UK Taxes and Tax Revenues: Composition and Trends

This study looks at the composition and trends of tax revenues in the UK. It provides a brief overview of the rather complicated system of different taxes in the UK. Three main taxes—personal income tax, national insurance contributions (NICs) and value added tax (VAT)—are shown to account for about three quarters of all tax revenues and that this has been stable over a period of time. In comparison to other countries the UK is similar in its tax composition to both the US and France, where the same three types of tax dominate revenues. It is much less similar to both Malaysia and Argentina. The study examines monthly UK tax revenues for these three taxes, using econometrically estimated trends. It finds that, in constant price terms, revenues have grown slowly and steadily over time, broadly keeping pace with growth in real GDP. Tax revenue forecasting in the UK is mainly undertaken by an independent body which publishes forecasts at the level of receipts for individual taxes. This considerably reduces the risk of political bias in these revenue forecasts

Evaluating the analytical distribution of bicoid gene expression profile

Segmentation in Drosophila melanogaster starts with a key maternal input known as bicoid gene. The initial positional information provided by this gene induces the sequential activation of segmentation network. Therefore, an accurate mathematical model describing the gene expression profile of bicoid gene expects to provide essential insights into the gene cross-regulations presented in that network. The significantly stochastic, highly volatile and non-normal nature of the bicoid gene expression profile encouraged us to look for the best distribution function describing this profile. We exploit the use of fifty-four different powerful and widely-used distributions and conclude that FatigueLife(3P) fits the data more accurately than the other distributions. The reliability and validity of the results are evaluated via both simulation studies and empirical evidence thereby adding more confidence and value to the findings of this researc

A glance at the applications of Singular Spectrum Analysis in gene expression data

AbstractIn recent years Singular Spectrum Analysis (SSA) has been used to solve many biomedical issues and is currently accepted as a potential technique in quantitative genetics studies. Presented in this article is a review of recent published genetics studies which have taken advantage of SSA. Since Singular Value Decomposition (SVD) is an important stage of this technique which can also be used as an independent analytical method in gene expression data, we also briefly touch upon some areas of the application of SVD. The review finds that at present, the most prominent area of applying SSA in genetics is filtering and signal extraction, which proves that SSA can be considered as a valuable aid and promising method for genetics analysis

A novel statistical signal processing approach for analysing high volatile expression profiles.

The aim of this research is to introduce new advanced statistical methods for analysing gene expression profiles to consequently enhance our understanding of the spatial gradients of the proteins produced by genes in a gene regulatory network (GRN). To that end, this research has three main contributions. In this thesis, the segmentation Network (SN) in Drosophila melanogaster and the bicoid gene (bcd) as the critical input of this network are targeted to study. The first contribution of this research is to introduce a new noise filtering and signal processing algorithm based on Singular Spectrum Analysis (SSA) for extracting the signal of bicoid gene. Using the proposed SSA algorithm which is based on the minimum variance estimator, the extraction of bcd signal from its noisy profile is considerably improved compared to the most widely accepted model, Synthesis Diffusion Degradation (SDD). The achieved results are evaluated via both simulation studies and empirical results. Given the reliance of this research towards introducing an improved signal extraction approach, it is mandatory to compare the proposed method with the other well-known and widely used signal processing models. Therefore, the results are compared with a range of parametric and non-parametric signal processing methods. The conducted comparison study confirmed the outperformance of the SSA technique. Having the superior performance of SSA, in the second contribution, the SSA signal extraction performance is optimised using several novel computational methods including window length and eigenvalue identification approaches, Sequential and Hybrid SSA and SSA based on Colonial Theory. Each introduced method successfully improves a particular aspect of the SSA signal extraction procedure. The third and final contribution of this research aims at extracting the regulatory role of the maternal effect genes in SN using a variety of causality detection techniques. The hybrid algorithm developed here successfully portrays the interactions which have been previously accredited via laboratory experiments and therefore, suggests a new analytical view to the GRNs

Cross country relations in European tourist arrivals

This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSS) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis

Optimizing bicoid signal extraction.

Signal extraction and analysis is of great importance, not only in fields such as economics and meteorology, but also in genetics and even biomedicine. There exists a range of parametric and nonparametric techniques which can perform signal extractions. However, the aim of this paper is to define a new approach for optimising signal extraction from bicoid gene expression profile. Having studied both parametric and nonparametric signal extraction techniques, we identified the lack of specific criteria enabling users to select the optimal signal extraction parameters. Exploiting the expression profile of bicoid gene, which is a maternal segmentation coordinate gene found in Drosophila melanogaster, we introduce a new approach for optimising the signal extraction using a nonparametric technique. The underlying criteria are based on the distribution of the residual, more specifically its skewness

Forecasting home sales in the four census regions and the aggregate US economy using singular spectrum analysis

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of Singular Spectrum Analysis (SSA) meth- ods, namely Recurrent SSA (RSSA) and Vector SSA (VSSA), in univariate and multivariate frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the perfor- mance of the SSA-based models with classical and Bayesian variants of the autoregressive and vector autoregressive models. Using an out-of-sample period of 1979:8-2014:6, given an in-sample period of 1973:1-1979:7, we find that the univariate VSSA is the best performing model for the aggregate US home sales, while the multivariate versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results high- light the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.http://link.springer.com/journal/106142017-12-31hb2016Economic

Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.

The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average

From Nature to Maths: Improving Forecasting Performance in Subspace–based methods using Genetics Colonial Theory

Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research

Pattern Recognition of Gene Expression with Singular Spectrum Analysis

Drosophila segmentation as a model organism is one of the most highly studied. Among many maternal segmentation coordinate genes, bicoid protein pattern plays a significant role during Drosophila embryogenesis, since this gradient determines most aspects of head and thorax development. Despite the fact that several models have been proposed to describe the bicoid gradient, due to its association with considerable error, each can only partially explain bicoid characteristics. In this paper, a modified version of singular spectrum analysis is examined for filtering and extracting the bicoid gene expression signal. The results with strong evidence indicate that the proposed technique is able to remove noise more effectively and can be considered as a promising method for filtering gene expression measurements for other applications