Statistical Aspects Research Articles

The Predictive Power of Regression Models to Determine Grass Weed Infestations in Cereals Based on Drone Imagery—Statistical and Practical Aspects

Site-specific weed management (SSWM) may reduce herbicide use by identifying weed patches and weed-free areas. However, one major constraint is robust weed detection algorithms that are able to predict weed infestations outside of the training data. This study investigates the predictive power of regression models trained on drone imagery that are used within fields to predict infestations of annual grass weeds in the late growth stages of cereals. The main objective was to identify the optimum sampling strategy for training regression models based on aerial RGB images. The study showed that training based on sampling from the whole range of weed infestations or the extreme values in the field provided better prediction accuracy than random sampling. Prediction models based on vegetation indices (VIs) offered a useful alternative to a more complex random forest machine-learning algorithm. For binary decision-making, linear regression utilizing weed density information resulted in higher accuracy than a logistic regression approach that only relied on information regarding the presence/absence of weeds. Across six fields, the average balanced accuracy based on linear regression was in the range of 75–83%, with the highest accuracy found when the sampling was from the extreme values in the field, and with the lowest accuracy found for random sampling. For future work on training weed prediction models, choosing training sets covering the entire sample space is recommended in favor of random sampling.

Lowering the Helstrom bound with non-standard coherent states

In quantum information processing, using a receiver device to differentiate between two nonorthogonal states leads to a quantum error probability. The minimum possible error is known as the Helstrom bound. In this work, we study statistical aspects and quantum limits for states that generalize the Glauber–Sudarshan coherent states, such as nonlinear, Perelomov, Barut–Girardello, and (modified) Susskind–Glogower coherent states. For some of these, we show that the Helstrom bound can be significantly lowered and even vanish in specific regimes.

On the Use of a Modified Intersection of Confidence Intervals (MICIH) Kernel Density Estimation Approach

Density estimation is an important aspect of statistics. Statistical inference often requires the knowledge of observed data density. A common method of density estimation is the kernel density estimation (KDE). It is a nonparametric estimation approach which requires a kernel function and a window size (smoothing parameter H). It aids density estimation and pattern recognition. So, this work focuses on the use of a modified intersection of confidence intervals (MICIH) approach in estimating density. The Nigerian crime rate data reported to the Police as reported by the National Bureau of Statistics was used to demonstrate this new approach. This approach in the multivariate kernel density estimation is based on the data. The main way to improve density estimation is to obtain a reduced mean squared error (MSE), the errors for this approach was evaluated. Some improvements were seen. The aim is to achieve adaptive kernel density estimation. This was achieved under a sufficiently smoothing technique. This adaptive approach was based on the bandwidths selection. The quality of the estimates obtained of the MICIH approach when applied, showed some improvements over the existing methods. The MICIH approach has reduced mean squared error and relative faster rate of convergence compared to some other approaches. The approach of MICIH has reduced points of discontinuities in the graphical densities the datasets. This will help to correct points of discontinuities and display adaptive density. Keywords: approach, bandwidth, estimate, error, kernel density

Cement Dust Air Pollution and Its Effects on Human Health in Ewekoro Local Government Area Ogun State, Nigeria

Many developing nations like Nigeria, the causes of air pollution are numerous ranging from disposal of domestic and industrial wastes, bush burning, gas glaring, car exhaust, etc. all these activities affect well-being of human. Cement factory air pollution is classified as one of the sources of air pollution that increase respiratory diseases in community. This paper focuses on the dimensions of the spatial pattern, standards limit of gaseous pollutants (CO, CO 2 and SO 2 ), and health record of the community. Suspended particles were monitored using ToxiRAE II for (Carbon Monoxide and Sulphur) and ALNOR CF910 for (carbon dioxide) air sampler to assess the levels of gaseous pollutants throughout the community. Data on health was collected at the neighborhood health centers. The data was analyzed and the statistical aspect was carried on with the help of Ordinary Least Square (OLS) for the relationship between the pollutant and health record collected where the root means square (r 2 ) show the significance 0.010703, 0.845100, and 0.000733 for SO 2 , CO, and CO 2 respectively. Keywords: Air pollution; Community health ; Gaseous Pollutants; GIS DOI: 10.7176/JEES/11-11-04 Publication date: November 30 th 2021

Discrete coalescent trees

In many phylogenetic applications, such as cancer and virus evolution, time trees, evolutionary histories where speciation events are timed, are inferred. Of particular interest are clock-like trees, where all leaves are sampled at the same time and have equal distance to the root. One popular approach to model clock-like trees is coalescent theory, which is used in various tree inference software packages. Methodologically, phylogenetic inference methods require a tree space over which the inference is performed, and the geometry of this space plays an important role in statistical and computational aspects of tree inference algorithms. It has recently been shown that coalescent tree spaces possess a unique geometry, different from that of classical phylogenetic tree spaces. Here we introduce and study a space of discrete coalescent trees. They assume that time is discrete, which is natural in many computational applications. This tree space is a generalisation of the previously studied ranked nearest neighbour interchange space, and is built upon tree-rearrangement operations. We generalise existing results about ranked trees, including an algorithm for computing distances in polynomial time, and in particular provide new results for both the space of discrete coalescent trees and the space of ranked trees. We establish several geometrical properties of these spaces and show how these properties impact various algorithms used in phylogenetic analyses. Our tree space is a discretisation of a previously introduced time tree space, called t-space, and hence our results can be used to approximate solutions to various open problems in t-space.

Towards Good Statistical Practice. CORSTAN Validated Questionnaire for Assessing the Correctness of Statistical Analysis in Medical Research.

In evidence-based medicine, the research methodology is determined by the risks of systematic biases and incorrect data analysis. Minimizing both risks increases the internal validity of the study. There are numerous recommendations and guidelines for data analysis and reporting, but the international community has not yet developed a questionnaire for reviewers to assess the quality of statistical analysis. To develop a tool for formalized assessment of the quality of statistical analysis presented in scientific medical publications. The questionnaire was developed based on the authors' decades of experience in statistical data analysis and reviewing the statistical aspects of biomedical articles and dissertations. The SAMPL guidelines, ICH E9, and other guidelines were taken into account when developing the questionnaire. Internal validation of the questionnaire was based on an independent assessment by two experts of 20 randomly selected articles on randomized controlled trials (RCTs) from elibrary.ru, and further statistical analysis of the agreement of experts' conclusions. The CORSTAN (CORrect STatistical ANalysis) questionnaire was developed, which consists of two parts: the first part (10 questions) is intended for evaluating studies of any designs, while the second (following eight questions) is for additional assessment of RCTs. A stratification of the risk of incorrect statistical analysis is proposed. The evaluation of the questionnaire's internal validity showed its substantial and almost perfect agreement for each question and each article both in the sum of points and risk level. The use of the questionnaire will simplify and harmonize the statistical review of publications and manuscripts in various institutions - scientific journals, dissertation boards, etc. The questionnaire can also be helpful for authors during preparing manuscripts; it will also help improve the quality of publications and research itself. We plan to improve the questionnaire as we gain experience in its application.

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129.

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

Image compression–encryption scheme using SPIHT and chaotic systems

The world is moving towards digitization for all forms of data. Images are one of the most used data that contains a lot of information. With the increasing network technology, the sharing of data across the internet has also manifold. Certain images are confidential and require security for storing or transmitting on an insecure communication channel. Digital images usually have highly correlated data where compression can be performed to reduce the load on the transmission channel or storage memory. The paper proposes an algorithm to compress the digital images and encryption is performed on the compressed data to provide security. Compression of the image is performed using the Set partitioning in hierarchical trees algorithm. The compressed image is encrypted using a chaotic sequence generated using the Henon map and the cipher image is scrambled using a permutation table obtained from Piecewise Linear chaotic map. To reduce the computation while generating the chaotic sequence, a base conversion operation is used. The initial parameters of the chaotic systems are generated from the elliptic curve point are shared securely using ElGamal based elliptic curve cryptosystem. Security analyses are conducted along with a comparison with other existing systems. The proposed algorithm is secured against various attacks and outperforms existing techniques concerning various statistical and security aspects of an enciphering algorithm.

Behavioral dynamics of pedestrian’s movements: Theoretical aspects of commercial arterial road frontage activities

The common scientific approaches to the reasoning of problems are mathematical reasoning or statistical reasoning. Mathematical or formal reasoning is usually deductive, therein one reason from general assumptions to specifics using symbolic logic and axioms for multi criteria decision-making. Mathematical probability, which is the basis of all statistical theories, had its beginning in the past. The aim of this paper is to explore a number of the mathematical and statistical aspects of the disposition and behavior of road frontage activities, which are of importance in pedestrian behavior as considered. It's shown that number of crossings from right to left is proportional to the pedestrian on the right (PXRL ∝ NR) and therefore, the number of crossings left to right is proportional to the pedestrians on the left (PXLR ∝ NL). Frequency distributions of the pedestrians generated for a given shopping string arterial were of 4 kinds, one related to pedestrians passing through not crossing the road, not going into and out of outlets. The second kind related to pedestrians crossing the road for the aim of going into and out of outlets, etc. the third kind related to pedestrian going into shops and eventually, the fourth kind related to others, e.g. Pedestrians generated from parking vehicles, buses, etc. A formula is given for the frequency with crossing from left to right and right to leave based on the land-use activities on the left and right. In considering the capacity of road systems it should be remembered that increases in traffic flow generally produce corresponding decreases in speed. However, it's an assumption that a rise in population generated along the footpath can cause the crossing the road, and usually produce corresponding decreases in vehicle speed. The paper concludes with a constatation of the pedestrian movements at a continuing rate that expressed in mathematical form.

Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview

Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of ITSA with respect to bias and confounding, data quality, and statistical aspects. We provide recommendations to strengthen the robustness of ITSA studies and reduce their susceptibility to producing spurious results as a consequence of arbitrary modelling decisions.

The stochastic modified Lundqvist-Korf diffusion process: statistical and computational aspects and application to modeling of the $$\hbox {CO}_2$$ emission in Morocco

The stochastic modified Lundqvist-Korf diffusion process: statistical and computational aspects and application to modeling of the $$\hbox {CO}_2$$ emission in Morocco

Cut points identification of continuous biomarkers: A challenge that goes beyond statistical aspects.

TO THE EDITOR: I just read the article written by Polley and Dignam ( [1][1] ) titled, “Statistical Considerations in the Evaluation of Continuous Biomarkers.” In that article, the authors described some common statistical issues related to biomarker cut point identification and provided

Biostatistical Methods for Understanding the Principle of Randomized Controlled Trials

Knowledge of statistics is essential for understanding the evidence from clinical trials. In this paper, the principles of blinding, FAS-ITT, pre-specified endpoints, subgroup analysis, missing values, sample size computation, applicability of global studies, non-inferiority trials, and meta-analysis are described. Moreover, the statistical aspects of the recent large-scale clinical trial regarding aducanumab as an Alzheimer's disease treatment will be discussed.

More than an Offender Location Tool: Geographic Profiling and Body Deposition Sites

In homicide cases, it is difficult to provide resolution for the bereaved or to obtain a successful criminal conviction of the guilty party when no body is found. Since the mid-nineteenth century, geographic and environmental patterns have been used to better understand the relationship between crime and its environment. Now known as geographic profiling, practitioners in this field amalgamate criminological, psychological, and geographical knowledge, as well as aspects of mathematics, statistics, and physics to identify spatial patterns associated with criminal behaviour as a means of locating anchor points of an offender (where they live, or work). The same techniques can also be used to locate the covert body deposition sites of their victims. This paper aims to (1) provide a brief summary of criminal behaviour and the environment and how understanding their relationship can be helpful to geographic profiling, (2) amalgamate the available literature on the application of geographic profiling in locating clandestine graves (as most documented uses are to locate offender residences), and (3) include a geographic profile of Ivan Milat, an Australian serial killer (officially) active from 1989 to 1992, demonstrating how geographic profiling techniques can help to identify additional victims and potential body deposition sites. The information in this review will be helpful to law enforcement and practitioners to improve missing persons investigations and searches for clandestine graves.

Factors Influencing for Severity of Road Traffic Accidents in Sri Lanka

The Sri Lankan Journal of Applied Statistics(SLJAS) is an open-access, international, double-blind peer-reviewed journal published by the Institute of Applied Statistics, Sri Lanka (IASSL). The main purpose of the journal is to publish the results of original work on applications of Statistics and on theoretical and methodical aspects of Statistics. The journal also welcomes critical reviews including conceptual discussions, opinions and book reviews. Applications of Statistics in the area of Agriculture & Forestry, Medical, Dental and Veterinary Sciences, Natural, Physical Sciences, Social Sciences, Economics and Actuarial Science fall within the scope of the journal. This journal does not charge any fee for article processing and submission.

Estimator of Domain Mean Using Stratified Sampling in the Presence on Non-Response

The Sri Lankan Journal of Applied Statistics(SLJAS) is an open-access, international, double-blind peer-reviewed journal published by the Institute of Applied Statistics, Sri Lanka (IASSL). The main purpose of the journal is to publish the results of original work on applications of Statistics and on theoretical and methodical aspects of Statistics. The journal also welcomes critical reviews including conceptual discussions, opinions and book reviews. Applications of Statistics in the area of Agriculture & Forestry, Medical, Dental and Veterinary Sciences, Natural, Physical Sciences, Social Sciences, Economics and Actuarial Science fall within the scope of the journal. This journal does not charge any fee for article processing and submission.

Financial stress and oil market volatility: new evidence

ABSTRACT This study investigates the effect of financial stress (financial stress index; FSI) on oil market realized volatility using Markov-regime switching MIDAS models. The empirical results show that FSI can remarkably improve the forecast accuracy of the oil market. Moreover, FSI measured by the United States can obtain more useful information than that measured by the world. Our findings are robust and reliable via alternative evaluation methods from both statistical and economic aspects.

Ten Commandments for Randomized Trials of Pharmacological Therapy for COPD and Other Lung Diseases

The randomized controlled trial is the quintessential scientific tool to evaluate the effectiveness and safety of medications. While early trials of drugs used for the treatment of chronic obstructive pulmonary disease (COPD) and other respiratory diseases were generally unambiguous, more recent studies have been controversial. It has become evident that the conduct, design and analysis of these trials were highly variable and may have been responsible for incoherencies in results and interpretation. With the advent of new studies, the need for guiding principles for the conduct of future randomized trials has become manifest. We describe the concept of the counterfactual principle as it applies to the treatment of patients and to the randomized trial. We then present ten methodological tenets for the design and statistical aspects of randomized controlled trials evaluating the effectiveness of drugs used in the treatment of several respiratory diseases. They include eight study design and two statistical analysis principles: 1) Study question; 2) Intervention; 3) Study population; 4) Blinding; 5) Run-in period; 6) Follow-up; 7) Outcome; 8) Safety; 9) Intent-to-treat; 10) Covariate adjustment. These tenets are described using mainly examples from trials of pharmacological treatments for COPD, as well as some from asthma and idiopathic pulmonary fibrosis, conducted over the last 30 years. The careful application of these principles in the conduct of randomized trials will provide rigorous studies and improve the validity of results. The ensuing clearer interpretation of findings will permit their well-founded contribution to treatment guidelines and optimal clinical management.

Effects of quenching protocols based on parametric oscillators

We consider the problem of understanding the basic features displayed by quantum systems described by parametric oscillators whose time-dependent frequency parameter ω(t) varies continuously during evolution so to realize quenching protocols of different types. To this scope we focus on the case where ω(t)2 behaves like a Morse potential, up to possible sign reversion and translations in the (t,ω2) plane. We derive closed form solution for the time-dependent amplitude of quasi-normal modes, which is the very fundamental dynamical object entering the description of both classical and quantum parametric oscillators, and highlight its significant characteristics for distinctive cases arising based on the driving specifics. After doing so, we provide an insight on the way quantum states evolve by paying attention on the position–momentum Heisenberg uncertainty principle and the statistical aspects implied by second-order correlation functions over number-type states.

Is there a role for statistics in artificial intelligence?

The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.