Modeling the distribution of DCT coefficients for JPEG reconstruction

Aim of study: The aim of this work was to model diameter distributions of Quercus suber stands. The ultimate goal was to construct models enabling the development of more affordable forest inventory methods. This is the first study of this type on cork oak forests in the area.Area of study: The area of study is “Los Alcornocales” Natural Park (Cádiz-Málaga, Spain).Material and methods: The diameter distributions of 100 permanent plots were modelled with the two-parameter Weibull function. Distribution parameters were fitted with the non-linear regression, maximum likelihood, moment and percentile-based methods. Goodness of fit with the different methods was compared in terms of number of plots rejected by the Kolmogorov-Smirnov test, bias, mean square error and mean absolute error. The scale and shape parameters in the Weibull function were related to the stand variables by using the parameter prediction model.Main results: The best fitting was obtained with the non-linear regression approach, using as initial values those obtained by maximum likelihood method, the percentage of rejections by the Kolmogorov-Smirnov test was 2% of the total number of cases. The scale parameter (b) was successfully modelled in terms of the quadratic mean diameter under cork (R2 adj = 0.99). The shape parameter (c) was modelled by using maximum diameter, minimum diameter and plot elevation (R2 adj = 0.40).Research highlights: The proposed model diameter distribution can be a highly useful tool for the inventorying and management of cork oak forests.Key words: maximum likelihood method; moment method; non linear regression approach; parameter prediction model; percentile method; scale parameter; shape parameter.

Felsenstein, J. (Department of Genetics SK-50, University of Washington, Seattle, Washington 98195). 1973. Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Syst. Zool. 22:240-249.The general maximum likelihood approach to the statistical estimation of phylogenies is outlined, for data in which there are a number of discrete states for each character. The details of the maximum likelihood method will depend on the details of the probabilistic model of evolution assumed. There are a very large number of possible models of evolution. For a few of the simpler models, the calculation of the likelihood of an evolutionary tree is outlined. For these models, the maximum likelihood tree will be the same as the parsimonious (or minimum-steps) tree if the probability of change during the evolution of the group is assumed a priori to be very small. However, most sets of data require too many assumed state changes per character to be compatible with this assumption. Farris (1973) has argued that maximum likelihood and parsimony methods are identical under a much less restrictive set of assumptions. It is argued that the present methods are preferable to his, and a counterexample to his argument is presented. An algorithm which enables rapid calculation of the likelihood of a phylogeny is described. [Evolutionary trees: maximum likelihood.] The first systematic attempt to apply standard statistical inference procedures to the estimation of evolutionary trees was the work of Edwards and Cavalli-Sforza (1964; see also Cavalli-Sforza and Edwards, 1967). At about the same time, the parsimony' or minimum evolutionary steps method of Camin and Sokal (1965) gave a great impetus to the development of welldefined procedures for obtaining evolutionary trees. Edwards and Cavalli-Sforza concerned themselves with data from continuous variables such as gene frequencies and quantitative characters. The CaminSokal approach, on the other hand, was developed for characters which are recorded as a series of discrete states. Although some taxonomists have declared that the problem of guessing phylogenies should be viewed as a problem of statistical inference (Farris, 1967, 1968; Throckmorton, 1968), until recently there have been no attempts to explore the relationship between the statistical inference and minimum-steps approaches. Recently, Farris (1973) has presented a detailed argument that, under certain reasonable assumptions, the maximum-likelihood method of statistical inference appropriate to discrete-character data is precisely the parsimony method of Camin and Sokal. In this paper, I will examine the application of maximum likelihood methods to discrete characters, and will show that parsimony methods are not maximum likelihood methods under the assumptions made by Farris. They are maximum likelihood methods under considerably more restrictive assumptions about evolution. METHODS OF MAXIMUM LIKELIHOOD Suppose that we want to estimate the evolutionary tree, T, which is to be specified by the topological form of the tree and the times of branching. We are given a set of data, D, and a model of evolution, M, which incorporates not only the evolutionary processes, but also the processes of sampling by which we obtained the data. This model will usually be probabilistic, involving random events such as changes of the environment, occurrence of favorable

Analysis of snow load probabilistic models and calculation of reference snow pressure using maximum likelihood method for multiple cities in Liaoning Province, China

Many clinical or prevention studies involve missing or censored outcomes. Maximum likelihood (ML) methods provide a conceptually straightforward approach to estimation when the outcome is partially missing. Methods of implementing ML methods range from the simple to the complex, depending on the type of data and the missing-data mechanism. Simple ML methods for ignorable missing-data mechanisms (when data are missing at random) include complete-case analysis, complete-case analysis with covariate adjustment, survival analysis with covariate adjustment, and analysis via propensity-to-be-missing scores. More complex ML methods for ignorable missing-data mechanisms include the analysis of longitudinal dropouts via a marginal model for continuous data or a conditional model for categorical data. A moderately complex ML method for categorical data with a saturated model and either ignorable or nonignorable missing-data mechanisms is a perfect fit analysis, an algebraic method involving closed-form estimates and variances. A complex and flexible ML method with categorical data and either ignorable or nonignorable missing-data mechanisms is the method of composite linear models, a matrix method requiring specialized software. Except for the method of composite linear models, which can involve challenging matrix specifications, the implementation of these ML methods ranges in difficulty from easy to moderate.

The T-Year estimate in a partial duration (PD) series with log-normal distributed pesk exceedances is analysed in case of parameter estimation by means of the maximum likelihood method and the method of moments, respectively. Approximate expressions for bias and variance of the T-year estimate are developed, and it is hereby found that the sample variance of the log-normal distribution parameters is the main reason for the uncertainty of the T-year estimate, whereas the sample variance of the intensity of the process is of minor importance. Providing smaller bias (in absolute terms) and smaller variance the maximum likelihood method is found to be superior to the method of moments. The two methods produce bias of different signs, positive bias in case of maximum likelihood estimation and negative in case of estimation by method of moments. The variance expression in case of maximum likelihood estimation is to some extent wave heights. The purpose of the investigation was to assess the 50-year significant wave height on the basis of 17 years of hindcasted wave data together with the standard deviation of the estimate.

This paper addresses a novel approach based on neuro-fuzzy inference system to solve the estimation problem of the K-distributed parameters. The method is based on a network implementation with real weights and the real genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. The proposed FNN estimator is based on the arithmetic and geometric means computed from the data in a manner which significantly reduces the computational requirements when compared to Raghavanpsilas and the maximum likelihood methods. The simulation results are presented to demonstrate the validity of the approach as well as the successfulness of the FNN estimator for low variances of parameter estimates when compared with existing MOFM (method of fractional moments) and ML/MOM (maximum-likelihood and method of moments) approaches. In addition, the method yields parameter estimates with lower computational complexity compared with standard techniques.

Relative Efficiencies of the Maximum Likelihood, Maximum Parsimony, and Neighbor-Joining Methods for Estimating Protein Phylogeny

Calculation of the uncertainty in complication probability for various dose–response models, applied to the parotid gland

The most general approach to the study of rare extreme events is based on the extreme value theory. The fundamental General Extreme Value Distribution lies in the basis of this theory serving as the limit distribution for normalized maxima. It depends on three parameters. Usually the method of maximum likelihood (ML) is used for the estimation that possesses well-known optimal asymptotic properties. However, this method works efficiently only when sample size is large enough (~200–500), whereas in many applications the sample size does not exceed 50–100. For such sizes, the advantage of the ML method in efficiency is not guaranteed. We have found that for this situation the method of statistical moments (SM) works more efficiently over other methods. The details of the estimation for small samples are studied. The SM is applied to the study of extreme earthquakes in three large virtual seismic zones, representing the regime of seismicity in subduction zones, intracontinental regime of seismicity, and the regime in mid-ocean ridge zones. The 68%-confidence domains for pairs of parameter (ξ, σ) and (σ, μ) are derived.

The ranked set sampling (RSS) is a cost-effective method of sampling that can be used in a wide range of statistical problems. In this paper, the shape and the scale parameters of Nadarajah-Haghighi extension of the exponential distribution are estimated based on a simple random sample (SRS) and RSS. Three cases are considered: 1) the scale parameter is known; 2) the shape parameter is known; 3) both shape and scale parameters are unknown. Observations are done when the ranking mechanism in the ranked set sample is perfect and when it is not. Method of moments, the maximum likelihood method, and a modification of the maximum likelihood method are used. The obtained estimators are compared in terms of their biases and mean square errors (MSE). The results revealed that estimators based on RSS tend to show better properties (smaller bias and MSE) relative to their SRS counterparts, regardless of the quality of the ranking.

The concepts of risk and uncertainty are commonly utilized for designing and evaluating hydraulic structures such as spillways and dikes. In this study, uncertainty analysis is performed for the risk of failure based on the generalized logistic distribution that is recently recommended as an appropriate probability distribution for flood frequency analysis in UK by Institute of Hydrology. For this purpose, we derived the expected values and the variances of the risk of failure based on the methods of moments, maximum likelihood, and probability weighted moments for the generalized logistic model. The derived variances of the risk of failure based on the methods of moments, maximum likelihood, and probability weighted moments are functions of sample size, design life, and non-exceedance probability. In addition, simulation experiments have been performed to figure out the behavior of the risk of failure for various the sample sizes, design lives, non-exceedance probabilities, and coefficients of variation.

In this paper a new method of estimating the shape parameter of generalized error distribution (GED), called ‘approximated moment method’, was proposed. The following estimators were considered: the one obtained through the maximum likelihood method (MLM), approximated fast estimator (AFE), and approximated moment method (AMM). The quality of estimator was evaluated on the basis of the value of the relative mean square error. Computer simulations were conducted using random number generators for the following shape parameters: s = 0.5, s = 1.0 (Laplace distribution) s = 2.0 (Gaussian distribution) and s = 3.0.

A Poisson ridge regression estimator

Problem statement: The double Pareto distribution appeared most often as model for variety of fields, including archaeology, biology, economics, environmental science, finance and physics. The distribution exhibits Paretian power-law behavior in both tails. The family of double Pareto distributions has recently been proposed for modeling growth rates such as annual gross domestic product, stock prices, foreign currency exchange rates and company sizes. In this study, I develop parameter estimates for the double Pareto distribution that are easy to compute. I compare the performance of the maximum likelihood estimate with Bayesian and the method of moments estimates. Approach: This study contracted with maximum likelihood, the method of moments and Bayesian using Jeffrey’s prior and the extension of Jeffrey’s prior information. The comparisons are made on the performance of these estimators with respect to the Mean Squared Error (MSE) for small, moderate and large samples and for some values of the scale and the extension of Jeffrey’s prior parameters using the simulation techniques. Results: It turns out that the maximum likelihood method and Bayesian method with Jeffrey’s prior result in smaller MSE compared to others in all cases. Conclusion: Based on the results of the simulation, the maximum likelihood method and Bayesian method with Jeffrey’s prior are found to be the best with respect to MSE.

Most estimators of the shape parameter of generalized Gaussian distribution (GGD) assume asymptotic case when there is available infinite number of observations, but in the real case, there is only available a set of limited size. The most popular estimator for the shape parameter, i.e., the maximum likelihood (ML) method, has a larger variance with a decreasing sample size. A very high value of variance for a very small sample size makes this estimation method very inaccurate. A new fast approximated method based on the standardized moment to overcome this limitation is introduced in the article. The relative mean square error (RMSE) was plotted for the range 0.3-3 of the shape parameter for comparison with other methods. The method does not require any root finding, any long look-up table or multi step approach, therefore it is suitable for real-time data processing