scholarly journals AJUSTE DA FUNÇÃO DE DISTRIBUIÇÃO DIAMÉTRICA WEIBULL POR PLANILHA ELETRÔNICA

FLORESTA ◽  
2011 ◽  
Vol 41 (2) ◽  
Author(s):  
William Thomaz Wendling ◽  
Dartagnan Baggio Emerenciano ◽  
Roberto Tuyoshi Hosokawa
Keyword(s):  
Distribution Function ◽  
Goodness Of Fit ◽  
Diameter Distribution ◽  
Goodness Of Fit Test ◽  
Function Model ◽  
Helpful Tool ◽  
Test Efficiency ◽  
Kolmogorov Smirnov ◽  
Electronic Spreadsheet ◽  
Main Model

Desenvolve-se uma metodologia traçada por um roteiro em algoritmo factível e amigável para efetivação em planilhas eletrônicas, reconhecidas como uma interface popular para cálculos. Busca-se, assim, apresentar uma ferramenta útil para alunos de graduação e recém-graduados em engenharia florestal, ou engenheiros mais experientes que ainda não dominem a técnica, para ajuste de um modelo de função densidade de probabilidade, com o objetivo de descrever a estrutura da distribuição diamétrica de populações florestais. O modelo adotado é o da função de Weibull, e o método de ajuste é o do percentis, com simulações comparadas por teste de aderência de Kolmogorov-Smirnov. A eficiência do método apresentado é testada por comparação a outro método alternativo.Palavras-chave:  Manejo florestal; florestas - modelos matemáticos; florestas - simulação por computador. AbstractWeibull diameter distribution function adjusts for electronic spreadsheet. This research develops a methodology based on easy and friendly algorithm for spreadsheets, a well known interface for calculus. It aims to present a helpful tool for forestry students, as well as for newly or experienced engineers who haven’t already known adjustment techniques for a density function model of probability, which is useful into diametric distribution structure descriptions of forest population. It has Weibull’s function as main model, percentile as adjustment method, and comparing simulations by Kolmogorov-Smirnov goodness-of-fit test. Efficiency of the presented method was tested by comparison to another method.Keywords: Forest management; forest - mathematical models; forest - computer simulator.

scholarly journals Probability density functions for description of diameter distribution in thinned stands of Tectona grandis

CERNE ◽  
2012 ◽  
Vol 18 (2) ◽  
pp. 185-196 ◽  
Author(s):  
Daniel Henrique Breda Binoti ◽  
Mayra Luiza Marques da Silva Binoti ◽  
Helio Garcia Leite ◽  
Leonardo Fardin ◽  
Julianne de Castro Oliveira
Keyword(s):  
Fatigue Life ◽  
Goodness Of Fit ◽  
Basal Area ◽  
Tectona Grandis ◽  
Diameter Distribution ◽  
Permanent Plots ◽  
Weibull Function ◽  
Goodness Of Fit Test ◽  
Mato Grosso ◽  
Kolmogorov Smirnov

The objective of this study was to evaluate the effectiveness of fatigue life, Frechet, Gamma, Generalized Gamma, Generalized Logistic, Log-logistic, Nakagami, Beta, Burr, Dagum, Weibull and Hyperbolic distributions in describing diameter distribution in teak stands subjected to thinning at different ages. Data used in this study originated from 238 rectangular permanent plots 490 m² in size, installed in stands of Tectona grandis L. f. in Mato Grosso state, Brazil. The plots were measured at ages 34, 43, 55, 68, 81, 82, 92, 104, 105, 120, 134 and 145 months on average. Thinning was done in two occasions: the first was systematic at age 81months, with a basal area intensity of 36%, while the second was selective at age 104 months on average and removed poorer trees, reducing basal area by 30%. Fittings were assessed by the Kolmogorov-Smirnov goodness-of-fit test. The Log-logistic (3P), Burr (3P), Hyperbolic (3P), Burr (4P), Weibull (3P), Hyperbolic (2P), Fatigue Life (3P) and Nakagami functions provided more satisfactory values for the k-s test than the more commonly used Weibull function.

scholarly journals Modeling Diameter Distribution of Black Alder (Alnus glutinosa (L.) Gaertn.) Stands in Poland

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 412 ◽  
Author(s):  
Piotr Pogoda ◽  
Wojciech Ochał ◽  
Stanisław Orzeł
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Alnus Glutinosa ◽  
Weibull Model ◽  
Diameter Distribution ◽  
Stand Age ◽  
Weibull Function ◽  
Black Alder ◽  
Kolmogorov Smirnov ◽  
Non Parametric

We present diameter distribution models for black alder (Alnus glutinosa (L.) Gaertn.) derived from diameter measurements made at breast height in 844 circular sample plots set in 163 managed stands located in south-eastern Poland. A total of 22,530 trees were measured. Stand age ranged from six to 89 years. The model formulation was based on the two-parameter Weibull function and a non-parametric percentile-based method. Weibull function parameters were recovered from the first raw and second central moments estimated using the stand quadratic mean diameter. The same stand characteristic was used to predict values of 12 percentiles in the percentile-based method. The model performance was assessed using the k-fold cross-validation method. The goodness-of-fit statistics include the Kolmogorov–Smirnov statistic, mean error, root mean squared error, and two variants of the error index introduced by Reynolds. The percentile model developed, accurately predicted diameter distributions in 88.4% of black alder stands, as compared to 81.9% for the Weibull model (Kolmogorov–Smirnov test). Alternative statistical metrics assessing goodness-of-fit to empirical distributions suggested that the non-parametric percentile model was superior to the parametric Weibull model, especially in stands older than 20 years. In younger stands, the two models were accurate only in 57% of the cases, and did not differ significantly with respect to goodness-of-fit measures.

scholarly journals Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Emission Characteristics ◽  
Goodness Of Fit Test ◽  
Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

Utilizing Simulation to Build a Retail Store Inventory Optimization Model

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
VICENTE SALVADOR E. MONTAÑO ◽  
MICHAEL E. CARTER II
Keyword(s):  
Simulation Model ◽  
Goodness Of Fit ◽  
Inventory Model ◽  
T Test ◽  
Optimal Order ◽  
Order Quantity ◽  
Goodness Of Fit Test ◽  
Reorder Point ◽  
Kolmogorov Smirnov ◽  
Optimal Order Quantity

The researchers build an inventory model for retail stores by validating their economicorder quantity through data driven simulation. This paper created an inventoryoptimization model for a personal care retailing business, to avoid stock out and minimize their holding cost and ordering cost. Simulating a thousand different scenarios, the research come up with an optimal inventory model for the two most sellable products in the store. The t-test reveals that product A has a significantly higher demand than product B. The simulation model validates the optimal order quantity of 59 units, with a reorder point of 25 units for product A. However, the simulation model recommends an optimal order quantity of 37 units and a reorder point of 10 units for product B. The Kolmogorov-Smirnov Goodness of Fit Test reveals the normal distribution of the 30 days inventory for Product A but not for Product B. Confirming that stocks out will unlikely happen for product A but will probably occur for product B. The model confirms EOQ findings of product with relatively high demand but low price but a departure for products with low demand but the high price.Keywords: Operations management, retail inventory system, t-test, Monte Carlo Simulation,Kolmogorov-Smirnov Goodness of Fit Test, Davao City, Philippines, Southeast Asia

scholarly journals A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality

2007 ◽  
Vol 135 (3) ◽  
pp. 1151-1157 ◽  
Author(s):  
Dag J. Steinskog ◽  
Dag B. Tjøstheim ◽  
Nils G. Kvamstø
Keyword(s):  
Goodness Of Fit ◽  
Small Sample ◽  
Power Comparison ◽  
Goodness Of Fit Test ◽  
Kolmogorov Smirnov ◽  
The Mean ◽  
Small Sample Problem ◽  
Tests For Normality ◽  
Test For Normality ◽  
Smirnov Test

Abstract The Kolmogorov–Smirnov goodness-of-fit test is used in many applications for testing normality in climate research. This note shows that the test usually leads to systematic and drastic errors. When the mean and the standard deviation are estimated, it is much too conservative in the sense that its p values are strongly biased upward. One may think that this is a small sample problem, but it is not. There is a correction of the Kolmogorov–Smirnov test by Lilliefors, which is in fact sometimes confused with the original Kolmogorov–Smirnov test. Both the Jarque–Bera and the Shapiro–Wilk tests for normality are good alternatives to the Kolmogorov–Smirnov test. A power comparison of eight different tests has been undertaken, favoring the Jarque–Bera and the Shapiro–Wilk tests. The Jarque–Bera and the Kolmogorov–Smirnov tests are also applied to a monthly mean dataset of geopotential height at 500 hPa. The two tests give very different results and illustrate the danger of using the Kolmogorov–Smirnov test.

scholarly journals COMPARAÇÃO DE DIFERENTES ABORDAGENS NA MODELAGEM DA DISTRIBUIÇÃO DIAMÉTRICA

FLORESTA ◽  
2010 ◽  
Vol 40 (4) ◽  
Author(s):  
Thelma Shirlen Soares ◽  
Hélio Garcia Leite ◽  
Carlos Pedro Boechat Soares ◽  
Antonio Bartolomeu do Vale
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Model Evaluation ◽  
Probability Distribution Function ◽  
Diameter Distribution ◽  
Weibull Function ◽  
Function Model ◽  
Distribution Modeling ◽  
The Right ◽  
Invariant Procedure

O objetivo deste estudo foi avaliar a eficiência da função de distribuição de probabilidade Weibull truncada à direita em relação ao procedimento de passo invariante baseado na relação de percentis da distribuição diamétrica. Verificou-se que o modelo de passo invariante apresenta ajustes e predições mais precisas quando comparado com o procedimento tradicional, sendo mais eficiente.Palavras-chave:Passo invariante; função Weibull; avaliação de modelo. AbstractComparison of different approaches to diameter distribution modeling. This study evaluated the efficiency of the Weibull probability distribution function truncated to the right in comparison with the step-invariant procedure to characterize the percentiles of the diameter distribution. The results indicated that the step-invariant procedure provides more accurate adjustments and predictions and is more efficient than the traditional procedure.Keywords: Step invariant; Weibull function; model evaluation.

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