Maximum Likelihood Prediction Research Articles

Analyzing the Strelitzia Juncea Cellulosic Fibers Mechanical Properties’ Experimental Data Using Various Statistical Methods

ABSTRACT Manufacturing composites using natural fibers support the development of more ecologically friendly applications in many industrial areas, including sports equipment, wind turbine blades, construction materials, packaging, medical devices, and prosthetics. To investigate the tests’ number (N) impact on the fibers’ tensile behavior, newly developed fibers from the Strelitzia juncea (SJ) plant are being studied for their mechanical properties using various statistical laws. This study aims to assess the SJ fibers’ performance with a gauge length of 50 mm under quasi-static stress conditions. 150 SJ fibers were tensile tested in five groups (30, 60, 90, 120, and 150) to determine the N impact on the fibers’ tensile strength, break deformation, and Young’s modulus. Weibull and log-normal distributions were used to study statistically the mechanical properties results, and their dispersion was investigated and quantified using least squares and maximum likelihood prediction model with a confidence level of 95%.

Predicting future failures in generalized order statistics and related models

Quantile regression predictions are considered for generalized order statistics which extend results previously established for record values and order statistics. In order to derive the results, some (univariate and bivariate) distortion representations of generalized order statistics are established. In particular, prediction of the sth generalized order statistic X ( s ) ∗ based on F given a single (generalized) order statistic X ( r ) ∗ with r<s will be addressed. The presentation includes results for both known and unknown baseline distributions. In the latter case, we consider exponential distributions with unknown mean as well as the proportional hazards model. Using unimodality properties of the marginal density functions (pdf) of generalized order statistics, we find a simple representation of the maximum likelihood prediction of X ( s ) ∗ given X ( r ) ∗ .

Asymptotic Predictive Inference of Negative Lower Tail Index Distributions

ABSTRACT In this paper, the results of El-Adll et al. [Asymptotic prediction for future observations of a random sample of unknown continuous distribution, Complexity 2022 (2022), Art. ID 4073799], are extended to the lower negative tail index distributions. Three distinct estimators of the lower negative tail index are proposed, as well as an asymptotic confidence interval. Moreover, different asymptotic predictive intervals for future observations are constructed for distributions attracted to the lower extreme value distribution with a negative tail index. Furthermore, the asymptotic maximum likelihood estimator (AMLE) of the shape parameter, as well as an asymptotic maximum likelihood predictor (AMLP), are obtained. Finally, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods.

TomFusioNet: A tomato crop analysis framework for mobile applications using the multi-objective optimization based late fusion of deep models and background elimination

Crop disease is a critical concern for the farmers which needs to be addressed to mitigate production loss. Multifarious frameworks have been proposed for the rapid recognition of tomato crop diseases, however, their practical deployment is a daunting task. The existing models suffer from performance issues such as overfitting and gradient vanishing. To alleviate these issues, this research proposes an end-to-end tomato crop analysis framework, TomFusioNet, for mobile applications. For feature extraction, the late-fusion technique is leveraged by aggregating the results of modified cross-domain transfer learning models using the maximum likelihood prediction strategy. Multi-layer Perceptron models are utilized as meta-learners. TomFusioNet’s pipeline comprises two modules, namely, DeepRec and DeepPred. DeepRec provides preliminary disease recognition results while DeepPred further identifies the type of illness in crop. Logic gate mapping is employed to reduce unnecessary wastage of mobile computation. The hyperparameters of DeepPred are tuned using the multi-objective optimization based non-dominated sorting genetic algorithm II for performance enhancement. We highlighted the significance of feature relevancy; therefore, a hue, saturation and value (HSV) color model-based background elimination technique is also proposed. TomFusioNet can be incorporated in a smartphone app, conceptualized for remote crop monitoring. The proposed DeepRec and DeepPred models achieved an average accuracy of 99.93% and 98.32%, respectively. According to experimental results, TomFusioNet, which is built on NSGA-II, beats state-of-the-art models with a convergence loss of 0.021 and an AUC value of 99.10%. The proposed app framework’s functioning does not require any human in loop. Furthermore, the latency of framework in real-time is less than 2 s, hence, it is effective for rapid tomato crop analysis.

A Bayesian approach to forward and inverse abstract argumentation problems

This paper studies a fundamental mechanism by which conflicts between arguments are drawn from sentiments regarding acceptability of the arguments. Given sets of arguments, an inverse abstract argumentation problem seeks attack relations between arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in each of the attack relations. It is an inverse problem of the traditional problem we refer to as the forward abstract argumentation problem. Given an attack relation, the forward abstract argumentation problem seeks sets of arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in the attack relation. We give a probabilistic model of argumentation-theoretic inference. It is a generative model formalising the process by which acceptability semantics interprets acceptability of arguments in a given attack relation. We show that it gives a broad view of solutions to the forward and inverse abstract argumentation problems. Specifically, solutions to the inverse and forward abstract argumentation problems are shown to be equivalent to a maximum likelihood estimate and maximum likelihood prediction, respectively, which are both available with the generative model. In addition, they are shown to be special cases of the posterior distribution and the evidence, respectively, which are both obtained by probabilistic inference on the generative model. We report an experiment result and application example of the generative model in the inverse problems.

Predictive inference of dual generalized order statistics from the inverse Weibull distribution

In this paper, some predictive results of dual generalized order statistics (DGOSs) from the inverse Weibull distribution are obtained. For this goal, different predictive and reconstructive pivotal quantities are proposed. Moreover, several predictive and reconstructive intervals concerning DGOSs based on the inverse Weibull distribution are constructed. Furthermore, the maximum likelihood predictor as well as the predictive maximum likelihood estimates based on DGOSs are studied. Finally, simulation studies are carried out to assess the efficiency of the obtained results.

Prediction of Future Lifetimes for a Simple Step-Stress Model with Type-II Censoring and Rayleigh Distribution

In this paper, we discuss the prediction problem of the lifetimes to failure of units from Rayleigh distribution with Type-II censoring for a simple step-stress setup under cumulative exposure model. We consider several methods of point prediction, including maximum likelihood predictor, conditional median predictor, and best unbiased predictor. In addition, we discuss the prediction intervals for future lifetimes of the censored units using pivotal quantity, highest conditional density, and shortest-length based methods. Monte Carlo simulation is conducted to compare the proposed prediction methods. Further, a real data set is analyzed for illustrative purposes.

Prediction of future failures in the log-logistic distribution based on hybrid censored data

We consider the prediction of future observations from the log-logistic distribution. The data is assumed hybrid right censored with possible left censoring. Different point predictors were derived. Specifically, we obtained the best unbiased, the conditional median, and the maximum likelihood predictors. Prediction intervals were derived using suitable pivotal quantities and intervals based on the highest density. We conducted a simulation study to compare the point and interval predictors. It is found that the point predictor BUP and the prediction interval HDI have the best overall performance. An illustrative example based on real data is given.

Residual lifetime prediction for sequential k-out-of-n systems with dependent component lifetimes

This paper deals with residual lifetimes of sequential k-out-of-n systems when component lifetimes are dependent. Given the first j component failure times, an unbiased predictor for the (j + i)-th component failure time is derived. Maximum likelihood prediction of future component failure times is also studied. Estimation of the nuisance parameters is investigated under the conditional proportional hazard rates model with the Exponential baseline distribution and the Gumbel-Hougaard copula. Illustrative examples are also given.

Prediction of Censored Weibull Lifetimes in a Simple Step-Stress Plan With Khamis-Higgins Model

In this paper, we discuss the prediction of the lifetimes to failure of censored units from Weibull distribution for a simple step-stress plan under Khamis-Higgins model. Different methods of prediction are considered including maximum likelihood predictor, modifified maximum likelihood predictor, conditional median predictor, and best unbiased predictor. Another aspect of prediction is constructing prediction limits for future lifetimes of the censored units. The pivotal quantity, highest conditional density, and shortest-length based methods are discussed in this paper. Monte Carlo simulations are performed to compare all the prediction methods developed here and one real data set is analyzed for illustrative purposes.

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.

Comparison of estimators and predictors based on modified weibull records: Bayesian and non-Bayesian approaches

Based on record statistics from three-parameter modified Weibull distribution, we consider the problem of estimating the unknown parameters using Bayesian and non-Bayesian approaches. Under a continuous-discrete joint prior distribution, Bayesian estimators and confidence intervals for the shape and scale parameters involved in the underlying model are obtained. In addition, maximum likelihood prediction and Bayesian prediction (either point or interval) of future record statistics based on an informative set of records are developed. Data analyses involving records extracted from a machine used to measure burr and times to breakdown of an insulating fluid between electrodes have been performed. Finally, Monte Carlo simulations are performed to compare the methods developed here.

Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization

Motivation Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy. Method We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods. Results On challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2-10%, and Q3 accuracy by more than 1-3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction. Availability and implementation A preliminary implementation in a new tool we call Nnessy is available free for non-commercial use at http://nnessy.cs.arizona.edu.

Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization.

MotivationProtein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy.MethodWe present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods.ResultsOn challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2–10%, and Q3 accuracy by more than 1–3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction.Availability and implementationA preliminary implementation in a new tool we call Nnessy is available free for non-commercial use at http://nnessy.cs.arizona.edu.

Prediction Methods for Future Failure Times Based on Type-II Right-Censored Samples from New Pareto-Type Distribution

In this paper, we consider the prediction problem of the future failure times based on observed data from two-parameter Pareto-type distribution under Type-II right-censored samples. Different point predictors including best unbiased, maximum likelihood and conditional median predictors of the future failure times are obtained. The corresponding prediction intervals using pivotal quantity, conditional argument and shortest-length based method are also developed. The so-obtained point predictors and prediction intervals are compared via experimental numerical simulation. The optimality criteria considered for comparison purposes are prediction bias and mean square prediction error for point predictors and average length and coverage probability for prediction intervals. Real-life data representing the duration of the failure times of mechanical components as reported by Murthy (in: Wiley series in probability and statistics, Wiley, Hoboken, 2004) are analyzed for illustrative purposes.

Socio-cultural aspects of farmers’ perception of the risk of climate change and variability in Central Ethiopia

ABSTRACT There is a dearth of evidence on the socio-cultural aspects of climate change risk perception in developing countries. This study investigates the variation in farmers’ perception of the risks of climate change and variability by their socio-cultural characteristics. Data were collected from 810 randomly selected households in central Ethiopia using a structured questionnaire. Polling, a maximum likelihood prediction method of multivariate analysis that jointly evaluates the combined roles of different variables, allowing for non-parametric interactions, was used to analyse the data. The results show that households with a high risk perception have high accurate knowledge about climate change, experience of climatic events, value both societal and individual responsibilities to reduce the impact of climate change, and reside in the midland agro-ecological settings. On the other hand, a low descriptive norm, low social capital, lack of access to media, low level of education, and valuing autonomy characterize households with a low risk perception. The findings entail that communication strategies focusing on evidence-based knowledge about causes and consequences of and responses to climate change, past experience of climatic events, as well as fostering self-transcendence and openness-to-change values raise risk perception to engage farmers in adaptation actions.

Prediction for Progressively Type-II Censored Competing Risks Data from the Half-Logistic Distribution

Point and interval predictions of the s-th order statistic in a future sample are discussed. The informative sample is assumed to be drawn from a general class of distributions which includes, among others, Weibull, compound Weibull, Pareto, Gompertz and half-logistic distributions. The informative and future samples are progressively type-II censored, under competing risks model, and assumed to be obtained from the same population. A special attention is paid to the half-logistic distribution. Using six different progressive censoring schemes, numerical computations are carried out to illustrate the performance of the procedure. An illustrative example based on real data is also considered. The biases, mean squared prediction errors of the maximum likelihood predictors, coverage probabilities and average interval lengths of the Bayesian prediction intervals are computed via a simulation study.

Maximum product of spacings prediction of future record values

A spacings-based prediction method for future upper record values is proposed as an alternative to maximum likelihood prediction. For an underlying family of distributions with continuous cumulative distribution functions, the general form of the predictor as a function of the estimator of the distributional parameters is established. A connection between this method and the maximum observed likelihood prediction procedure is shown. The maximum product of spacings predictor turns out to be useful to predict the next record value in contrast to likelihood-based procedures, which provide trivial predictors in this particular case. Moreover, examples are given for the exponential and the Pareto distributions, and a real data set is analyzed.

Maximum observed likelihood prediction of future record values

Point prediction of future upper record values is considered. For an underlying absolutely continuous distribution with strictly increasing cumulative distribution function, the general form of the predictor obtained by maximizing the observed predictive likelihood function is established. The results are illustrated for the exponential, extreme-value and power-function distributions, and the performance of the obtained predictors is compared to that of maximum likelihood predictors on the basis of the mean squared error and the Pitman’s measure of closeness criteria. For exponential and extreme-value distributions, it is shown that under slight restrictions, the maximum observed likelihood predictor outperforms the maximum likelihood predictor in terms of both performance criteria.

Maximum likelihood prediction of records from 3-parameter Weibull distribution and some approximations

Based on record data, numerous authors have discussed the estimation of two-parameter Weibull distribution using classical and Bayesian approaches. In this paper, prediction of future records based on observed ones, using the maximum likelihood method, is considered. For a restricted parametric space, the existence and uniqueness of the maximum likelihood predictors of future records as well as the predictive maximum likelihood estimators of all unknown quantities are also established. Alternative approximate methods to obtain the likelihood estimators and predictors, which always exist and are easy-to-determine, are also discussed. The alternative approximate procedures studied in this paper are transformation-based predictive likelihood function, corrected predictive likelihood function, maximum product of spacings prediction Monte Carlo simulations are performed to compare the proposed methods and one real data set is also analyzed for illustrative purposes.