A study of present value maximization for the monopolist problem in time scales

There are some mathematical characters in the abstract that will not transfer. Refer to the download to read full abstract. General results for time scales with right dense points are established. A study of issues that arise when unifying (C) and (D) is included in the analysi

RETRACTED: Innovative Practices for the Promotion of Local/Indigenous Knowledge for Disaster Risk Reduction Management in Sudur Paschim Province, Nepal

The description of the Retraction: This article has been retracted. The Original article has previously been published elsewhere without disclosure to the editor, permission to republish, or justification (ie, redundant publication).We apologise for any inconvenience this retraction may have caused readers.Refers to :RETRACTED: Innovative Practices for the Promotion of Local/Indigenous Knowledge for Disaster Risk Reduction Management in Sudur Paschim Province, NepalKabi Prasad Pokhrel, Shambhu Prasad Khatiwada, Narayan Prasad Paudyal, Keshav Raj Dhakal, Chhabi Lal Chidi, Narayan Prasad Timilsena, Dhana Krishna MahatJournal of Geographical Research, Volume 4, Issue 3, July 2021DOI of original article: https://doi.org/10.30564/jgr.v4i3.322

A study of present value maximization for the monopolist problem in time scales A STUDY OF PRESENT VALUE MAXIMIZATION FOR THE MONOPOLIST PROBLEM IN TIME SCALES PRESENT VALUE MAXIMIZATION OF MONOPOLIST IN TIME SCALES

ABSTRACT The present value of the investment of the monopolist for the continuous case is given by P V = ∞ 0 e −rt q(t)p(t)dt with demand condition p(t) = f (t) − q(t) − a 1 q (t) − a 2 q (t),(C), and the present value for the discrete case is We will discuss various conditions and possibilities of maximization of present value of a monopolist. Basically we are focused on the Continuous (C) and Discrete (D) cases. In the (C), there is an exponential approach of growth if and only if a 2 = 0. The boundary conditions in (C) generate some mathematical issues. The first derivative of the quantity q (t) has finite jump at t = 0. If a 2 = 0 then the jump is similar to the jump of q(t) at t = 0. If the sufficient condition for the C problems are satisfied, then the demand equation is unstable. Finally, in (C) the maximum positive discount rate depends on a 1 and a 2 that yields finite maximum present value. In (D) we do not need to have any adjustment as long as α = 0. The sufficient conditions for maximum present value are satisfied for all t ∈ (0, 1). The optimal path is uniquely determined by the boundary condition and the choice of discount factor β. The stability of discount factor is a major player in (D) problem. The stable demand condition implies the existence of bounded finite maximum present value for all β ≤ 1. General results for time scales with right dense points are established. A study of issues that arise when unifying (C) and (D) is included in the analysis

Statistical Analysis and Modeling of Brain Tumor Data: Histology and Regional Effects

Comprehensive statistical models for non-normally distributed cancerous tumor sizes are of prime importance in epidemiological studies, whereas a long term forecasting models can facilitate in reducing complications and uncertainties of medical progress. The statistical forecasting models are critical for a better understanding of the disease and supply appropriate treatments. In addition such a model can be used for the allocations of budgets, planning, control and evaluations of ongoing efforts of prevention and early detection of the diseases. In the present study, we investigate the effects of age, demography, and race on primary brain tumor sizes using quantile regression methods to obtain a better understanding of the malignant brain tumor sizes. The study reveals that the effects of risk factors together with the probability distributions of the malignant brain tumor sizes, and plays significant role in understanding the rate of change of tumor sizes. The data that our analysis and modeling is based on was obtained from Surveillance Epidemiology and End Results (SEER) program of the United States. We also analyze the discretely observed brain cancer mortality rates using functional data analysis models, a novel approach in modeling time series data, to obtain more accurate and relevant forecast of the mortality rates for the US. We relate the cancer registries, race, age, and gender to age-adjusted brain cancer mortality rates and compare the variations of these rates during the period of the study that data was collected. Finally, in the present study we have developed effective statistical model for heterogenous and high dimensional data that forecast the hazard rates with high degree of accuracy, that will be very helpful to address subject health problems at present and in the future

Forecasting Age-Specific Brain Cancer Mortality Rates Using Functional Data Analysis Models

Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. We apply functional data analysis techniques to model age-specific brain cancer mortality trend and forecast entire age-specific functions using exponential smoothing state-space models. The age-specific mortality curves are decomposed using principal component analysis and fit functional time series model with basis functions. Nonparametric smoothing methods are used to mitigate the existing randomness in the observed data. We use functional time series model on age-specific brain cancer mortality rates and forecast mortality curves with prediction intervals using exponential smoothing state-space model. We also present a disparity of brain cancer mortality rates among the age groups together with the rate of change of mortality rates. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the United States. The brain cancer mortality rates, classified under International Classification Disease code ICD-O-3, were extracted from SEERStat software

Forecasting Age-Specific Brain Cancer Mortality Rates Using Functional Data Analysis Models

Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. We apply functional data analysis techniques to model age-specific brain cancer mortality trend and forecast entire age-specific functions using exponential smoothing state-space models. The age-specific mortality curves are decomposed using principal component analysis and fit functional time series model with basis functions. Nonparametric smoothing methods are used to mitigate the existing randomness in the observed data. We use functional time series model on age-specific brain cancer mortality rates and forecast mortality curves with prediction intervals using exponential smoothing state-space model. We also present a disparity of brain cancer mortality rates among the age groups together with the rate of change of mortality rates. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the United States. The brain cancer mortality rates, classified under International Classification Disease code ICD-O-3, were extracted from SEERStat software