exact maximum likelihood solution for the problem is analytically intractable and an expectation maximization (EM) method based solution is developed for coherent and non-coherent signal models. Coherent signal model assumes that the phase difference between the coefficients of two consecutive snapshots are known a-priori which is an assumption compatible with the Swerling-1/3 target models in the radar signal processing literature. Non-coherent signal model does not have such an assumption and it is suitable for Swerling-2/4 targets. The suggested EM based solution is shown to yield an important estimation accuracy improvement over conventional maximum-likelihood solution which ignores the intermittency of jammer and also over the atomic norm based high resolution estimation techniques. Cramer-Rao type performance lower bounds for the problem is also provided to illustrate the efficacy of the suggested estimator.

Enhanced RSS-Based UAV Localization Via Trajectory and Multi-Base Stations

To improve the localization precision of unmanned aerial vehicle (UAV), a novel framework is established by jointly utilizing multiple measurements of received signal strength (RSS) from multiple base stations (BSs) and multiple points on trajectory. First, a joint maximum likelihood (ML) of exploiting both trajectory information and multi-BSs is proposed. To reduce its high complexity, two low-complexity localization methods are designed. The first method is from BS to trajectory (BST), called LCSL-BST. First, fixing the nth BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule. Finally, all computed positions of UAV for different BSs are combined to form the resulting position. The second method reverses the order, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) of the joint ML method. From simulation results, we can see that the proposed joint ML and separate LCSL-BST methods have made a significant improvement over conventional ML method without use of trajectory knowledge in terms of location performance. The former achieves the joint CRLB and the latter is of low-complexity.

A 1-D Search Approach for Single Sensor Passive Localization Based on Signal Periodicity

An efficient one-dimensional search solution is proposed for single-sensor emitter localization based on signal periodicity. By introducing an intermediate variable, this single-sensor localization problem can be solved by solving a set of equations for classic multi-sensor time difference of arrival localization, whose algebraic solutions can be obtained by the well-known two-stage weighted least squares (TSWLS) algorithm. In this manner, the two-dimensional or three-dimensional search for the emitter location can be replaced by a one-dimensional search for the intermediate variable. Simulations indicate that, at the expense of a small loss of localization accuracy, the proposed method achieves a significant advantage in computation time, compared with the conventional maximum likelihood technique.