Mostrando recursos 1 - 20 de 36

  1. Box-Jenkins Analysis of Mean Monthly Temperature in Rwanda

    Maniraguha, Jean de Dieu; Gasana, Emelyne Umunoza
    Generally, temperature is a sensation of coldness and hotness and is affected by air and humidity as climate factors in certain areas. The high altitude of Rwanda provides the country with pleasant tropical highland climate. The recent research done on climate changes and global warming found an increase in temperature of 1 to 2 degrees Celsius between year 2000 and year 2050 due to the natural variability and due to the green house gases that trap the infrared radiation,i.e., these gases will cause the temperature of the earth to increases infrared radiation from the earth surface and reflect it back...

  2. Jump Resonance in Wind-Felled Plantains

    Asemota, Godwin Norense Osarumwense
    In this paper, jump resonance was applied to wind-felled plantains, which budded on the plantain pseudo-stem side when guyed about $60^{o}$ to the horizontal to obtain the jump function. Duffing's model, describing function and Chebyshev polynomials were used to obtain the best fit of approximants to the developed plantains jump function. Traditionally, wind-damaged plantains and banana pseudo-stems are cut for future vegetative growth, leading to heavy and perennial losses to individuals, households, communities, nations and even regions. The motivation for this study was the possibility of using jump resonance discontinuity as a plantain wind-damaged salvage process. Furthermore, it was to...

  3. Uniform in Bandwidth Law of the Iterated Logarithm for a Transformation Kernel Estimator of Copulas

    Seck, Cheikh Tidiane; Diam, Ba; Lo, Gane Samb
    In this paper, we establish a uniform in bandwidth law of the iterated logarithm for the Transformation kernel estimator of bivariate copulas introduced in Omelka et al. (2009). To this end, we make use of a general empirical process approach inspired by the works in Mason and Swanepoel (2011). We obtain the asymptotic order of the maximal deviation of this estimator from its expectation. Then, we show that the bias converges asymptotically to zero at the same order provided that the second-order partial derivatives of the copula exist and are bounded. We also propose a bandwidth selection method by using...

  4. Multivariate Analysis of Rwanda Economic Indicators using Vector Autoregressive (VAR) Model

    Uwamariya, Denise; Gasana, Emelyne Umunoza
    Rwanda's economy has been growing fast due to important economic and structural reforms over the last decade. Consumer Price Index (CPI), Exchange Rate and Nominal Growth Domestic Product (NGDP) constitute some of the major economic indicators in emerging market economies that require monetary authorities to elaborate tools and policies to prevent high volatility in prices. Thus, understanding CPI, exchange rate and NGDP dynamics is a key to the design of fund programs to help stabilize the economy of a developing country such as Rwanda. In this study, secondary data from the National Bank of Rwanda, depicting quarterly time series of...

  5. R-transform associated with asymptotic negative spectral moments of Jacobi ensemble

    Pielaszkiewicz, Jolanta
    We derive an explicit formula for the R-transform of inverse Jacobi matrix $I+W_1^{-1}W_2$, where $W_1,W_2\sim\mathcal{W}_p(I,n_i)$, $i=1,2$ are independent and $I$ is $p\times p$ dimensional identity matrix using property of asymptotic freeness of Wishart and deterministic matrices. Procedure can be extended to other sets of the asymptotically free independent matrices. Calculations are illustrated with some simulations on fixed size matrices.

  6. Further properties of linear prediction sufficiency and the BLUPs in the linear model with new observations

    Markiewicz, Augustyn; Puntanen, Simo
    A linear statistic $\mathrm{Fy}$ is called linearly sufficient for the estimable parametric function of $\mathrm X_{*} \beta$ under the linear model $\mathscr M = \{ \mathrm y, \mathrm X \beta, \mathrm V \}$ if there exists a matrix $\mathrm A$ such that $\mathrm {AFy}$ is the best linear unbiased estimator, BLUE, for $\mathrm X_{*} \beta$. The concept of linear sufficiency with respect to a predictable random vector is defined in the corresponding way but considering best linear unbiased predictor, BLUP, instead of BLUE. In this paper, we consider the linear sufficiency of $\mathrm{Fy}$ with respect to $\mathrm{y}_{*}$, $\mathrm X_{*} \beta$,...

  7. Spatio-temporal Predictions using Multivariate Singular Spectrum Analysis

    Awichi, Richard
    In this paper, we present a method for utilizing the usually intrinsic spatial information in spatial data sets to improve the quality of temporal predictions within the framework of singular spectrum analysis (SSA) techniques. The SSA-based techniques constitute a model free approach to time series analysis and ordinarily, SSA can be applied to any time series with a notable structure. Indeed it has a wide area of application including social sciences, medical sciences, finance, environmental sciences, mathematics, dynamical systems and economics. SSA has two broad aims: i) To make a decomposition of the original series into a sum of a...

  8. Editorial Paper of the Special Issue 13 (1) of Afrika Statistika on Selected papers presented to EACSAM 2017

    Singull, Martin; Rachdi, Mustapha; Lo, Gane Samb
    We briefly introduce to this special issue of Afrika Statistika devoted to selected Papers presented at the First East African Conference of Statistical Mathematics with Applications (EACSMA-2017).

  9. Comparison between two bivariate Poisson distributions through the phi-divergence

    Nganga, Prévot C. Batsindila; Bidounga, Rufin; Mizère, Dominique; Kokonendji, Célestin C.
    In the literature, there are two probabilistic models of bivariate Poisson : the model according to Holgate and the model according to Berkhout and Plug. These two models express themselves by their probability mass function. The model of Holgate puts in evidence a strictly positive correlation, which is not always realistic. To remedy this problem, Berkhout and Plug proposed a bivariate Poisson distribution accepting the correlation as well negative, equal to zero, that positive. In this paper, we show that these models are nearly everywhere asymptotically equal. From this survey that the $\phi$-divergence converges toward zero, both models are therefore...

  10. Comparison between two bivariate Poisson distributions through the phi-divergence

    Nganga, Prévot C. Batsindila; Bidounga, Rufin; Mizère, Dominique; Kokonendji, Célestin C.
    In the literature, there are two probabilistic models of bivariate Poisson : the model according to Holgate and the model according to Berkhout and Plug. These two models express themselves by their probability mass function. The model of Holgate puts in evidence a strictly positive correlation, which is not always realistic. To remedy this problem, Berkhout and Plug proposed a bivariate Poisson distribution accepting the correlation as well negative, equal to zero, that positive. In this paper, we show that these models are nearly everywhere asymptotically equal. From this survey that the $\phi$-divergence converges toward zero, both models are therefore...

  11. Bayesian Inference of C-AR(1) Time Series Model with Structural Break

    Kumar, Jitendra; Kumar, Ashok; Agiwal, Varun; Shangodoyin, Dahud Kehinde
    A variable may be affected by some associated variables which may influence the estimation and testing procedures and also not much important to model separately, such types of variables are called covariates. The present paper dealt the covariate autoregressive (C-AR(1)) time series model with structural break in mean and variance under Bayesian framework. Parameters of the model have been estimated considering appropriate prior assumptions and compared with maximum likelihood estimator. A simulation study has been carried out to validate the theoretical results, and then the same implemented on the monthly REER time series of SAARC countries. Both studies, empirical and...

  12. Bayesian Inference of C-AR(1) Time Series Model with Structural Break

    Kumar, Jitendra; Kumar, Ashok; Agiwal, Varun; Shangodoyin, Dahud Kehinde
    A variable may be affected by some associated variables which may influence the estimation and testing procedures and also not much important to model separately, such types of variables are called covariates. The present paper dealt the covariate autoregressive (C-AR(1)) time series model with structural break in mean and variance under Bayesian framework. Parameters of the model have been estimated considering appropriate prior assumptions and compared with maximum likelihood estimator. A simulation study has been carried out to validate the theoretical results, and then the same implemented on the monthly REER time series of SAARC countries. Both studies, empirical and...

  13. Generalized Stacy-Lindley Mixture Distribution

    Radakumari, Maya; Irshad, Muhammed Rasheed
    In this paper, we introduce a five parameter extension of mixture of two Stacy gamma distributions called generalized Stacy-Lindley mixture distribution. Several statistical properties are derived. Two types of estimation techniques are used for estimating the parameters. Asymptotic confidence interval is also calculated for these parameters. Finally, a real data application illustrates the performance of our proposed distribution.

  14. Generalized Stacy-Lindley Mixture Distribution

    Radakumari, Maya; Irshad, Muhammed Rasheed
    In this paper, we introduce a five parameter extension of mixture of two Stacy gamma distributions called generalized Stacy-Lindley mixture distribution. Several statistical properties are derived. Two types of estimation techniques are used for estimating the parameters. Asymptotic confidence interval is also calculated for these parameters. Finally, a real data application illustrates the performance of our proposed distribution.

  15. The mode-dispersion approach for constructing continuous associated kernels

    Libengue Dobele-Kpoka, Francial Giscard Baudin; Kokonendji, Célestin C.
    We introduce the mode-dispersion approach for constructing the (asymmetric) continuous associated kernels fromsuitable parametric probability density functions (p.d.f.) that we shall call the type of kernel. This leads us to value the choice of the associated kernel, since it takes into account the support of the unknown density $f$, to be estimated. All associated kernel density estimators must be without edge effect. For illustrating this, we introduce the extended beta kernel, which is a typical model of kernels with bounded supports. However, in the presence of a large bias of the density estimator, we propose a general but light modification...

  16. The mode-dispersion approach for constructing continuous associated kernels

    Libengue Dobele-Kpoka, Francial Giscard Baudin; Kokonendji, Célestin C.
    We introduce the mode-dispersion approach for constructing the (asymmetric) continuous associated kernels fromsuitable parametric probability density functions (p.d.f.) that we shall call the type of kernel. This leads us to value the choice of the associated kernel, since it takes into account the support of the unknown density $f$, to be estimated. All associated kernel density estimators must be without edge effect. For illustrating this, we introduce the extended beta kernel, which is a typical model of kernels with bounded supports. However, in the presence of a large bias of the density estimator, we propose a general but light modification...

  17. On the strong convergence of the hazard rate and its maximum risk point estimators in presence of censorship and functional explanatory covariate

    Agbokou, Komi; Gneyou, Kossi Essona
    In the literature much work has been devoted to the non-parametric estimation of survival analysis functions. In this work, we focus on the nonparametric estimation of the conditional hazard rate and the point of its maximum, in the model of right censored data with presence of functional covariate. We establish the almost uniform complete convergence of these estimators at appropriate rates. This generalizes the almost sure convergence obtained in the literature.

  18. On the strong convergence of the hazard rate and its maximum risk point estimators in presence of censorship and functional explanatory covariate

    Agbokou, Komi; Gneyou, Kossi Essona
    In the literature much work has been devoted to the non-parametric estimation of survival analysis functions. In this work, we focus on the nonparametric estimation of the conditional hazard rate and the point of its maximum, in the model of right censored data with presence of functional covariate. We establish the almost uniform complete convergence of these estimators at appropriate rates. This generalizes the almost sure convergence obtained in the literature.

  19. The Kumaraswamy G Exponentiated Gumbel Type-2 Distribution

    Okorie, Idika Eke; Akpanta, Anthony Chukwudi; Ohakwe, Johnson; Chikezie, David Chidi; Obi, Eunice Oluchi
    The distribution due to Okorie et al. (2016) is further extended to a wider family of distribution called the Kumaraswamy Generalized Exponentiated Gumbel type-2 distribution. Twenty two distributions are identified as sub-models of the new distribution. Some of its important statistical properties are explicitly derived and the parameters of the new distribution are estimated through the method of maximum likelihood estimation.

  20. The Kumaraswamy G Exponentiated Gumbel Type-2 Distribution

    Okorie, Idika Eke; Akpanta, Anthony Chukwudi; Ohakwe, Johnson; Chikezie, David Chidi; Obi, Eunice Oluchi
    The distribution due to Okorie et al. (2016) is further extended to a wider family of distribution called the Kumaraswamy Generalized Exponentiated Gumbel type-2 distribution. Twenty two distributions are identified as sub-models of the new distribution. Some of its important statistical properties are explicitly derived and the parameters of the new distribution are estimated through the method of maximum likelihood estimation.

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