THE ANALYSIS OF TWO-PROFILE ENVIRONMENT GROUND CLUTTER STATISTICS MEASURED USING FORWARD SCATTER RADAR WITH VHF AND UHF BANDS

2016 ◽  
Vol 78 (7) ◽  
Author(s):  
N. N. Ismail ◽  
N. E. A. Rashid ◽  
Z. Ismail Khan ◽  
N. Ripin ◽  
M. F. Abdul Rashid
Keyword(s):  
Carrier Frequency ◽  
High Frequency ◽  
Goodness Of Fit ◽  
Weibull Model ◽  
Measured Signal ◽  
Very High Frequency ◽  
Chi Square ◽  
Distribution Method ◽  
Ground Clutter ◽  
Log Normal

In this paper, a FSR two-profile environment ground clutter-measured signal with very high frequency (VHF) and ultra high frequency (UFH) at a border of dense forest and free space area are presented. Statistical distribution method is used to model the clutter signal, namely Weibull, Gamma, Log-Logistic and Log-Normal distribution. Two goodness-of-fit (GOF) tests are used to calculate the error between the amplitude of the clutter data and the statistical model, which are the root mean square error (RMSE) and chi-square (CS). At the end of this analysis, Weibull model was found to be the best fit for 64 MHz clutter signal while Gamma model is best fitted at 151 MHz carrier frequency. Another model known as Log-Logistic model fits well to a clutter signal measured with 434 MHz carrier frequency.

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

scholarly journals Estimating the Best-Fitted Probability Distribution for Monthly Maximum Temperature at the Sylhet Station in Bangladesh

2021 ◽  
Vol 2 (2) ◽  
pp. 60-67
Author(s):  
Rashidul Hasan Rashidul Hasan
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Probability Model ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Anderson Darling ◽  
Log Normal

The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.

scholarly journals PEMILIHAN DISTRIBUSI PROBABILITAS PADA ANALISA HUJAN DENGAN METODE GOODNESS OF FIT TEST

2016 ◽  
Vol 18 (2) ◽  
pp. 139-148
Author(s):  
Togani Cahyadi Upomo ◽  
Rini Kusumawardani
Keyword(s):  
Normal Distribution ◽  
Goodness Of Fit ◽  
Gumbel Distribution ◽  
Rainfall Event ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Pearson Iii Distribution

Rainfall event is a stochastic process, so to explain and analyze this processes the probability theory and frequency analysisare used. There are four types of probability distributions.They are normal, log normal, log Pearson III and Gumbel. To find the best probabilities distribution, it will used goodness of fit test. The tests consist of chi-square and smirnov-kolmogorov. Results of the chi-square test for normal distribution, log normal and log Pearson III was 0.200, while for the Gumbel distribution was 2.333. Results of Smirnov Kolmogorov test for normal distribution D = 0.1554, log-normal distribution D = 0.1103, log Pearson III distribution D = 0.1177 and Gumbel distribution D = 0.095. All of the distribution can be accepted with a confidence level of 95%, but the best distribution is log normal distribution.Kejadian hujan merupakan proses stokastik, sehingga untuk keperluan analisa dan menjelaskan proses stokastik tersebut digunakan teori probabilitas dan analisa frekuensi. Terdapat empat jenis distribusi probabilitas yaitu distribusi normal, log normal, log pearson III dan gumbel. Untuk mencari distribusi probabilitas terbaik maka akan digunakan pengujian metode goodness of fit test. Pengujian tersebut meliputi uji chi-kuadrat dan uji smirnov kolmogorov. Hasil pengujian chi kuadrat untuk distribusi normal, log normal dan log pearson III adalah 0.200, sedangkan untuk distribusi gumbel 2.333. Hasil pengujian smirnov kolmogorov untuk distribusi normal dengan nilai D = 0.1554, distribusi log normal dengan nilai D = 0.1103, distribusi log pearson III dengan nilai D = 0.1177 dan distribusi gumbel dengan nilai D = 0.095. Seluruh distribusi dapat diterima dengan tingkat kepercayaan 95%, tetapi distribusi terbaik adalah distribusi log normal.

scholarly journals Clutter Distribution Analysis of A Tropical Foliage Clutter for Different Profiles Based on FSR Micro-Sensor Network

2018 ◽  
Vol 7 (3.7) ◽  
pp. 72
Author(s):  
Nur Alia Zulkifli ◽  
N E. Abd Rashid ◽  
Z I. Khan ◽  
N N. Ismail ◽  
R S. A. Raja Abdullah ◽  
...  
Keyword(s):  
High Frequency ◽  
Free Space ◽  
Distribution Model ◽  
Distribution Analysis ◽  
Distribution Models ◽  
Very High Frequency ◽  
Forward Scatter ◽  
Micro Sensor ◽  
Log Normal ◽  
Very High

Comparison of four different clutter profiles (border, seaside, free space and forest) using Forward Scatter Radar (FSR), which operates in Ultra-High and Very High Frequency (UHF and VHF) bands, is analyzed in this paper. Clutter levels ranging from low, medium, strong and very strong on each profile were studied. Based on the standard deviation of each clutter profile, border suits the best profile as the strongest clutter profile amidst seaside and free space, while the forest is determined as the lowest clutter profile. Subsequently, the characteristics of the clutter are investigated and compared based on the five distribution models (Log-Normal, Log-Logistic, Gamma, Weibull and Nakagami).  The parameters of the five distributions are evaluated using Root Mean Square Error (RMSE) in order to prove that the distribution model fits best to the clutter data. It can be concluded that Gamma distribution is the best distribution model for all cases of frequency bands and profiles.  

STATISTICAL ANALYSIS FOR TERENGGANU FORWARD SCATTER RADAR SEASIDE CLUTTER

2016 ◽  
Vol 78 (5-7) ◽  
Author(s):  
Nor Najwa Ismail ◽  
Nur Emileen Abd Rashid ◽  
Zuhani Ismail Khan
Keyword(s):  
Statistical Analysis ◽  
Weibull Distribution ◽  
High Frequency ◽  
Goodness Of Fit ◽  
Distribution Model ◽  
Logistic Distribution ◽  
Distribution Models ◽  
Very High Frequency ◽  
Forward Scatter ◽  
Scatter Radar

The statistical analysis for Terengganu, Malaysia seaside clutter is presented in this paper. The measured clutter data were collected using a prototype of forward scatter radar (FSR) micro-sensor network with very high frequency (VHF) and ultra-high frequency (UHF) bands. Four categories of clutter strength were recorded during the measurements, which are low, medium, strong and very strong clutter. The classes were divided according to the wind speed occurred during the measurements period. The analysis is to determine the best-fit distribution model for the measured clutter data. Four types of distribution models are used in this analysis, which are Weibull, Gamma, Log-Logistic and Log-Normal distribution. One of the goodness of fit (GOF) tests called root mean square error (RMSE) is used to prove which distribution is a better fit to the probability distribution of the measured clutter data. The obtained results show that for 64 MHz with all clutter level strength, Weibull distribution provides better fit and records the lowest RMSE. Weibull distribution also fits best to the clutter data for low clutter of 151 MHz. However, for the rest of clutter level strength for 151 MHz, Gamma distribution is the best-fitted model with lowest RMSE values. Log-Logistic distribution proves to be the best fitted model to all clutter level strength of clutter data for 434 MHz with smallest RMSE values.

scholarly journals A New Parametric Life Distribution with Modified Bagdonavičius–Nikulin Goodness-of-Fit Test for Censored Validation, Properties, Applications, and Different Estimation Methods

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 592 ◽  
Author(s):  
Mahmoud Mansour ◽  
Mahdi Rasekhi ◽  
Mohamed Ibrahim ◽  
Khaoula Aidi ◽  
Haitham M. Yousof ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Goodness Of Fit ◽  
Real Data ◽  
Weibull Model ◽  
Estimation Methods ◽  
Model Parameters ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Data Set ◽  
The Right

In this paper, we first study a new two parameter lifetime distribution. This distribution includes “monotone” and “non-monotone” hazard rate functions which are useful in lifetime data analysis and reliability. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Renyi entropy, δ-entropy, order statistics and probability weighted moments are derived. Non-Bayesian estimation methods such as the maximum likelihood, Cramer-Von-Mises, percentile estimation, and L-moments are used for estimating the model parameters. The importance and flexibility of the new distribution are illustrated by means of two applications to real data sets. Using the approach of the Bagdonavicius–Nikulin goodness-of-fit test for the right censored validation, we then propose and apply a modified chi-square goodness-of-fit test for the Burr X Weibull model. The modified goodness-of-fit statistics test is applied for the right censored real data set. Based on the censored maximum likelihood estimators on initial data, the modified goodness-of-fit test recovers the loss in information while the grouped data follows the chi-square distribution. The elements of the modified criteria tests are derived. A real data application is for validation under the uncensored scheme.

scholarly journals A Family of Bayesian Estimators for the Two-Parametric Burr Type II Distribution

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
R. Alshenawy ◽  
Navid Feroze ◽  
Ali Al-Alwan ◽  
Mahreen Saleem ◽  
Sahidul Islam
Keyword(s):  
Goodness Of Fit ◽  
Real Data ◽  
Information Criteria ◽  
Type Ii ◽  
Chi Square ◽  
Lifetime Distributions ◽  
Inverse Gamma ◽  
Kolmogorov Smirnov ◽  
Log Normal ◽  
Posterior Estimation

This study discusses the posterior estimation for the parameters of the Burr type II distribution (BIID). The informative and noninformative priors along with different loss functions have also been assumed for the posterior estimation. The applicability of the proposed distribution has also been discussed. The modeling capability of the proposed model has been compared with seven classes of the lifetime distributions using real data. The generalizations of Weibull, exponential, Rayleigh, gamma, log normal, Pareto, Maxwell, Levy, Laplace, inverse gamma, Gompertz, chi-square, inverse chi-square, half normal, and log-logistic distributions have been considered for the comparison. The comparison has been made based on different goodness-of-fit criteria, such as Akaike information criteria (AIC), Bayesian information criteria (BIC), and Kolmogorov-Smirnov (KS) test. Based on the results from the study, it can be suggested that the BIID can efficiently replace commonly used lifetime distributions and their modifications. The results under this model were comparable with different conventional/modified distributions having up to six parameters.

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