Growth curve model for analyzing the effects of Calcium foliar feed on the wilting rate of post-harvest rose flowers

Cut rose flowers contribute to the economy and development of the export markets for several developing countries. Despite this contribution, profitable production of rose flowers is limited by wilting which leads to lower production. This paper aims to investigate the effects of Calcium foliar feed on the wilting rate of post-harvest rose flowers using the Growth Curve Model. This method was applied to the data consisting of wilting scores on five treatment groups. The Likelihood ratio test was used to test the growth curve and the equality of the growth curves in all groups. Results revealed that the expected growth curves for all groups followed different quadratic functions. The results also revealed that the wilting rate increased with the increase of calcium concentration compared to the control. This leads to a useful model for policy-makers or further analyses.

Application of ARIMA models on Export potential Indicator

The export potential indicator is designed for countries that aim to support established exports by increasing exports to new or existing target markets, and several studies are being managed using various mathematical model to predict the export values. Here, we propose an econometric model that could be useful to predict the export values. We performed the ARIMA model to evaluate the realized and unrealized export potentials of products. We therefore propose to carry out actions in favor of increasing the export potential.

Optimal model for weaning-weight of Bunaji Bulls at NAPRI Farm, Shika, Nigeria

Empirical models have over the years been commonly established by animal research centers for the study of weight-age profiles in order to understand the metabolic processes of growth. They provide efficient parameter estimates for mature weight and rate of maturing, but were found to consistently either over-or-under estimate the mature weight. estimate the mature weight. They also perform poorly in predicting weight in early life or beyond the range of input data. At the National Animal Production Research Institute (NAPRI) farm, Shika, Brody was established as the model that provides efficient parameter estimates of weight-age profiles for Bunaji bulls. However, a major drawback of the model is its consistent underestimation of weight prior to six months of age, leading to poor prediction of weaning weight. To address this shortcoming, we propose in this article a joint mean-covariance model that provide optimal parameter estimates for the weaning weight of Bunaji bulls

Modeling the Rwanda Exchange Rates by GARCH Models

Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.

Modeling the Rwanda Exchange Rates by GARCH Models

Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.

Modeling the relationship between temperature and mortality : a case study in SENEGAL

In this paper, we have used the Generalized Additive Model (GAM) to investigate the relationships between high temperature and daily number of deaths in Niakhar, a Sehalian-Sudanese climate in central Senegal. Daily data on number of deaths and meteorological variables over the period of 1983-2013 were considered. Descriptive statistics show that, over the study period, the total of non-accidental deaths were 12,798, among which we notice that 490 persons (3.83%) died of cardiovascular disease, 1,015 persons (7.93%) died of respiratory disease, 3,970 persons (31.02%) died of certain infectious and parasitic diseases, and 224 persons (1.75%) died of nervous system disease From the GAM model, we observe that high temperature significantly increased the relative risk (RR)Indeed, relative risk of deaths due to cardiovascular disease is 1.034 with a 95% confidence intervals (CI) 1.025 to 1.044, while it is 1.030 with a 95% CI 1.026 to 1.033 for certain infectious and parasitic disease. For respiratory disease, the RR is 1.012 with a 95% CI 1.007 to 1.017, and for nervous system disease, the relative risk is 1.034 with 95% CI 1.026 to 1.043.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

The spread of the coronavirus is putting a strain on financial markets and the resulting stock market volatility is causing huge problems for investors. Volatility in the U.S. market has returned to levels not seen since the 2011 sovereign debt crisis. It is already clear that this volatility has had a negative effect on the economy. In this study, we introduce a regime-switching GJR-GARCH modelwith a stable distribution to investigate the predictive power of the S&P 500 index volatility to VaR estimation. The results of VaR backtesting at a 5% risk level confirm that the model performs better and is a useful tool for the risk manager and financial regulator

The spread of the coronavirus is putting a strain on financial markets and the resulting stock market volatility is causing huge problems for investors. Volatility in the U.S. market has returned to levels not seen since the 2011 sovereign debt crisis. It is already clear that this volatility has had a negative effect on the economy. In this study, we introduce a regime-switching GJR-GARCH modelwith a stable distribution to investigate the predictive power of the S&P 500 index volatility to VaR estimation. The results of VaR backtesting at a 5% risk level confirm that the model performs better and is a useful tool for the risk manager and financial regulator.

Change-point detection: application of Cusum method to real life data

Several procedures have been developed for the detection of abrupt changes in time series. Among these procedures, it can be mentioned the Cumulative Sum (Cusum) type method. It is in such a perspective that Katchekpele et al. (2017) proposed a method using a Cusum type test to detect a change-point in the unconditional variance of the generalised autoregressive conditional heteroskedasticity(GARCH) models. The aim of this paper is to present an application of their technique. After briefly recalling how the test statistic was constructed, the change-point detection algorithm is given and it is shown how it is applied to some real life data.