Aggregating preferences in multi-issue domains by using maximum likelihood estimators

The change point identification has played a vital role in process improvement for an attribute process. This identification is able to effectively help process personnel to quickly determine the corresponding root causes and significantly improve the underlying process. Although many studies have focused on identifying the change point of a process, a generic identification approach has not been developed. The typical maximum likelihood estimator (MLE) approach has limitations: particularly, the known prior process distribution and mathematical difficulties. These deficiencies are commonly encountered in practice. Accordingly, this study proposes an artificial neural network (ANN) mechanism to overcome the difficulties of typical MLE approach in determining the change point of an attribute process. Specifically, the performance among the statistical process control (SPC) chart alone, the typical MLE approach, and the proposed ANN mechanism are investigated for the following cases: (1) a known attribute process distribution with the associated MLE being available to be used, (2) an unknown attribute process distribution with the MLE being unable to be used, and (3) an unknown attribute process distribution with the MLE being misused. The superior results and the performance of the proposed approach are reported and discussed.

In food microbial measurements, when most or very often bacterial counts are below to the limit of quantification (LOQ) or the limit of detection (LOD) in collected food samples, they are either ignored or a specified value is substituted. The consequence of this approach is that it may lead to the over or underestimation of quantitative results. A maximum likelihood estimation (MLE) or Bayesian models can be applied to deal with this kind of censored data. Recently, in food microbiology, an MLE that deals with censored results by fitting a parametric distribution has been introduced. However, the MLE approach has limited practical application in food microbiology as practical tools for implementing MLE statistical methods are limited. We therefore developed a user-friendly MLE tool (called “Microbial-MLE Tool”), which can be easily used without requiring complex mathematical knowledge of MLE but the tool is designated to adjust log-normal distributions to observed counts, and illustrated how this method may be implemented for food microbial censored data using an Excel spreadsheet. In addition, we used two case studies based on food microbial laboratory measurements to illustrate the use of the tool. We believe that the Microbial-MLE tool provides an accessible and comprehensible means for performing MLE in food microbiology and it will also be of help to improve the outcome of quantitative microbial risk assessment (MRA).

The calibration of transfer functions is essential for accurate pavement performance predictions in the Pavement-ME design. Several studies have used the least square approach to calibrate these transfer functions. Least square is a widely used simplistic approach based on certain assumptions. Literature shows that these least square approach assumptions may not apply to the non-normal distributions. This study introduces a new methodology for calibrating the transverse cracking and international roughness index (IRI) models in rigid pavements using maximum likelihood estimation (MLE). Synthetic data for transverse cracking, with and without variability, are generated to illustrate the applicability of MLE using different known probability distributions (exponential, gamma, log-normal, and negative binomial). The approach uses measured data from the Michigan Department of Transportation's (MDOT) pavement management system (PMS) database for 70 jointed plain concrete pavement (JPCP) sections to calibrate and validate transfer functions. The MLE approach is combined with resampling techniques to improve the robustness of calibration coefficients. The results show that the MLE transverse cracking model using the gamma distribution consistently outperforms the least square for synthetic and observed data. For observed data, MLE estimates of parameters produced lower SSE and bias than least squares (e.g., for the transverse cracking model, the SSE values are 3.98 vs 4.02, and the bias values are 0.00 and -0.41). Although negative binomial distribution is the most suitable fit for the IRI model for MLE, the least square results are slightly better than MLE. The bias values are -0.312 and 0.000 for the MLE and least square methods. Overall, the findings indicate that MLE is a robust method for calibration, especially for non-normally distributed data such as transverse cracking.

This study presents a new six-DOF force/torque sensor and its calibration method. This calibration method apply the method of so-called maximum likelihood estimation (MLE). MLE is utilized to determine and identify the parameters related to applied torques/forces towards resulted deformations at varied locations of the sensor structure. Formulating such relations in a vector–matrix form, those parameters are captured as coefficients in a matrix related to torques/forces towards angular/linear deformations in different directions. In addition to applying MLE, finite element modeling and analysis are conducted to generate realistic-like empirical data for the afore-mentioned calibration computation. The matrix formed by these coefficients can predict exactly forces and torques by this sensor based on deformations detected by strain gauges. In simulated results the worst sum of error for three forces is less than 0.04% while the worst sum of error for three torques is less than 0.005%. Experiments are also conducted, and the results show that the designed sensor and maximum-likelihood parameter estimation approach achieve favorable performance of predicting forces and torques with error less than 1%. The developed sensor is suitable for real-time sensing of multi-dimensional interactive forces/torques in a robot arm.

Lu and Boutilier proposed a novel approach based on "minimax regret" to use classical score based voting rules in the setting where preferences can be any partial (instead of complete) orders over the set of alternatives. We show here that such an approach is vulnerable to a new kind of manipulation which was not present in the classical (where preferences are complete orders) world of voting. We call this attack "manipulative elicitation." More specifically, it may be possible to (partially) elicit the preferences of the agents in a way that makes some distinguished alternative win the election who may not be a winner if we elicit every preference completely. More alarmingly, we show that the related computational task is polynomial time solvable for a large class of voting rules which includes all scoring rules, maximin, Copeland α for every α ∈ [0,1], simplified Bucklin voting rules, etc. We then show that introducing a parameter per pair of alternatives which specifies the minimum number of partial preferences where this pair of alternatives must be comparable makes the related computational task of manipulative elicitation NP-complete for all common voting rules including a class of scoring rules which includes the plurality, k-approval, k-veto, veto, and Borda voting rules, maximin, Copeland α for every α ∈ [0,1], and simplified Bucklin voting rules. Hence, in this work, we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it.

Abstract. Longitudinal developmental research is often focused on patterns of change or growth across different (sub)groups of individuals. Particular to some research contexts, developmental inquiries may involve one or more (sub)groups that are small in nature and therefore difficult to properly capture through statistical analysis. The current study explores the lower-bound limits of subsample sizes in a multiple group latent growth modeling by means of a simulation study. We particularly focus on how the maximum likelihood (ML) and Bayesian estimation approaches differ when (sub)sample sizes are small. The results show that Bayesian estimation resolves computational issues that occur with ML estimation and that the addition of prior information can be the key to detect a difference between groups when sample and effect sizes are expected to be limited. The acquisition of prior information with respect to the smaller group is especially influential in this context.

This paper investigates the performance of multiple approaches, including maximum likelihood estimation (MLE), for determining the Doppler frequency of radar returns from clutter scatterers. Fields backscattered from clutter are modeled as a complex stationary random process with a specified temporal correlation function received in the presence of additive thermal noise at a specified clutter-to-noise ratio. The MLE implementation is formulated, and its results are then compared through a simulation study to those obtained from traditional pulse-pair processing and other fast Fourier transform based methods in terms of error statistics and computational complexity. The methods are also compared for measurements of sea clutter from an X -band radar system. The results of the study show that the MLE approach has modest advantages over other methods in certain conditions, but that the performance gains obtained may often be insufficient to justify the extra computational cost or a-priori knowledge requirements of the method.

Scatterometry is frequently used as a non-imaging indirect optical method to reconstruct the critical dimensions (CD) of periodic nanostructures. A particular promising direction is EUV scatterometry with wavelengths in the range of 13 - 14 nm. The conventional approach to determine CDs is the minimization of a least squares function (LSQ). In this paper, we introduce an alternative method based on the maximum likelihood estimation (MLE) that determines the statistical error model parameters directly from measurement data. By using simulation data, we show that the MLE method is able to correct the systematic errors present in LSQ results and improves the accuracy of scatterometry. In a second step, the MLE approach is applied to measurement data from both extreme ultraviolet (EUV) and deep ultraviolet (DUV) scatterometry. Using MLE removes the systematic disagreement of EUV with other methods such as scanning electron microscopy and gives consistent results for DUV.

Estimation of the source to an epidemic-like spreading has important applications such as rooting out a computer virus in the Internet or rumor spreading in an online social network. Given a snapshot observation of the nodes in the network possessing the malicious information, how to identify the source of the spreading? This problem was first formulated in the seminal work by Shah and Zaman (TIT 2011) as a maximum likelihood estimation problem for a degree-regular graph. In this paper, we provide a probabilistic characterization to the rumor boundary of the observed graph. This leads to a new probabilistic approach of maximum likelihood estimation for a general tree graph that enables a distributed message-passing algorithm. We also evaluate the performance of the message-passing algorithm for finding the rumor center in general graphs numerically.

Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentation focuses on an alternative approach to modeling ordinal categorical data. The technique, based on orthogonal polynomials, provides a much simpler method of model fitting than the conventional approach of maximum likelihood estimation, as it does not require iterative calculations nor the fitting and re-fitting to search for the best model. Another advantage is that quadratic and higher order effects can readily be included, in contrast to conventional log-linear models which incorporate linear terms only.The focus of the discussion is the application of the new parameter estimation technique to multi-way contingency tables with at least one ordered variable. This will also be done by considering singly and doubly ordered two-way contingency tables. It will be shown by example that the resulting parameter estimates are numerically similar to corresponding maximum likelihood estimates for ordinal log-linear models.

We investigate an alternative model for signals encountered in acoustic environments to the traditional Gaussian process. The sound signals in this case are assumed to be sub-Gaussian of impulsive nature. The noise encountered in these environments predominantly stems from reverberation or multipath effects, which makes it significantly dependent on the source. Hence, the noise is also modeled as jointly sub-Gaussian. The Levy alpha-stable distribution, of characteristic exponent 0.5 and index of symmetry 1, is used together with a multivariate Gaussian density to derive the sub-Gaussian process. Based on this density, the stochastic Maximum Likelihood (ML) estimator is formulated. A separable solution of the estimator is given. Subsequently, simulations demonstrating the performance gains relative to the Gaussian-based ML estimator are provided, as well as a comparison of the two methods on localization of real sounds gathered with a 20- and 41-microphone arrays.

This study aims at evaluating the performance of Zero Inflated Negative Binomial (ZINB) regression analysis using the Maximum Likelihood Estimation (MLE) approach through simulation study. The research data used are secondary data and simulations. Secondary data was obtained from the Ministry of Health of the Republic of Indonesia in 2023 regarding cases of under-five deaths due to pneumonia with a total of 38 samples. The simulation study is conducted to analyze the performance of ZINB regression based on various sample sizes and proportions of zero values. The results show that the ZINB regression model with the MLE approach produces parameter estimates that tend to be more sensitive to sample size, with improved performance at large sample sizes. The ZINB regression model gives results for a large proportion of zeros and satisfies the excess zero condition. Data with a large proportion of zeros reflects high variability as well as the presence of excess zeros, so the ZINB regression model can provide more stable and precise parameter estimates than those with a lower proportion of zeros. Therefore, the ZINB regression model is effective for data with a high proportion of zeros as it fits the characteristics of the data distribution.

SEAPODYM is a model developed for investigating spatiotemporal dynamics of fish populations under the influence of both fishing and the environment. The model simulates age-structured population dynamics using advection–diffusion–reaction equations describing movement, recruitment, and natural and fishing mortality. The dynamic processes are constrained by environmental data and distributions of prey species. Model parameter estimation using fishing data was implemented earlier based on a maximum likelihood estimation (MLE) approach and adjoint technique. Here, we describe the integration of tagging data into the existing MLE approach with application to skipjack tuna (Katsuwonus pelamis) in the Pacific Ocean. We find that tagging data improve estimates of species habitat parameters and movement rates and hence allow better representation of spatial dynamics of fish population. Due to estimated lower diffusion and higher advection rates, the model predicts less non-observed “cryptic” biomass, which leads to the stock sizes being closer to those estimated by stock assessment models commonly used by tuna commissions.

This paper develops a rigorous and practical method for estimating the reliability-with confidence regions-of a complex system based on a combination of full system and subsystem (and/or component or other) tests. It is assumed that the system is composed of multiple processes (e.g., the subsystems and/or components within subsystems), where the subsystems may be arranged in series, parallel (i.e., redundant), combination series/parallel, or other mode. Maximum likelihood estimation (MLE) is used to estimate the overall system reliability. Interestingly, for a given number of subsystems and/or components, the likelihood function does not change with the system configuration; rather, only the optimization constraints change, leading to an appropriate MLE. The MLE approach is well suited to providing asymptotic or finite-sample confidence bounds through the use of Fisher information or bootstrap Monte Carlo-based sampling.

NASA has proposed to put a Space Station in orbit in the early 1980s. This paper describes the steps involved in applying maximum likelihood estimation (MLE) method to identify the parameters of the dynamic model of the Space Station. The amount of computation in the MLE approach is reduced by using the method of successive approximation. Numerical results based on simulated data is presented in the paper.