Application of the method of robust estimation by posterior variance in detecting the raw error of geodetic control network

If the raw error appears in set of measuring data, it affects significantly on adjustment results and displacement values of monitoring points, thus conclusion about displacement of works is incorrect. The method of robust estimation by posterior variance for detecting the raw error bases on principle of choosing weight of robust estimation, this is the other type that belong to the least square statistical estimation, which is used to process the measuring data with raw error when they were given into random model of the adjustment problem. Through processing data of Son La hydroelectricity construction network, the obtained result proved that the method good efficiency, it not only finds the measuring value that contains the raw error, but also determines the value of the raw error nearly accurately, moreover, it is able to detect many raw error in the set of data.

INFERIORITY COMPLEX, ADJUSTMENT PROBLEM AND ACADEMIC PERFORMANCE OF DIFFERENTLY-ABLED STUDENTS IN THE STATE OF WEST BENGAL

Purposes of the Study: The main purpose of the present study was to explore the Level of Inferiority Complex and Adjustment Problems of the Differently-Abled students. Besides this, the other purposes of the present study were to find out the relation of Inferiority Complex with Adjustment Problems and Academic Performance of the Differently-Abled students in the state of West Bengal. Methodology: The investigators used Survey based Quantitative method for the present study. The sample consists of 86 Differently-Abled Students from 14 (fourteen) selected special and normal schools which were situated in the district Hooghly, Bankura & Purulia in the state of West Bengal. The simple random sampling technique has been used for the selection of samples. The investigators have developed two tools, namely Questionnaire for Measuring Inferiority Complex and the Adjustment Problem Inventory by themselves to measure the Level of Inferiority Complex and Adjustment Problem of Differently-Abled Students. The present investigators have used SPSS (Version-20) followed by MEAN; S.D.; 't'-Test; ANOVA and Graph for analyzing the data. Major Findings of the Study: The overall results indicate that the Levels of Inferiority Complex and Adjustment Problem of the Differently-Abled students were Moderate. It was found that Gender, Age and Reading Class had no significant influence on the Inferiority Complex of the Differently-Abled students. It was also explored that the Inferiority Complex and Academic Performance of the Differently-Abled students were negatively interrelated with each other. It means that Academic Performance is decreased by increasing the level of Inferiority Complex of the Differently-Abled students. Applications of the Study: This study will be helpful for the parents, teachers, administrators, counsellors, educational policymakers as well as our society to treat the Differently-Abled students in a better way. Novelty/Originality of the Study: Through the present study, it was found that the level of Inferiority Complex of the Differently-Abled students can be influenced by their Nature of Disability and the Number of Sisters & Brothers. It was a novel finding of the study.

Entropy-Based Solutions for Ecological Inference Problems: A Composite Estimator

Information-based estimation techniques are becoming more popular in the field of Ecological Inference. Within this branch of estimation techniques, two alternative approaches can be pointed out. The first one is the Generalized Maximum Entropy (GME) approach based on a matrix adjustment problem where the only observable information is given by the margins of the target matrix. An alternative approach is based on a distributionally weighted regression (DWR) equation. These two approaches have been studied so far as completely different streams, even when there are clear connections between them. In this paper we present these connections explicitly. More specifically, we show that under certain conditions the generalized cross-entropy (GCE) solution for a matrix adjustment problem and the GME estimator of a DWR equation differ only in terms of the a priori information considered. Then, we move a step forward and propose a composite estimator that combines the two priors considered in both approaches. Finally, we present a numerical experiment and an empirical application based on Spanish data for the 2010 year.

Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation

B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.

A Resolution to Valuation Conflicts of Swaptions/Caps and OIS/LIBOR

In this article, the authors provide a unified valuation framework under which a multicurve economy can be established and caps/floors and swaptions can be consistently priced. Furthermore, if a lognormal distribution is employed for the forward price (or 1 plus forward rate), then a “model-free” volatility calibration can be achieved, and all swaptions and caps/floors are perfectly repriced. This article leverages earlier work by Chen, Hsieh, and Huang (2017) who fix a crucial drift-adjustment problem of the traditional LIBOR market model (LMM) where the LIBOR rates follow a lognormal distribution. By assuming 1 + LIBOR to be lognormal (hence LIBOR is shifted lognormal), Chen, Hsieh, and Huang achieve an exact and deterministic drift-adjustment term. In this article, they extend the model to provide a perfect calibration to both swaptions and caps/floors (which is not doable under the traditional LMM), and by using a foreign currency analogy, they show that the model supports multiple curves, which is a key element to overnight index swap (OIS) discounting.