A logit analysis of private woodlot owner's harvesting decisions in New Brunswick

1988 ◽  
Vol 18 (3) ◽  
pp. 330-336 ◽  
Author(s):  
M. S. Jamnick ◽  
D. R. Beckett
Keyword(s):  
Maximum Likelihood ◽  
New Brunswick ◽  
Logit Model ◽  
Screening Tool ◽  
Logit Analysis ◽  
Independent Data ◽  
Data Set ◽  
Extension Personnel

A maximum likelihood logit model of New Brunswick's private woodlot owner's harvesting decisions indicates that these decisions are related to owner and ownership characteristics. The model was able to correctly classify 75.2% of the observations from the data used to construct the model and 75.7% of the observations from an independent data set. Examples of how the model can be used to develop policies to encourage or discourage harvesting or as a screening tool for forest extension personnel are discussed.

Properties and methods of estimation for a bivariate exponentiated Fréchet distribution

2020 ◽  
Vol 70 (5) ◽  
pp. 1211-1230
Author(s):  
Abdus Saboor ◽  
Hassan S. Bakouch ◽  
Fernando A. Moala ◽  
Sheraz Hussain
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Superior Performance ◽  
Estimation Methods ◽  
Conditional Probability Density ◽  
Data Set ◽  
Fréchet Distribution ◽  
Frechet Distribution

AbstractIn this paper, a bivariate extension of exponentiated Fréchet distribution is introduced, namely a bivariate exponentiated Fréchet (BvEF) distribution whose marginals are univariate exponentiated Fréchet distribution. Several properties of the proposed distribution are discussed, such as the joint survival function, joint probability density function, marginal probability density function, conditional probability density function, moments, marginal and bivariate moment generating functions. Moreover, the proposed distribution is obtained by the Marshall-Olkin survival copula. Estimation of the parameters is investigated by the maximum likelihood with the observed information matrix. In addition to the maximum likelihood estimation method, we consider the Bayesian inference and least square estimation and compare these three methodologies for the BvEF. A simulation study is carried out to compare the performance of the estimators by the presented estimation methods. The proposed bivariate distribution with other related bivariate distributions are fitted to a real-life paired data set. It is shown that, the BvEF distribution has a superior performance among the compared distributions using several tests of goodness–of–fit.

scholarly journals On the Estimation of Reliability of Weighted Weibull Distribution: A Comparative Study

2016 ◽  
Vol 5 (4) ◽  
pp. 1
Author(s):  
Bander Al-Zahrani
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Real Data ◽  
Reliability Function ◽  
Nonparametric Methods ◽  
Empirical Method ◽  
Density Estimator ◽  
Bayes Estimators ◽  
Unknown Parameters ◽  
Data Set

The paper gives a description of estimation for the reliability function of weighted Weibull distribution. The maximum likelihood estimators for the unknown parameters are obtained. Nonparametric methods such as empirical method, kernel density estimator and a modified shrinkage estimator are provided. The Markov chain Monte Carlo method is used to compute the Bayes estimators assuming gamma and Jeffrey priors. The performance of the maximum likelihood, nonparametric methods and Bayesian estimators is assessed through a real data set.

scholarly journals Maximum Likelihood Estimation of the VAR(1) Model Parameters with Missing Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Helena Mouriño ◽  
Maria Isabel Barão
Keyword(s):  
Stochastic Process ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Moving Average ◽  
Practical Importance ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Missing Observations ◽  
Data Set ◽  
The Impact

Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.

scholarly journals Virologic Response to Lopinavir-Ritonavir-Based Antiretroviral Regimens in a Multicenter International Clinical Cohort: Comparison of Genotypic Interpretation Scores

2008 ◽  
Vol 52 (11) ◽  
pp. 4050-4056 ◽  
Author(s):  
Philip Grant ◽  
Eric C. Wong ◽  
Richard Rode ◽  
Robert Shafer ◽  
Andrea De Luca ◽  
...  
Keyword(s):  
Viral Load ◽  
Multivariate Analyses ◽  
Virologic Response ◽  
Receiver Operator Characteristic ◽  
Independent Data ◽  
Data Set ◽  
Clinical Cohort ◽  
Multicenter Cohort ◽  
Mutation Score ◽  
Clinical Breakpoints

ABSTRACT Several genotypic interpretation scores have been proposed for the evaluation of susceptibility to lopinavir/ritonavir (LPV/r) but have not been compared using an independent data set. This study was a retrospective multicenter cohort of patients initiating LPV/r-based therapy. The virologic response (VR) was defined as a viral load of <500 copies/ml at week 24. The genotypic interpretation scores surveyed were the LPV mutation score, the ViroLogic score, the ATU score, the Stanford database score, and the International AIDS Society-USA mutation list. Of the 103 patients included in the analysis, 76% achieved VR at 24 weeks. For scores with clinical breakpoints defined (LPV mutation, ATU, ViroLogic, and Stanford), over 80% of the patients below the breakpoints achieved VR, while 50% or less above the breakpoints responded. Protease mutations at positions 10, 54, and 82 and at positions 54, 84, and 90 were associated with a lack of VR in the univariate and multivariate analyses, respectively. The area under the receiver-operator characteristic curves for the five genotypic interpretation scores studied ranged from 0.73 to 0.76. The study confirms that the currently available genotypic interpretation scores which are widely used by clinicians performed similarly well and can be effectively used to predict the virologic activity of LPV/r in treatment-experienced patients.

Utilizarea teoriei valorilor extreme în climatologie

2019 ◽  
Author(s):  
Valentin Raileanu ◽  
Keyword(s):  
Maximum Likelihood ◽  
Extreme Values ◽  
Probability Distributions ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
R Software ◽  
Data Set ◽  
Data Format ◽  
Generalized Pareto ◽  
Distribution Parameters

The article briefly describes the history and fields of application of the theory of extreme values, including climatology. The data format, the Generalized Extreme Value (GEV) probability distributions with Bock Maxima, the Generalized Pareto (GP) distributions with Point of Threshold (POT) and the analysis methods are presented. Estimating the distribution parameters is done using the Maximum Likelihood Estimation (MLE) method. Free R software installation, the minimum set of required commands and the GUI in2extRemes graphical package are described. As an example, the results of the GEV analysis of a simulated data set in in2extRemes are presented.

Estimation of P(X

2017 ◽  
Vol 34 (7) ◽  
pp. 1111-1122 ◽  
Author(s):  
Soumya Roy ◽  
Biswabrata Pradhan ◽  
E.V. Gijo
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Quality Characteristic ◽  
Logistic Distribution ◽  
Type Ii ◽  
Finite Sample ◽  
Data Set ◽  
Content Type ◽  
Type Ii Censored Data ◽  
Interval Estimators

Purpose The purpose of this paper is to compare various methods of estimation of P(X<Y) based on Type-II censored data, where X and Y represent a quality characteristic of interest for two groups. Design/methodology/approach This paper assumes that both X and Y are independently distributed generalized half logistic random variables. The maximum likelihood estimator and the uniformly minimum variance unbiased estimator of R are obtained based on Type-II censored data. An exact 95 percent maximum likelihood estimate-based confidence interval for R is also provided. Next, various Bayesian point and interval estimators are obtained using both the subjective and non-informative priors. A real life data set is analyzed for illustration. Findings The performance of various point and interval estimators is judged through a detailed simulation study. The finite sample properties of the estimators are found to be satisfactory. It is observed that the posterior mean marginally outperform other estimators with respect to the mean squared error even under the non-informative prior. Originality/value The proposed methodology can be used for comparing two groups with respect to a suitable quality characteristic of interest. It can also be applied for estimation of the stress-strength reliability, which is of particular interest to the reliability engineers.

Predicting lean meat yield in beef cattle using ultrasonic muscle depth and width measurements

2003 ◽  
Vol 83 (3) ◽  
pp. 429-434 ◽  
Author(s):  
R. Bergen ◽  
D. H. Crews ◽  
Jr., S. P. Miller ◽  
J. J. McKinnon
Keyword(s):  
Beef Cattle ◽  
Carcass Composition ◽  
Ultrasound Measurement ◽  
Independent Data ◽  
Data Set ◽  
Meat Yield ◽  
Lean Meat ◽  
Ultrasound Measurements ◽  
Width Measurements ◽  
Highly Correlated

The value of live ultrasound longissimus dorsi depth and width measurements as predictors of estimated carcass lean meat yield of steers (CARLEAN-S) and bulls (CARLEAN-B) was studied. In trial 1, equations were developed to predict estimated lean meat yield of steers (n = 116) from carcass weight (Eq. 1) or liveweight (Eq. 2), fat depth and l. dorsi area or liveweight, fat depth and l. dorsi depth × width (Eq. 3). Equation 1 was most precise (RSD = 25.6 g kg-1), followed by Eq. 2 (RSD = 27.8g kg-1) and Eq. 3 (RSD = 30.2g kg-1). Equations 2 and 3 predicted CARLEAN-S with similar accuracy (SEP = 23.8 vs. 24.9 g kg-1, respectively) and were highly correlated with each other (r = 0.89) in an independent data set (n = 118). Repeatability and accuracy of pre-slaughter l. dorsi depth and width measurements were studied in yearling bulls (trial 2; n = 191). When ultrasound measurements were expressed as a percentage of the average ultrasound measurement, repeatabilities of l. dorsi depth (SER = 6.2 to 7.8%) and width (SER = 4.2 to 6.1%) measurements were similar to fat depth and l. dorsi area measurements (SER = 17.9 and 4.5%, respectively). When ultrasound measurements were compared to the corresponding carcass measurements, l. dorsi depth (SEP = 10.3 to 13.9%) and width (SEP = 6.7 to 8.5%) measurements were as accurate as fat depth and l. dorsi area measurements (SEP = 32.9 and 8.4%, respectively). Equations were developed to predict CARLEAN-B of yearling bulls (n = 82) from liveweight, 12th rib ultrasound fat depth and either l. dorsi depth × width measurements (Eqs. 4 and 5) or two l. dorsi depth measurements (Eq. 6). All equations had similar precision (RSD = 19.4 to 19.5 g kg-1) and predicted CARLEAN-B similarly (SEP = 25.0, 24.6 and 26.1g kg-1 for Eqs. 4, 5 and 6, respectively) in an independent data set (n = 109). All equations were highly correlated (r ≥0.97) with an equation using ultrasound fat depth and l. dorsi area in the independent data set. Longissimus muscle depth and width measurements were as valuable as l. dorsi area for predicting carcass composition of yearling beef bulls in the present study. Key words: Ultrasound, beef cattle, carcass traits

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