• Journal of Forest and Environmental Science
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• 제32권3호
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• pp.280-290
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• 2016

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

• Korean Journal of Agricultural and Forest Meteorology
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• 제16권4호
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Animal Bioscience
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• 제35권5호
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• pp.648-658
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• 2022

Objective: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. Methods: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piaui state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R²), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. Results: The von Bertalanffy model was the best fitting model for BW according to the highest value of R² (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). Conclusion: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

• Journal of Ecology and Environment
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• 제48권2호
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• pp.196-206
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• 2024

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R² and adjusted [adj]-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R² ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH₁₀), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R² varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH₁₀ as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB_Stem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGB_Branch = exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGB_Foliage = exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGB_Total = exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• The Korean Journal of Applied Statistics
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• 제23권6호
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• pp.1169-1178
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• 2010

This article consider a performance model selection based on AIC under unbalanced deign in linear mixed effect models. Vaida and Balanchard (2005) proposed conditional AIC for model selection in linear mixed effect models when the prediction of random effects is of primary interest. Theoretical properties of cAIC and related criteria have been investigated by Liang et al. (2008) and Greven and Kneib (2010). However, all of the simulation studies were performed under a balanced design. Even though functional form of AIC remain same even under the unbalanced deign, it is worthwhile to investigate performance of AIC based model selection criteria under the unbalanced design. The simulation study in this article shows how unbalancedness affects model selection in linear mixed effect models.

• Asian-Australasian Journal of Animal Sciences
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• 제12권3호
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• pp.331-335
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• 1999

The present study describes the analysis of the multiphasic growth function (MGF) to body weight in laboratory and wild mice. Three genetic groups of laboratory mice (Mus musculus domesticus) designated $CF_{{\sharp}1}$, C3H/HeNCrj and C57BL/6NCrj, and a genetic group of Yonakuni wild mice (Mus musculus molossinus yonakuni, Yk) were used. Mean body weights of each genetic group-sex subclass from birth to 69 days of age taken at 3-day intervals were analyzed by a monophasic, diphasic and triphasic functions for describing growth patterns. A comparison among the three functions of the MGF was based on the goodness-of-fit criteria: residual standard deviation (RSD), adjusted R-square (Adj $R^2$) and Akaike's information criterion (AIC). Result of this study indicated that body weight averaged heavier for males than for females. Among the four genetic groups within both sexes, $CF_{{\sharp}1}$ showed the highest, subsequent followed by C3H/HeNCrj, C57BL/6NCrj and Yk. Comparison among the three functions revealed that the triphasic function was the best fit to growth data, with the lowest RSD, the highest Adj $R^2$ and the lowest AIC, for the four genetic groups. For the triphasic function, RSD within each genetic group-sex subclass was similar for males and females. Adj $R^2$ was 0.999 for all genetic group-sex subclasses. AIC for laboratory mice males and females ranged from -70.48 to 66.50 and from -92.81 to -68.64, respectively; whereas for Yk wild mice males was -74.29 and females -78.42.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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• pp.6575-6579
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• 2015

Background: Breast cancer (BC) is the most frequent malignancy among females and is a leading cause of death of middle-aged women. Herein, we evaluated baseline characteristics for BC patients and also compared these variables across ealry and late recurrence groups. Materials and Methods: Between 1995 to 2014, among female breast cancer patients referred to our oncology clinic, eighty-six were entered into our study. All had distant metastasis. Early recurrence was defined as initial recurrence within 5 years following curative surgery irrespective of site. Likewise, late recurrence was defined as initial recurrence after 5 years. No recurrence was defined for survivors to a complete minimum of 10 years follow-up. Significant prognostic factors associated with early or late recurrence were selected according to the Akaike Information Criterion. Results: The median follow-up was 9 years (range, 1-18 years). During follow-up period, 51 recurrences occurred (distant metastasis), 31 early and 20 late. According to the site of recurrence, there were 51 distant. In this follow-up period, 19 patients died. Compared with the early recurrence group, the no recurrence group had lower lymph node involvement and more p53 positive lesions but the late recurrence group had lower tumor size. In comparison to no recurrence, p53 (odds ratio [OR] 6.94, 95% CI 1.49-32.16) was a significant prognostic factor for early recurrence within 5 years. Conclusions: Tumor size, p53 and LN metastasis are the most important risk factors for distance recurrence especially in early recurrence and also between of them, p53 is significant prognostic factor for early recurrence.

• Asian Pacific Journal of Cancer Prevention
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• 제17권sup3호
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• pp.311-316
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• 2016

Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (${\pm}11.9$) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.

• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.1829-1831
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• 2012

Background: The generalized gamma distribution statistics constitute an extensive family that contains nearly all of the most commonly used distributions including the exponential, Weibull and log normal. A saturated version of the model allows covariates having effects through all the parameters of survival time distribution. Accelerated failure-time models assume that only one parameter of the distribution depends on the covariates. Methods: We fitted both the conventional GG model and the saturated form for each of its members including the Weibull and lognormal distribution; and compared them using likelihood ratios. To compare the selected parameter distribution with log logistic distribution which is a famous distribution in survival analysis that is not included in generalized gamma family, we used the Akaike information criterion (AIC; r=l(b)-2p). All models were fitted using data for 369 women age 50 years or more, diagnosed with stage IV breast cancer in BC during 1990-1999 and followed to 2010. Results: In both conventional and saturated parametric models, the lognormal was the best candidate among the GG family members; also, the lognormal fitted better than log-logistic distribution. By the conventional GG model, the variables "surgery", "radiotherapy", "hormone therapy", "erposneg" and interaction between "hormone therapy" and "erposneg" are significant. In the AFT model, we estimated the relative time for these variables. By the saturated GG model, similar significant variables are selected. Estimating the relative times in different percentiles of extended model illustrate the pattern in which the relative survival time change during the time. Conclusions: The advantage of using the generalized gamma distribution is that it facilitates estimating a model with improved fit over the standard Weibull or lognormal distributions. Alternatively, the generalized F family of distributions might be considered, of which the generalized gamma distribution is a member and also includes the commonly used log-logistic distribution.