An Accurate Substitution Method for Analyzing Censored Data

It is known that various types of location privacy attacks can be carried out using a personalized transition matrix that is learned for each target user, or a population transition matrix that is common to all target users. However, since many users disclose only a small amount of location information in their daily lives, the training data can be extremely sparse. The aim of this paper is to clarify the risk of location privacy attacks in this realistic situation. To achieve this aim, we propose a learning method that uses tensor factorization (or matrix factorization) to accurately estimate personalized transition matrices (or a population transition matrix) from a small amount of training data. To avoid the difficulty in directly factorizing the personalized transition matrices (or population transition matrix), our learning method first factorizes a transition count tensor (or matrix), whose elements are the number of transition counts that the user has made, and then normalizes counts to probabilities. We focus on a localization attack, which derives an actual location of a user at a given time instant from an obfuscated trace, and compare our learning method with the maximum likelihood (ML) estimation method in both the personalized matrix mode and the population matrix mode. The experimental results using four real data sets show that the ML estimation method performs only as well as a random guess in many cases, while our learning method significantly outperforms the ML estimation method in all of the four data sets.

Travel Time Reliability Estimation Model Using Observed Link Flows in a Road Network

Um dos problemas comumente encontrado na análise estatística de dados provenientes do monitoramento da qualidade da água envolve os chamados dados censurados. Este estudo mostra como os valores calculados das concentração médias de amostras que contenham dados censurados são influenciadas pelo tamanho da amostra, percentual de dados censurados e método de cálculo. As concentrações médias foram calculadas com os métodos de substituição e maximum likelihood estimator (MLE) para amostras de demanda química de oxigênio e fosfato. O intervalo de confiança da média foi utilizado como métrica de comparação. Os resultados revelam que as concentrações médias calculadas com o método de substituição simples se posiciona fora do intervalo de confiança. Já o método MLE calcula valores aceitáveis, porém, a eficácia do método depende de um limite de elasticidade do nível de censura e do tamanho da amostra.

Recent catastrophic failures of the rocket motor on a US missile caused concern in the US Navy that the missile might not be serviceable past its estimated service life of 20 years. This paper analyzes nearly 2000 firings of the motor under field conditions using classical maximum likelihood estimation (MLE) methods and Bayesian methods. MLE methods indicate that there could be less than a 1% chance that the motor will survive past 20 years of life and are not considered credible. Bayesian methods indicate that there is better than a 99% chance that the motors will survive past 20 years of life. The authors present reasons for preferring the Bayesian analysis and discuss testing schemes for more precise estimates. They comment on the lack of data to perform degradation analysis as a function of temperature cycling for the motor and make recommendations for future missile system data collection.

Population density is an important state variable in ecology and monitoring of wildlife populations. In the study of large carnivores, traditional density estimation methods have relied on camera trapping to collect capture–recapture data using sampling designs suited to a single target species and ad hoc methods to calculate the effective trapping area. We describe an application of spatially explicit capture–recapture analysis to estimate density for four sympatric carnivore species using a standard spatial sampling grid, camera trap sampling, and maximum-likelihood estimation methods. We used camera traps deployed in four adjacent, sequential camera arrays to construct capture histories for leopard, aardwolf, spotted hyena, and striped hyena in an African savanna/scrub ecosystem. We considered two methods of constructing trapping histories: (1) a simultaneous layout in which all cameras are deployed and active for 21 trapping intervals of 24 h each; and (2) an incomplete layout in which cameras are considered deployed for 84 sampling intervals, but with only a fraction of traps active for a given sampling interval. We estimated density and confidence or profile likelihood intervals for each species using a mean maximum distance moved method and maximum-likelihood estimation procedures with model averaging. Maximum-likelihood densities ranged from 4.93/100 km2 for spotted hyena to 12.03/100 km2 for leopard. Estimates did not differ between simultaneous and incomplete trap layouts. Our approach demonstrates the utility and cost effectiveness of using maximum-likelihood density estimation methods in studies of sympatric species that are individually recognizable.

In weak GNSS signal environments, extending integration time is paramount to improving the GNSS receiver's sensitivity. Furthermore, sufficient coherent integration can help to separate the line-of-sight (LOS) and non LOS (NLOS) signals — primarily in the Doppler domain — for multipath mitigation. However, extending integration time is limited by the presence of the navigation message data bits. The Maximum-Likelihood (ML) estimation method has been shown as the most effective way to estimate the navigation bit boundary locations (i.e., bit synchronization) and subsequently estimate the data bit values (i.e., bit decoding) in the presence of noise alone. This paper further analyzes the performance of ML estimation method as a function of various other parameters by using the successful synchronization rate (SSR) and successful decoding rate (SDR) as the criteria. The parameters considered include the number of data bits required (i.e., integration time) and the Doppler frequency error, and both are evaluated under different signal strength scenarios. The requirements for bit synchronization are analyzed under three SSRs, which are 85%, 90% and 95%. Results indicate that, under the SSR of 90% and the SDR of 90%, bit synchronization and bit decoding are valid for signal strengths of 15 dB-Hz and 20 dB-Hz respectively, and the Doppler frequency errors should not be larger than 24 Hz and 11 Hz respectively.

Aims: This study modelled a- and b-values of earthquakes employing the least squares regression and maximum likelihood estimate methods. Methodology: Data used in the study were obtained from the International Seismological Centre (ISC), an earthquake catalogue of the United Kingdom. The time window was from 1st January 1988 to 31st December 2010 (30 years) with earthquake focal depth of 0-700km and magnitude Mb ≥ 1.3. Ten different locations were selected and a total of 149,965 events were used. The acquired data were processed and analysed using Microsoft Excel and the hypothesis was tested using independent t-test statistics with the aid of Statistical Software for Social Sciences (SPSS) version 23.0. Results: The findings of the study revealed that the b- and a-values calculated using the least squares regression method were higher than the ones obtained using the maximum likelihood estimate method. Also, the hypothesis revealed that there is a significant difference between the use of the least squares regression method and the maximum likelihood estimate method in the determination of b- and a-value of earthquakes in a given region. Conclusion: The maximum likelihood estimate gives a better estimate of b- and -a values than the least squares regression method.

Developing an accurate Lowest Astronomical Tide (LAT) in a continuous form is essential for many maritime applications as it can be employed to develop an accurate continuous vertical control datum for hydrographic surveys applications and to produce accurate dynamic electronic navigation charts for safe maritime navigation by mariners. The LAT can be developed in a continuous (surface) using an estimated LAT surface model from the hydrodynamic ocean model along with coastal discrete LAT point values derived from tide gauges data sets to provide the corrected LAT surface model. In this paper, an accurate LAT surface model was developed for the Red Sea case study using a Maximum Likelihood Estimator (MLE) with multiple hydrodynamic ocean models hybridization, namely, WebTide, FES2014, DTU10, and EOT11a models. It was found that the developed optimal hybrid LAT model using MLE with multiple hydrodynamic ocean models hybridization ranges from 0.1 m to 1.63 m, associated with about 2.4 cm of uncertainty at a 95% confidence level in the Red Sea case study area. To validate the accuracy of the developed model, the comparison was made between the optimal hybrid LAT model developed from multiple hydrodynamic ocean models hybridization using the MLE method with the individual LAT models estimated from individual WebTide, FES2014, DTU10, or EOT11a ocean models based on the associated uncertainties estimated at a 95% confidence level. It was found that the optimal hybrid LAT model accuracy is superior to the individual LAT models estimated from individual ocean models with an improvement of about 50% in average, based on the estimated uncertainties. The importance of developing optimal LAT surface model using the MLE method with multiple hydrodynamic ocean models hybridization in this paper with few centimeters level of uncertainty can lead to accurate continuous vertical datum estimation that is essential for many maritime applications.

Chemical risk assessment is important for risk management, and estimates of chemical exposure must be as accurate as possible. Chemical concentrations in food below the limit of detection are known as nondetects and result in left-censored data. During statistical analysis, the method used for handling values below the limit of detection is important. Many risk assessors employ widely used substitution methods to treat left-censored data, as recommended by international organizations. The National Institute of Food and Drug Safety Evaluation of South Korea also recommends these methods, which are currently used for chemical exposure assessments. However, these methods have statistical limitations, and international organizations recommend more advanced alternative statistical approaches. In this study, we assessed the validity of currently used statistical methods for handling nondetects. To identify the most suitable statistical method for handling nondetection, we created virtual data and conducted simulation studies. Based on both simulation and case studies, the Maximum Likelihood Estimation (MLE) and Robust Regression on Order Statistics (ROS) methods were found to be the best options. The statistical values obtained from these methods were similar to those obtained from the commonly used 1/2 Limit of Detection (LOD) substitution method for nondetection treatment. In three case studies, we compared the various methods based on the root mean squared error. The data for all case studies were from the same source, to avoid heterogeneity. Across various sample sizes and nondetection rates, the mean and 95th percentile values for all treatment methods were similar. However, "lognormal maximum likelihood estimation" method was not suitable for estimating the mean. Risk assessors should consider statistical processing of monitoring data to reduce uncertainty. Currently used substitution methods are effective and easy to apply to large datasets with nondetection rates <80%. However, advanced statistical methods are required in some circumstances, and national guidelines are needed regarding their use in risk assessments.

In this paper, we propose a disturbance covariance matrix estimation method for radar signal processing when the number of training samples is severely limited. Several maximum likelihood (ML) estimation methods based on the structured of the covariance matrix, which is modeled as a sum of an unknown positive semi-definite Hermitian matrix and a matrix proportional to identity, have been proposed to solve the problem. Based on the covariance structure, we incorporate additional constraint on the low rank property of the covariance matrix, and propose a low rank regularized ML estimation method for covariance matrix estimation. The low rank is achieved by solving a reweighted trace minimization problem. Experimental results demonstrate the effective of the proposed method, both in the accuracy of rank estimation and the improvement of signal interference and noise ratio (SINR).

Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method (MLE). Then I consider whether Weisberg’s account could capture the judgments involved in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg’s atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion.

This research aims to analyze which factors are most determining in influencing interest in using pay-later. This type of research is multivariate analytical research using a quantitative approach. All pay-later service customers who engage in e-commerce make up the study's population. Purposive sampling and non-probability sampling were employed in the sample-taking process based on this study. There are one hundred samples. Researchers have made observations to gather data. The author intends to employ principal component analysis and maximum likelihood estimation techniques for data analysis. Following the completion of the study, the following findings were made. The principal component analysis method's RMSE value is 0.02. The maximum likelihood estimates method's root mean square error is 0.015. Therefore, it can be said that the principal component analysis approach is appropriate for usage because it can explain factor formation due to its strong factor loading value and low RMSE value. The influence of advantages is the most important element determining interest in pay-later utilizing the principal component analysis method since it has the highest loading factor value in decision-making.

Magnetic Resonance Sounding: Special Issue of Journal of Applied Geophysics, 2008

The beta regression model (BRM) is used when the dependent variable may take continuous values and be bounded in the interval (0, 1), such as rates, proportions, percentages and fractions. Generally, the parameters of the BRM are estimated by the method of maximum likelihood estimation (MLE). However, the MLE does not offer accurate and reliable estimates when the explanatory variables in the BRM are correlated. To solve this problem, the ridge and Liu estimators for the BRM were proposed by different authors. In the current study, the James Stein Estimator (JSE) for the BRM is proposed. The matrix mean squared error (MSE) and the scalar MSE properties are derived and then compared to the available ridge estimator, Liu estimator and MLE. The performance of the proposed estimator is evaluated by conducting a simulation experiment and analyzing two real-life applications. The MSE of the estimators is considered as a performance evaluation criterion. The findings of the simulation experiment and applications indicate the superiority of the suggested estimator over the competitive estimators for estimating the parameters of the BRM.

Location awareness has become one of the most popular areas for mobile internet applications. Normally, the method of maximum likelihood estimation (MLE) can be used to estimate the position of the unknown nodes. However, when the number of anchor nodes is small within the communication range, the traditional methods cannot work very well. This paper provides a novel location approach to estimate the position by selecting valuable neighbors (both anchor and regular nodes) with Minimized Error Propagation Location method based on Error estimation (MEPLE). The simulation results show that the MEPLE outperforms the MLE overall, especially when the number of anchor node is relatively small such as between 10 and 20, MEPLE can reduce 50% to 80% estimation error on average.