israel adikah | Kwame Nkrumah University of Science and Technology (original) (raw)

Papers by israel adikah

Research paper thumbnail of Relevance of COVID-19 vaccine on the tourism industry: Evidence from China

PLOS ONE, 2022

Background Vaccination is indeed one of the interventional strategies available to combat coronav... more Background Vaccination is indeed one of the interventional strategies available to combat coronavirus disease (COVID-19). This study emphasizes the relevance of citizens’ acceptance of the COVID-19 vaccine in assisting global recovery from the pandemic and aiding the tourism industries to return to normalcy. This study further presented the impact of COVID-19 on the tourism industry in China. Also, the study confirmed the past performance of tourism in China to the current tourism-related COVID-19 effects from a global perspective by employing Australia’s outbound tourism data from 2008 to 2020 on top 6 destinations, including China, Indonesia, New Zealand, Thailand, the United Kingdom, and the United States. Methods Jeffrey’s Amazing Statistical Program (JASP) was used to analyze this study. The JASP statistical software was employed to accurately analyze the vaccines administered in China from December 15, 2020, to March 28, 2021. Results The study results demonstrate an overwhelm...

Research paper thumbnail of An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

Innovative Systems Design and Engineering

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a ... more In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE. Keywords: Maximum Likelihood Estimation , minimum variance unbiased, Estimator, Probability Distribution Function. DOI: 10.7176/ISDE/11-3-05 Publication date: June 30 th 2020

Research paper thumbnail of Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange)

International Journal of Science and Research (IJSR), Mar 5, 2020

This study examines the dependence structure of Ghana's financial market using copula methods and... more This study examines the dependence structure of Ghana's financial market using copula methods and the correlation method. Modeling multivariate probability distributions can be difficult if the marginal probability density functions of the random variables of the components differ. Most microeconomic modeling situations have marginal distributions that cannot be easily combined into joint distributions. Since there are few or no joint parametric distributions based on the margins of different families, the copula method provides a simple and general approach to building joint distributions in these situations. Financial markets are concerned with whether prices of different assets exhibit dependence. For these reasons, copulas have become very important as a technique for modeling these non-constant correlations. This has been a great blessing for financial engineering because it is possible to flexibly model these nonlinear relationships. Copula is a suitable tool for modeling dependence between random variables with any marginal distributions. This is why the copula method will be used to study how the various selected stocks move together. How can the Copula method be used on a stock exchange market? This report introduces the idea of a copula, consisting of correlation and dependence, completes the basic mathematics behind its composition and the applications in financial engineering, in particular the structure of dependency in the Ghanaian financial market (promotions). This report examines the linear and non-linear dependency (structure) between the stocks selected on the Ghanaian stock market using the Joe Clayton Copula.

Research paper thumbnail of An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a ... more In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE. The estimator using Maximum Likelihood (MLE) is an important tool for determining the real chances of the hypothetical communication model. A communication channel can also be quite complex and therefore, needs a model to simplify calculations on the decoder's side. The model must be close to the complex communication channel. Numerous standard statistical models are used for this task; Gaussian, Binomial, Exponential, Geometric, Poisson, etc. A standard communication model is chosen based on empirical data. All these models have unique parameters that have these characteristics. The determination of these parameters for the chosen model is necessary to model the communication channel within reach. In statistics, the Maximum Likelihood Estimator (MLE) is a method for assessing the parameters of a particular statistical model that determines the parameter values that allow observations to be made under certain conditions. MLE can be seen as a special case of the rear maximum estimate with a uniform preventive distribution of parameters or as a variant of that ignores the above and is therefore not regulated. The maximum probability method corresponds to too many estimation methods known in statistics. Assuming that increases are generally distributed with unknown average and variance, average and variance can be estimated with MLE, while only a few samples of the general population are known. MLE would achieve this by taking the average and variance as parameters and finding certain parametric values that make the observed results more likely given the model. In general, the maximum probability method for a fixed dataset and an underlying statistical model selects the value of the model parameters that maximize probability function. Maximum probability (MLE) is an important tool for determining the actual possibilities of the hypothetical communication model.

Research paper thumbnail of An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

Innovative Systems Design and Engineering, 2020

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a ... more In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a
particular statistical model, finding parameter values that maximize probability, observations, and the parameters
are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes
a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore
unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the
minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator,
which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical
estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for
complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given dataset.
The safest way to find the MLE is to search the grid, as long as the space between the searches are small
enough, we are sure to find the MLE.

Research paper thumbnail of Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange

Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange), 2020

This study examines the dependence structure of Ghana's financial market using copula methods and... more This study examines the dependence structure of Ghana's financial market using copula methods and the correlation method. Modeling multivariate probability distributions can be difficult if the marginal probability density functions of the random variables of the components differ. Most microeconomic modeling situations have marginal distributions that cannot be easily combined into joint distributions. Since there are few or no joint parametric distributions based on the margins of different families, the copula method provides a simple and general approach to building joint distributions in these situations. Financial markets are concerned with whether prices of different assets exhibit dependence. For these reasons, copulas have become very important as a technique for modeling these non-constant correlations. This has been a great blessing for financial engineering because it is possible to flexibly model these nonlinear relationships. Copula is a suitable tool for modeling dependence between random variables with any marginal distributions. This is why the copula method will be used to study how the various selected stocks move together. How can the Copula method be used on a stock exchange market? This report introduces the idea of a copula, consisting of correlation and dependence, completes the basic mathematics behind its composition and the applications in financial engineering, in particular the structure of dependency in the Ghanaian financial market (promotions). This report examines the linear and non-linear dependency (structure) between the stocks selected on the Ghanaian stock market using the Joe Clayton Copula.

Research paper thumbnail of Relevance of COVID-19 vaccine on the tourism industry: Evidence from China

PLOS ONE, 2022

Background Vaccination is indeed one of the interventional strategies available to combat coronav... more Background Vaccination is indeed one of the interventional strategies available to combat coronavirus disease (COVID-19). This study emphasizes the relevance of citizens’ acceptance of the COVID-19 vaccine in assisting global recovery from the pandemic and aiding the tourism industries to return to normalcy. This study further presented the impact of COVID-19 on the tourism industry in China. Also, the study confirmed the past performance of tourism in China to the current tourism-related COVID-19 effects from a global perspective by employing Australia’s outbound tourism data from 2008 to 2020 on top 6 destinations, including China, Indonesia, New Zealand, Thailand, the United Kingdom, and the United States. Methods Jeffrey’s Amazing Statistical Program (JASP) was used to analyze this study. The JASP statistical software was employed to accurately analyze the vaccines administered in China from December 15, 2020, to March 28, 2021. Results The study results demonstrate an overwhelm...

Research paper thumbnail of An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

Innovative Systems Design and Engineering

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a ... more In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE. Keywords: Maximum Likelihood Estimation , minimum variance unbiased, Estimator, Probability Distribution Function. DOI: 10.7176/ISDE/11-3-05 Publication date: June 30 th 2020

Research paper thumbnail of Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange)

International Journal of Science and Research (IJSR), Mar 5, 2020

This study examines the dependence structure of Ghana's financial market using copula methods and... more This study examines the dependence structure of Ghana's financial market using copula methods and the correlation method. Modeling multivariate probability distributions can be difficult if the marginal probability density functions of the random variables of the components differ. Most microeconomic modeling situations have marginal distributions that cannot be easily combined into joint distributions. Since there are few or no joint parametric distributions based on the margins of different families, the copula method provides a simple and general approach to building joint distributions in these situations. Financial markets are concerned with whether prices of different assets exhibit dependence. For these reasons, copulas have become very important as a technique for modeling these non-constant correlations. This has been a great blessing for financial engineering because it is possible to flexibly model these nonlinear relationships. Copula is a suitable tool for modeling dependence between random variables with any marginal distributions. This is why the copula method will be used to study how the various selected stocks move together. How can the Copula method be used on a stock exchange market? This report introduces the idea of a copula, consisting of correlation and dependence, completes the basic mathematics behind its composition and the applications in financial engineering, in particular the structure of dependency in the Ghanaian financial market (promotions). This report examines the linear and non-linear dependency (structure) between the stocks selected on the Ghanaian stock market using the Joe Clayton Copula.

Research paper thumbnail of An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a ... more In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE. The estimator using Maximum Likelihood (MLE) is an important tool for determining the real chances of the hypothetical communication model. A communication channel can also be quite complex and therefore, needs a model to simplify calculations on the decoder's side. The model must be close to the complex communication channel. Numerous standard statistical models are used for this task; Gaussian, Binomial, Exponential, Geometric, Poisson, etc. A standard communication model is chosen based on empirical data. All these models have unique parameters that have these characteristics. The determination of these parameters for the chosen model is necessary to model the communication channel within reach. In statistics, the Maximum Likelihood Estimator (MLE) is a method for assessing the parameters of a particular statistical model that determines the parameter values that allow observations to be made under certain conditions. MLE can be seen as a special case of the rear maximum estimate with a uniform preventive distribution of parameters or as a variant of that ignores the above and is therefore not regulated. The maximum probability method corresponds to too many estimation methods known in statistics. Assuming that increases are generally distributed with unknown average and variance, average and variance can be estimated with MLE, while only a few samples of the general population are known. MLE would achieve this by taking the average and variance as parameters and finding certain parametric values that make the observed results more likely given the model. In general, the maximum probability method for a fixed dataset and an underlying statistical model selects the value of the model parameters that maximize probability function. Maximum probability (MLE) is an important tool for determining the actual possibilities of the hypothetical communication model.

Research paper thumbnail of An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

Innovative Systems Design and Engineering, 2020

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a ... more In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a
particular statistical model, finding parameter values that maximize probability, observations, and the parameters
are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes
a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore
unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the
minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator,
which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical
estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for
complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given dataset.
The safest way to find the MLE is to search the grid, as long as the space between the searches are small
enough, we are sure to find the MLE.

Research paper thumbnail of Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange

Dependency between Stock Movements Using the Clayton Copula Method (Ghana Stock Exchange), 2020

This study examines the dependence structure of Ghana's financial market using copula methods and... more This study examines the dependence structure of Ghana's financial market using copula methods and the correlation method. Modeling multivariate probability distributions can be difficult if the marginal probability density functions of the random variables of the components differ. Most microeconomic modeling situations have marginal distributions that cannot be easily combined into joint distributions. Since there are few or no joint parametric distributions based on the margins of different families, the copula method provides a simple and general approach to building joint distributions in these situations. Financial markets are concerned with whether prices of different assets exhibit dependence. For these reasons, copulas have become very important as a technique for modeling these non-constant correlations. This has been a great blessing for financial engineering because it is possible to flexibly model these nonlinear relationships. Copula is a suitable tool for modeling dependence between random variables with any marginal distributions. This is why the copula method will be used to study how the various selected stocks move together. How can the Copula method be used on a stock exchange market? This report introduces the idea of a copula, consisting of correlation and dependence, completes the basic mathematics behind its composition and the applications in financial engineering, in particular the structure of dependency in the Ghanaian financial market (promotions). This report examines the linear and non-linear dependency (structure) between the stocks selected on the Ghanaian stock market using the Joe Clayton Copula.