Two-parameter Statistical Models Research Articles

Assessment of Wind Resource and Its Energy Potential in Three States of Northwest Nigeria

The growing need for clean energy to address the environmental issues caused by the usage of fossil fuels results to the need for Nigeria to develop an alternative energy from untapped wind which is in abundance in the region. The aim of this study is to evaluate wind power potential and wind energy resources of three locations (Kaduna, Kano and Katsina) of northwestern part of Nigeria. The Nigerian Meteorological agency (NIMET) provided the wind speed data spanning 25 years (1996-2020). In the analysis, Weibull two-parameter statistical model was used. The distribution of wind speed across Nigeria demonstrates that some areas in the North are equipped to generate wind. Microsoft excel was used in analyzing the wind speed data obtained. In addition, evaluation of the region’s wind energy resources shows that Kano recorded the highest potential, with WPD of 443.03 Wm-2 at 10m AGL with annual WED of 4.921 kWhm-2day-1 while Kaduna recorded the lowest potential of 198.43 Wm-2 with annual WED of 2.093 kWhm-2day-1. As a result, Kaduna is found to be ideal for small scale wind power generation, while Kano and Katsina may be suitable for large scale wind power generation.

Survival Analysis of the PRC Model from Adaptive Progressively Hybrid Type-II Censoring and Its Engineering Applications

A new two-parameter statistical model, obtained by compounding the generalized-exponential and exponential distributions, called the PRC lifetime model, is explored in this paper. This model can be easily linked to other well-known six-lifetime models; namely the exponential, log-logistic, Burr, Pareto and generalized Pareto models. Adaptive progressively hybrid Type-II censored strategy, used to increase the efficiency of statistical inferential results and save the total duration of a test, has become widely used in various sectors such as medicine, biology, engineering, etc. Via maximum likelihood and Bayes inferential methodologies, given the presence of such censored data, the challenge of estimating the unknown parameters and some reliability time features, such as reliability and failure rate functions, of the PRC model is examined. The Markov-Chain Monte Carlo sampler, when the model parameters are assumed to have independent gamma density priors, is utilized to produce the Bayes’ infer under the symmetric (squared-error) loss of all unknown subjects. Asymptotic confidence intervals as well as the highest posterior density intervals of the unknown parameters and the unknown reliability indices are also created. An extensive Monte Carlo simulation is implemented to investigate the accuracy of the acquired point and interval estimators. Four various optimality criteria, to select the best progressive censored design, are used. To demonstrate the applicability and feasibility of the proposed model in a real-world scenario, two data sets from the engineering sector; one based on industrial devices and the other on aircraft windshield, are analyzed. Numerical evaluations showed that the PRC model furnishes a superior fit compared to seven other models in the literature, including: alpha-power exponential, log-logistic, Nadarajah–Haghighi, generalized-exponential, Weibull, gamma and exponential lifetime distributions. The findings demonstrate that, in order to obtain the necessary estimators, the Bayes’ paradigm via Metropolis–Hastings sampler is recommended compared to its competitive likelihood approach.

A new method for studying the evolution of particle breakage

This paper proposes a simple method for studying the evolution of particle breakage. A two-parameter statistical model is first proposed to describe the distribution of new particles generated from breakage of an initially uniformly graded sample. A Markov chain model is then proposed to describe the particle size evolution of non-uniformly graded samples. The two-parameter statistical model is shown to be able to capture different breakage patterns such as asperity breakage and particle splitting observed during breakage of uniformly graded samples. In the Markov chain model, it is shown that the concept of ‘effective breakage probability’, proposed to define the relative breakage extent of the whole sample, is comparable to a well-established breakage index in the literature. Both models are successfully validated against experimental data, where the characteristics of the model parameters and the engineering implications are discussed.

Quality and Statistical Classification of Brazilian Vegetable Oils Using Mid-Infrared and Raman Spectroscopy

Palm oil, soy oil, sunflower oil, corn oil, castor oil, and rapeseed oil were analyzed with Fourier transform infrared (FT-IR) and FT-Raman spectroscopy. The quality of different oils was evaluated and statistically classified by principal component analysis (PCA) and a partial least squares (PLS) regression model. First, a calibration set of spectra was selected from one sampling batch. The qualitative variations in spectra are discussed with a prediction of oil composition (saturated, mono- and polyunsaturated fatty acids) from mid-infrared analysis and iodine value from FT-Raman analysis, based on ratioing the intensity of bands at given wavenumbers. A more robust and convincing oil classification is obtained from two-parameter statistical models. The statistical analysis of FT-Raman spectra favorably distinguishes according to the iodine value, while the mid-infrared spectra are most sensitive to hydroxyl moieties. Second, the models are validated with a set of spectra from another sampling batch, including the same oil types as-received and after different aging times together with a hydrogenated castor oil and high-oleic sunflower oil. There is very good agreement between the model predictions and the Raman measurements, but the statistical significance is lower for mid-infrared spectra. In the future, this calibration model will be used to check vegetable oil qualities before using them in polymerization processes.

Fractal characteristics of characteristic parameter statistical distributions of typical random signals

A two-parameter statistical model of the pulsed signals which can be used to describe the basic structural characteristics of the random signals is established in this paper. As a result, the random signal counting distributions of amplitude q(lV) and width p(l), as well as the different signal subset counting distributions under given width l(lV) and amplitude lV(l), all match well with the form of the lognormal distribution with a natural numberused as an independent variable. Besides, it is also found that there are internal relations between the counting distribution statistical characteristic parameters lnV, ln, lnV, ln and the corresponding signal characteristic parameters lV and l. In essence, these relations are the representations of random signal fractal characteristics. In a word, two characteristic parameter statistical distributions of the random signals each have a non-integral dimensional fractal feature.

Challenges Associated with Heterologous Expression of Flavobacterium psychrophilum Proteins in Escherichia coli

A two-parameter statistical model was used to predict the solubility of 96 putative virulence-associated proteins of Flavobacterium psychrophilum (CSF259-93) upon over expression in Escherichia coli. This analysis indicated that 88.5% of the F. psychrophilum proteins would be expressed as insoluble aggregates (inclusion bodies). These solubility predictions were verified experimentally by colony filtration blot for six different F. psychrophilum proteins. A comprehensive analysis of codon usage identified over a dozen codons that are used frequently in F. psychrophilum, but that are rarely used in E. coli. Expression of F. psychrophilum proteins in E. coli was often associated with production of minor molecular weight products, presumably because of the codon usage bias between these two organisms. Expression of recombinant protein in the presence of rare tRNA genes resulted in marginal improvements in the expressed products. Consequently, Vibrio parahaemolyticus was developed as an alternative expression host because its codon usage is similar to F. psychrophilum. A full-length recombinant F. psychrophilum hemolysin was successfully expressed and purified from V. parahaemolyticus in soluble form, whereas this protein was insoluble upon expression in E. coli. We show that V. parahaemolyticus can be used as an alternate heterologous expression system that can remedy challenges associated with expression and production of F. psychrophilum recombinant proteins.

A note on the harmonic law: A two-parameter family of distributions for ratios

Consider two-parameter statistical models for positive continuous observations. Suppose that these models are closed under change of scale and under reciprocals, properties that are very appropriate when the observations are ratios of positive magnitudes. Additionally, suppose that the maximum likelihood estimator of the population mean is the sample mean (Gauss's principle). Surprisingly, only one statistical model satisfies these three properties and this is a special case of the generalized inverse gaussian family of distributions known as Harmonic Law.

Invariant Exponential Models Applied to Reliability Theory and Survival Analysis

For a two-parameter exponential model with increasing failure rate (IFR) or decreasing failure rate (DFR) distributions necessary and sufficient conditions of the existence of a solution of the likelihood equations are given. Also, all of the scale-invariant two-parameter statistical models closed by raising to a power and by exponential tilting are introduced. The conditions of existence of a solution of the likelihood equations are studied for these invariant models, and the models are applied to obtain some uniformly most powerful unbiased tests of exponentially against alternatives in these models.

Evaluation of fractional, lower-order statistics-based detection algorithms on real radar sea-clutter data

Alpha-stable distributions have recently been recognised in the signal processing community as simple, yet accurate, two-parameter statistical models for signals and noises that contain an impulsive component of various degrees of severity. On the basis of this finding, several signal processing problems have been addressed and solved within the framework of alpha-stable distributions and with the use of fractional, lower-order moments. The authors attempt to popularise these new signal processing tools within the radar community. In particular, they evaluate the goodness-of-fit of alpha-stable models in the radar environment and test the performance of new signal processing algorithms for signal detection on real radar sea-clutter data. They also include in the paper a brief review of the key ideas of signal processing with alpha-stable distributions, as well as a large number of references to the literature for further probing.

Electrical breakdown induced by silicon nitride roughness in thin oxide–nitride–oxide films

The time-dependent dielectric breakdown (TDDB) characteristics of thin oxide–nitride–oxide (ONO) films containing 4 nm of Si3N4 were found to be exclusively determined by statistical thickness fluctuations of the Si3N4 layer. A two-parameter statistical model describes this roughness and explains experimental TDDB data. A simple growth model combined with the statistical model reduces the number of parameters to one. It is consistent with TDDB data and is in quantitative agreement with transmission electron microscopy and x-ray photoelectron spectroscopy data and is able to explain the origin of the roughness. On an ONO with a 4 nm Si3N4 layer and an area of 8 cm2—corresponding to the total storage area of a 256 Mbit dynamic random access memory—the thinnest point in the Si3N4 can be expected to be below 10 Å. So eliminating the Si3N4 roughness would bring a drastic improvement in reliability.

Two-parameter statistical model for atoms

A simple two-parameter charge density of the form ρ (r) =C/(1+βr)n with β=α/n, where C is determined by normalization and α and n are determined by minimization of the total energy, is examined in connection with an energy density functional that contains kinetic energy terms up to fourth order in the gradient expansion and the classical Dirac exchange term. This charge density is finite at the nucleus, is monotonically decreasing, becomes pure exponential when n→∞, is extremely accurate for determining two-electron Hartree–Fock densities, and gives good energy values for first-row atoms. For higher atomic number, the energy values are much superior to the traditional Thomas–Fermi–Dirac values. Also presented, following Fermi [Z. Phys. 48, 73 (1928)] is a prediction with this charge density of the number of electrons with a given quantum number l in an atom with atomic number Z. The agreement with the known values is excellent: whereas Fermi found that the d shell begins to fill at Z=19.33 and the f shell at Z =53.02, the new model has the d shell beginning to fill at Z =20.96, and the f shell at Z=57.51; actual values are 20 to 21 and 57 to 58, respectively.

A Statistical Model for Sunspot Activity.

view Abstract Citations (7) References (8) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS A Statistical Model for Sunspot Activity. Granger, C. W. J. Abstract By an empirical approach, a simple two-parameter statistical model is proposed for fitting to the Wolf sunspot number data and a fit of around 88 per cent of the total variation is found. Properties of the two parameters are discussed, and other possible relationships between measurable elements of the cycles are suggested. Publication: The Astrophysical Journal Pub Date: July 1957 DOI: 10.1086/146380 Bibcode: 1957ApJ...126..152G full text sources ADS |