scholarly journals Composite Estimators for Forest Growth Derived from Symmetric, Varying-Length Observation Intervals

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 409
Author(s):  
Francis A. Roesch
Keyword(s):  
Linear Combination ◽  
Mean Squared Error ◽  
Observation Interval ◽  
Forest Growth ◽  
Interval Length ◽  
Population Parameter ◽  
Squared Error ◽  
Non Linear ◽  
Bias Ratio ◽  
Standard Set

Estimates of growth or change in a forest population parameter for a specific length of time, such as cubic meters of wood per hectare per year, are often made from sample observation intervals of different lengths of time. For instance, a basic building block of growth estimators in forest inventory systems is often the annual mean of the first differences of all observations for a particular year, regardless of observation interval length. The aggregate differences between successive observations on re-measured forest sample plots can be viewed as a linear combination, while forest growth is usually assumed to be non-linear. Bias can be assumed to exist whenever a linear combination is used to estimate a specific segment of an underlying non-linear trend. The amount of bias will depend upon the relationship of the intended estimation interval relative to the set of observation intervals. Here, three specific segments, relative to each year of interest, form the bases for a standard set of three estimands. Bias-ratio-adjusted composite estimators for use with observations made on alternative sets of symmetric interval lengths are compared in a simulation against this standard set of estimands. The first estimand has a one-year basis, the second has a five-year mid-interval basis, and the third has a five-year end-of-period basis. For the first and second bases, the initial results clearly show a logical ordering of bias and mean-squared error by observation interval length relative to the target interval length. As expected, some deviance from these clear trends are shown for the end-of-period basis. In the presence of three simple distributions of symmetric measurement intervals, the bias-ratio adjustments and subsequent composite estimators are shown to usually be effective in reducing bias and mean-squared error, while being most obviously effective for the most disparate distribution of intervals and for the end-of-period basis.

Microscope Volume Segmentation Improved through Non-Linear Restoration

2010 ◽  
Vol 1 (4) ◽  
pp. 37-46 ◽  
Author(s):  
Moacir P. Ponti
Keyword(s):  
Mean Squared Error ◽  
Imaging System ◽  
Volume Segmentation ◽  
K Value ◽  
Thresholding Method ◽  
Squared Error ◽  
Non Linear ◽  
Before And After ◽  
Restoration Method ◽  
Noise Statistics

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparison between the segmentation results before and after restoration is presented. The evaluation of the region segmentation included the root mean squared error and a normalized uniformity measure. Results showed significant improvement of segmentation when using the non-linear restoration method based on prior known information, such as the imaging system and the noise statistics.

scholarly journals Prediksi Lebar Tajuk Pohon Dominan pada Pertanaman Jati Asal Kebun Benih Klon di Kesatuan Pemangkuan Hutan Ngawi, Jawa Timur

2018 ◽  
Vol 12 (2) ◽  
pp. 127 ◽  
Author(s):  
Ronggo Sadono
Keyword(s):  
Forest Management ◽  
Standard Error ◽  
Mean Squared Error ◽  
Management Unit ◽  
Seed Orchards ◽  
Root Mean Squared Error ◽  
Crown Width ◽  
Squared Error ◽  
Non Linear ◽  
Teak Tree

Penelitian ini bertujuan untuk menentukan model perkembangan lebar tajuk pohon dominan jati asal Kebun Benih Klon pada tegakan berkualitas baik.Penelitian dilakukan di Kesatuan Pemangkuan Hutan Ngawi pada petak tanaman jati asal Kebun Benih Klon bertumbuhan baik pada umur 615 tahun. Petak tanaman bertumbuhan baik ditentukan berdasarkan kriteria persentase keberhasilan tanaman, rata-rata tinggi pohon dan rata-rata diameter batang serta aksesibilitasnya. Pada petak yang memenuhi syarat bertumbuhan baik dipilih sebanyak 30 sampel pohon dominan dan tiap sampel diukur radius tajuk pada empat arah mata angin. Hasil pengukuran radius tajuk digunakan untuk menghitung rata-rata radius tajuk sebagai rata-rata kuadratik 4 arah pengukuran radius tajuk dan lebar tajuk sebagai dua kali rata-rata radius tajuk. Rata-rata aritmatik dari lebar tajuk 30 pohon dominan tiap petak pengukuran digunakan sebagai variabel respons dan umur tegakan sebagai variabel prediktor. Data pengukuran selanjutnya dipilah menjadi dua bagian, yaitu sebagian besar untuk pengembangan model dan satu bagian lagi untuk validasi model. Analisis regresi non linear dengan metode kuadrat terkecil digunakan untuk memilih 4 kandidat model penduga rata-rata lebar tajuk, yaitu model Sigmoid, Power, Schumacher dan Gompertz. Pemilihan model didasarkan atas nilai koefisien determinasi tertinggi dan standard error of the estimate terkecil serta signifikansi uji F dan uji T. Akhirnya, model terbaik diuji kelayakannya dengan kriteria root mean squared error, simpangan agregatif dan simpangan relatif. Model Gompertz adalah model terbaik untuk memprediksi perkembangan rata-rata lebar tajuk pohon dominan, yang dapat dituliskan dengan persamaan:CW = 6,585 Xe-0,705xe-0,091sagedan dapat menjelaskan 79% variasi data. Model tersebut lolos validasi dan layak digunakan untuk memprediksi rata-rata lebar tajuk pohon dominan jati asal Kebun Benih Klon pada tegakan berkualitas baik umur 6 tahun sampai dengan umur 15 tahun di Kesatuan Pemangkuan Hutan Ngawi.Predicting Crown-width of Dominant Trees on Teak Plantation from Clonal Seed Orchards in Ngawi Forest Management Unit, East JavaAbstractThis study aims to determine the model of crown width development of the dominant teak tree planted using seeds from clonal seed orchards. The research was carried out in Ngawi Forest Management Unit on the good quality teak compartment having stands age from 6 to 15 years old. The good quality compartments were determined based on higher stand density, taller average tree height, larger average stem diameter, and good accessibility. In a well-qualified compartment, 30 samples of the dominant tree were selected and each sample was measured for the crown radius in the four radii. The measured crown radius was used to calculate average crown radius as a quadratic mean of 4-crown radii and crown width as double of average crown radius. The arithmetic mean of the crown width of the 30 dominant trees in each measured compartment was used as the response variable and stand age as the predictor variable. The measurement data were then sorted into two parts, namely: mostly for model fitting and the remaining for model validation. Non-linear regression analysis with the least squares method was used to evaluate 4 candidate models of average crown width, namely: Sigmoid, Power, Schumacher, and Gompertz models. The model selection was based on the highest coefficient of determination and the smallest standard error of the estimate and the significance of F test and T test. The best model was eventually validated using the following criteria : root mean squared error, aggregate deviation, and relative deviation. Gompertz model was the best model to predict the average crown width development of dominant teak tree and expressed as:CW = 6.585 Xe-0.705xe-0.091xageand able to explain 79% variation of data. The model was passed based on statistical validation and it was feasible for predicting the average of crown width of dominant teak tree from clonal seed orchards on good quality stand aged 6 to 15 years in Ngawi Forest Management Unit.

Minimax Shrunken Technique for Estimate Burr X Distribution Shape Parameter

2018 ◽  
pp. 484
Author(s):  
Abbas Najim Salman ◽  
Maymona M. Ameen ◽  
A. E. Abdul-Nabi
Keyword(s):  
Relative Efficiency ◽  
Prior Information ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
The Real ◽  
Squared Error ◽  
Distribution Shape ◽  
Original Estimate ◽  
Bias Ratio

      The present paper concern with minimax shrinkage estimator technique in order to estimate Burr X distribution shape parameter, when prior information about the real shape obtainable as original estimate while known scale parameter.  Derivation for Bias Ratio, Mean squared error and the Relative Efficiency equations.  Numerical results and conclusions for the expressions mentioned above were displayed. Comparisons for proposed estimator with most recent works were made.  

Microscope Volume Segmentation Improved through Non-Linear Restoration

2012 ◽  
pp. 344-353
Author(s):  
Moacir P. Ponti
Keyword(s):  
Mean Squared Error ◽  
Imaging System ◽  
Volume Segmentation ◽  
K Value ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Non Linear ◽  
Before And After ◽  
Restoration Method ◽  
Noise Statistics

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparison between the segmentation results before and after restoration is presented. The evaluation of the region segmentation included the root mean squared error and a normalized uniformity measure. Results showed significant improvement of segmentation when using the non-linear restoration method based on prior known information, such as the imaging system and the noise statistics.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

2024 ◽  
Vol 84 ◽  
Author(s):  
A. Yousafzai ◽  
W. Manzoor ◽  
G. Raza ◽  
T. Mahmood ◽  
F. Rehman ◽  
...  
Keyword(s):  
Climate Change ◽  
Mean Squared Error ◽  
Forest Growth ◽  
Growth And Yield ◽  
Silver Fir ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Yield Data ◽  
Root Mean Squared Error ◽  
Squared Error

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe’s (LM), Willmott’s index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

scholarly journals Variance Estimator Using Tri-mean and Inter Quartile Range of Auxiliary Variable

2017 ◽  
Vol 1 ◽  
pp. 83-91
Author(s):  
S.K. Yadav ◽  
Sheela Misra ◽  
S.S. Mishra ◽  
Shankar Prasad Khanal
Keyword(s):  
Mean Squared Error ◽  
Natural Populations ◽  
Auxiliary Variable ◽  
Population Parameter ◽  
Variance Estimator ◽  
Population Variance ◽  
Inter Quartile Range ◽  
First Order ◽  
Squared Error ◽  
Quartile Range

Background: Whenever the population is large and it is very time taking and costly to take observation on each unit of the population then sampling is the only way to get the appropriate estimate of the population parameter under consideration. Many authors have given many estimators for estimating population variance with greater efficiency.Objective: The objective of the study is to search for more efficient estimator than the competing estimators of population variance of study variable.Materials and Methods: The estimator utilizing information on tri-mean and inter quartile range of auxiliary variable has been is developed. The expressions for the bias and mean squared error (MSE) of the proposed estimator have been derived up to the first order of approximation. A theoretical comparison of the proposed estimator has been made with the competing estimators of population variance.Results: The theoretical findings have been justified with the help of numerical example from some natural populations. It has been found that the proposed estimator is best among the competing estimators of population variance as it has least mean squared error among them.Conclusion: Since the proposed estimator is best among the competing estimator of the population variance, therefore it must be used for the improved estimation of population variance.Nepalese Journal of Statistics, 2017, Vol. 1, 83-91

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

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