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A Spatial-Nonparametric Approach for Prediction of Claim Frequency in Motor Insurance

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Open Journal of Statistics 2021年第4期11卷 493-505页

作者： Gideon Kipngetich Ananda Kube thomas mageto Pan African University Nairobi Kenya Department of Mathematics Statistics and Actuarial Sciences Kenyatta University Nairobi Kenya Department of Statistics and Actuarial Sciences JKUAT Nairobi Kenya

Spatial modeling has largely been applied in epidemiology and disease modeling. Different methods such as Generalized linear models (GLMs) have been made available to prediction of the claim frequencies. However, due to heterogeneity nature of policies, the methods do not generate precise and accurate claim frequencies predictions;these parametric statistical methods extensively depend on limiting assumptions (linearity, normality, independence among predictor variables, and a pre-existing functional form relating the criterion variable and predictive variables). This study investigates how to derive a spatial nonparametric model estimator based on smoothing Spline for predicting claim frequencies. The simulation results showed that the proposed estimator is efficient for prediction of claim frequencies than the kernel based counterpart. The estimator derived was applied to a sample of 6500 observations obtained from Cooperative Insurance Company, Kenya for the period of 2018-2020 and the results showed that the proposed method performs better than the kernel based counterpart. It is worth noting that inclusion of the spatial effects significantly improves the estimator prediction of claim frequency.

关键词： Nonparametric SAR Smoothing Spline Claims CIC Spatial

Approximation of Finite Population Totals Using Lagrange Polynomial

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Open Journal of Statistics 2017年第4期7卷 689-701页

作者： Lamin Kabareh thomas mageto Benjamin Muema Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Approximation of finite population totals in the presence of auxiliary information is considered. A polynomial based on Lagrange polynomial is proposed. Like the local polynomial regression, Horvitz Thompson and ratio estimators, this approximation technique is based on annual population total in order to fit in the best approximating polynomial within a given period of time (years) in this study. This proposed technique has shown to be unbiased under a linear polynomial. The use of real data indicated that the polynomial is efficient and can approximate properly even when the data is unevenly spaced.

关键词： Lagrange Polynomial Approximation Finite Population Total Auxiliary Information Local Polynomial Regression Horvitz Thompson and Ratio Estimator

A Modification to the Fuzzy Regression Discontinuity Model to Settings with Fuzzy Variables

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Open Journal of Statistics 2022年第5期12卷 676-690页

作者： Portia Kuzivakwashe Mafukidze Samuel Musili Mwalili thomas mageto Department of Mathematics The Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Despite the fact that fuzzy regression discontinuity designs are growing in popularity, a lot of research takes into account treatment non-compliance difficulties, specifically the fuzziness of the treatment impact. This paper took into account independent and dependent fuzzy factors when creating these designs. Additionally we took into account treatment non-compliance difficulties, specifically the fuzziness of the treatment impact, as other research does. The modified Fuzzy Regression Discontinuity model is preferable for modeling fuzzy data. It enables us to draw improved causal effects accommodating fuzzy variables, not just the fuzziness of the treatment effect as in Fuzzy Regression Discontinuity models. A fuzzy dataset is converted into crisp data by the Centroid method of defuzzification. Once the data is crisp, the traditional least squares methods of approximation are used to estimate the parameters in the model since these parameters are considered crisp whilst the error terms are fuzzy. The Alcohol Use Disorders Identification Test score(AUDIT score) can be used as a cutoff to initiate treatment in this case and can be used to predict the progression of HIV disease and/or AIDS. Counseling helps to lower the use of alcohol in people living with HIV/AIDS (PLWHA) as a result, improving the participants’ CD4 counts.

关键词： Fuzzy Regression Discontinuity Fuzzification Centroid Method of Defuzzification Membership Function

A Multiplicative Bias Correction for Nonparametric Approach and the Two Sample Problem in Sample Survey

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Open Journal of Statistics 2017年第6期7卷 1053-1066页

作者： Kemtim Tamboun Stephane Romanus Odhiambo Otieno thomas mageto Pan African University Institute of Science Technology and Innovation Nairobi Kenya

Let two separate surveys collect related information on a single population U. Consider situation where we want to best combine data from the two surveys to yield a single set of estimates of a population quantity (population parameter) of interest. This Article presents a multiplicative bias reduction estimator for nonparametric regression to two sample problem in sample survey. The approach consists to apply a multiplicative bias correction to an estimator. The multiplicative bias correction method which was proposed, by Linton & Nielsen, 1994, assures a positive estimate and reduces the bias of the estimate with negligible increase in variance. Even as we apply this method to the two sample problem in sample survey, we found out through the study of it asymptotic properties that it was asymptotically unbiased, and statistically consistent. Furthermore an empirical study was carried out to compare the performance of the developed estimator with the existing ones.

关键词： Multiplicative Bias Correction Two Sample Problem Bias

ANN-Time Varying GARCH Model: Simulations and Application in Modelling Temperature for Weather Derivatives

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Open Journal of Statistics 2022年第3期12卷 433-441页

作者： Elias K. Karuiru John Mwaniki Kihoro thomas mageto Anthony Gichuhi Waititu The Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya School of Computing and Mathematics Co-Operative University Nairobi Kenya Department of Statistics and Actuarial Sciences JKUAT Nairobi Kenya

In economics and finance, minimising errors while building an abstract representation of financial assets plays a critical role due to its application in areas such as risk management, decision making and option pricing. Despite the many methods developed to handle this problem, modelling processes with fixed and random periodicity still remains a major challenge. Such methods include Artificial Neural networks (ANN), Fuzzy Inference system (FIS), GARCH models and their hybrids. This study seeks to extend literature of hybrid ANN-Time Varying GARCH model through simulations and application in modelling weather derivatives. The study models daily temperature of Kenya using ANN-Time Varying GARCH (1, 1), Time Lagged Feedforward neural network (TLNN) and periodic GARCH family models. Mean square error (MSE) and coefficient of determination R2 were used to determine performance of the models under study. Results obtained show that the ANN-Time Varying GARCH model gives the best results.

关键词： Artificial Neural Network Time Varying GARCH Weather Derivatives Temperature

ANN-Time Varying GARCH Model for Processes with Fixed and Random Periodicity

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Open Journal of Statistics 2021年第5期11卷 673-689页

Financial Time Series Forecasting is an important tool to support both individual and organizational decisions. Periodic phenomena are very popular in econometrics. Many models have been built aiding capture of these periodic trends as a way of enhancing forecasting of future events as well as guiding business and social activities. The nature of real-world systems is characterized by many uncertain fluctuations which makes prediction difficult. In situations when randomness is mixed with periodicity, prediction is even much harder. We therefore constructed an ANN Time Varying Garch model with both linear and non-linear attributes and specific for processes with fixed and random periodicity. To eliminate the need for time series linear component filtering, we incorporated the use of Artificial Neural Networks (ANN) and constructed Time Varying GARCH model on its disturbances. We developed the estimation procedure of the ANN time varying GARCH model parameters using non parametric techniques.

关键词： Fixed Periodicity Random Periodicity Artificial Neural Network Time Varying GARCH

Tuning beneficial calcineurin phosphatase activation to counterα-synuclein toxicity in a yeast model of Parkinson’s disease

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Neural Regeneration Research 2025年第1期20卷 199-200页

作者： Srishti Chawla Mikael Molin thomas Nystrom Institute for Biomedicine Sahlgrenska AcademyCentre for Ageing and Health-AgeCapUniversity of GothenburgGothenburgSweden Department of Life Sciences Chalmers University of TechnologyGothenburgSweden

Calcineurin(CN)is a calcium-and calmodulindependent serine/threonine that has been studied in many model organisms including yeast,filamentous fungi,plants,and *** biological functions range from ion homeostasis and virulence in lower eukaryotes to T-cell activation in humans by human nuclear factors of activated *** is a heterodimeric protein consisting of a catalytic subunit,calcineurin A(Cna1p),which contains an active site with a dinuclear metal center,and a regulatory Ca^(2+) binding subunit called calcineurin B(Cnb1p)required to activate *** calcineurin B subunit has been highly conserved through evolution:For example,the mammalian calcineurin B shows 54%identity with calcineurin B from Saccharomyces cerevisiae.

关键词： activation dinuclear conserved

Microglia:a promising therapeutic target in spinal cord injury

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Neural Regeneration Research 2025年第2期20卷 454-463页

作者： Xiaowei Zha Guoli Zheng thomas Skutella Karl Kiening Andreas Unterberg Alexander Younsi Department of Neurosurgery Heidelberg University HospitalHeidelbergGermany Department of Neuroanatomy Institute for Anatomy and Cell BiologyHeidelberg UniversityHeidelbergGermany

Microglia are present throughout the central nervous system and are vital in neural repair,nutrition,phagocytosis,immunological regulation,and maintaining neuronal *** a healthy spinal cord,microglia are accountable for immune surveillance,however,when a spinal cord injury occurs,the microenvironment drastically changes,leading to glial scars and failed axonal *** this context,microglia vary their gene and protein expression during activation,and proliferation in reaction to the injury,influencing injury responses both favorably and unfavorably.A dynamic and multifaceted injury response is mediated by microglia,which interact directly with neurons,astrocytes,oligodendrocytes,and neural stem/progenitor *** a clear understanding of their essential nature and origin,the mechanisms of action and new functions of microglia in spinal cord injury require extensive *** review summarizes current studies on microglial genesis,physiological function,and pathological state,highlights their crucial roles in spinal cord injury,and proposes microglia as a therapeutic target.

关键词： astrocytes cytokines functional recovery immune regulation M1/M2 activation macrophages microglia neuroinflammation spinal cord injury therapy

Sleep as a window to understand and regulate Alzheimer's disease:emerging roles of thalamic reticular nucleus

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Neural Regeneration Research 2025年第6期20卷 1711-1712页

作者： Haoqi Sun Shiqian Shen Robert J.thomas M.Brandon Westover Can Zhang Department of Neurology Beth Israel Deaconess Medical CenterHarvard Medical SchoolBostonMAUSA Center for Translational Pain Research Department of AnesthesiaCritical Care and Pain MedicineMassachusetis General HospitalHarvard Medical SchoolBostonMAUSA Division of Pulmonary Critical Care and Sleep MedicineDepartment of MedicineBeth Israel Deaconess Medical CenterHarvard Medical SchoolBostonMAUSA Genetics and Aging Research Unit McCance Center for Brain HealthMass General Institute for Neurodegenerative DiseaseDepartment of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMAUSA

Introduction:Alzheimer 's disease(AD) is a common neurodegenerative disorder and the primary cause of dementia. Considerable evidence supports the “amyloid hypothesis,” stating that the pathogenesis of AD is primarily caused by the deposition of amyloid-β(Aβ), which drives tau phosphorylation, neuroinflammation, and neurodegeneration in the brain. The amyloid hypothesis is strengthened by the significant and moderate benefit of lecanemab, a humanized antibody through an anti-amyloid mechanism,showing slowed clinical decline(van Dyck et al.,2023). The recent positive results of anti-amyloid trials have brought back focus on the amyloid hypothesis through biochemical, genetic, and pharmacological approaches(Zhang, 2023).

关键词： Alzheimer inflammation hypothesis