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GDP Forecast for India using Mixed Data Sampling Technique

GDP Forecast for India using Mixed Data Sampling Technique

The predictors which are obtained through DFM varies on a quarterly, monthly, weekly or daily basis. This variation in predictor frequency makes it difficult to obtain the GDP Forecast. In the currently followed traditional method of forecast, the predictors having higher frequency of occurrence are averaged to lower frequency and forecast is carried out at the lower frequency. For example, monthly economic indicators are averaged to obtain quarterly data and it is used for forecast along with quarterly economic indicators. This method of forecast has some draw backs as lot of valuable data are lost during averaging and frequency conversion. To counter this problem, Mixed Data Sampling (MIDAS) Technique can be used [1](Ghysels et al., 2004). By using MIDAS method, the need for averaging higher frequency indicators is eliminated and the forecast can be carried out including all the observations.

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GDP Forecast for India using Mixed Data Sampling Technique

GDP Forecast for India using Mixed Data Sampling Technique

of all the goods and service produced in a country in a given time period. GDP calculation is important as it helps in assessing the economic health of a country. By forecasting the GDP, policy makers of a country can make decisions so as to ensure that economic development progresses unhindered and to reduce the chances of economic recession. This project work attempts to develop an efficient method to forecast the GDP of India using Mixed Data Sampling Technique. The GDP of India is affected by various macroeconomic indicators which vary in quarterly, monthly, weekly or daily frequencies. A preliminary study was conducted in order to identify the macroeconomic indicators affecting the GDP of India. After identifying the indicators, the Dynamic Factor Modelling method was first used to identify the relevant predictors from large amounts of macroeconomic and financial data affecting Indian Economy. After identifying 22 relevant predictors, the Mixed Data Sampling Technique was implemented in order to obtain the GDP Forecast. . Both the forecasts were compared by calculating the Root Mean Square Forecast Error in order to ascertain the accuracy of the MIDAS technique. Then the Diebold Mariano test was conducted in order to identify whether there was a significant difference between the GDP Forecasts.

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A New Partition-based Clustering Algorithm For Mixed Data

A New Partition-based Clustering Algorithm For Mixed Data

the difficulty, some scholars have performed a depth explo- ration. For instance, by integrating the K-Means[13], [14], [15] algorithm and K-Modes algorithm [16], [17] directly together, and extending to the K-Prototypes algorithm[18], Huang and others succeeded to solve the clustering problem of mixed data in 1998. In 2002, the literature[19] proposed a hierarchical agglomerative clustering algorithm SBAC (Sim- ilarity Based Agglomerative Clustering). The similarity of Good all is used to measure the similarity between object relation and class relation by SBAC algorithm. Among them, attribute weights which do not often occur, are assigned large values by similarity measure. For a numeric attribute, similarity measure not only contain the value of the differ- ence in size, but also contain the unique value to appear. In 2010, the evolution of the k - prototype algorithm[20] is proposedEKP by introducing an evolutionary algorithm framework and it makes the algorithm own global search ability. In 2011, k - the prototype algorithm of extended algorithm KL - FCM - GM[21] is proposed. In 2012, fuzzy k - prototype algorithm is proposed[22] . On the one hand, this algorithm can keep the data set inherent uncertainty a longer time before the determination data which belongs to the prototype. On the other hand, the effect of different dimen- sions of data on the clustering process is fully considered. Because of its high efficiency and simple implement, par- titional clustering algorithm, represented by K-Proto types, has been widely applied. However, dissimilarity measures between numerical attributes and categorical attributes are not uniformed and there exists difficulties of weighting factor selections in K-prototypes algorithm. Therefore, a partitioned clustering algorithm for processing mixed data, which is based on Kprototypes algorithm, is particularly necessary to appear.Therefore, in this paper, we firstly propose the multi Modes representation means of category centres of categorical data in K-prototypes algorithm, and then the Euclidean is expanded to mixed data so as to reflect the dissimilarity between the objects and categories. On the basis, a partitioned clustering algorithm for processing mixed data is proposed. Finally, the experimental results on UCI dataset show that the proposed algorithm can improve the clustering performance effectively and accurately.

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Aggression annotated Corpus of Hindi English Code mixed Data

Aggression annotated Corpus of Hindi English Code mixed Data

As the interaction over the web has increased, incidents of aggression and related events like trolling, cyberbullying, flaming, hate speech, etc. too have increased manifold across the globe. While most of these behaviour like bullying or hate speech have predated the Internet, the reach and extent of the Internet has given these an unprecedented power and influence to affect the lives of billions of people. So it is of utmost significance and importance that some preventive measures be taken to provide safeguard to the people using the web such that the web remains a viable medium of communication and connection, in general. In this paper, we discuss the development of an aggression tagset and an annotated corpus of Hindi-English code-mixed data from two of the most popular social networking / social media platforms in India – Twitter and Facebook. The corpus is annotated using a hierarchical tagset of 3 top-level tags and 10 level 2 tags. The final dataset contains approximately 18k tweets and 21k facebook comments and is being released for further research in the field.

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Clustering of Mixed Data Types with Application to Toxicogenomics

Clustering of Mixed Data Types with Application to Toxicogenomics

mean values of data sets to select vectors that separate samples into one of two or more mutually exclusive classes. LDA, single gene ANOVA and hierarchical clustering of gene expression data, from samples treated with phenobarbital, a barbiturate and enzyme inducer, or one of the peroxisome proliferator chemical compounds, were used to select genes with highly correlated expression profiles that separate the treatments of the samples with the two classes of agents while simultaneously partitioning sub-classes of peroxisome proliferators. There was concordance between the discriminator genes from the different classification approaches suggesting sufficient richness of the microarray data, specificity of the discriminating profiles and robustness of the computational algorithms to discern gene expression differences in the two-class separation problem. However, while the LDA method selected unique gene-pairs that separated the individual treatments as well as the fibrate subclass from the peroxisome proliferators class of agents, hierarchical clustering and the ANOVA model identified groups of genes, for example GenBank accession numbers AA818412, AA964948, AI070587, AA965078, AA997009 and AI111901, as genes that can discriminate phenobarbital from the peroxisome proliferators. The cytochrome P450, 2b19 phenobarbital-inducible gene (GenBank accession number AA818412) is a pivotal component of phenobarbital

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Data diffraction : challenging data integration in mixed methods research

Data diffraction : challenging data integration in mixed methods research

Our argument has less to do with how or where or when to integrate, although these may be contingent necessary conditions that make integration possible at all. Rather, we interrogate the very premise of integration in the first place. That is to say, an implicit assumption to integration is that the empirical data will depict a particular social phenomenon. Yet there is no a priori rea- son that this is necessarily so. Mixed data could equally reveal multiple phenomena that are entangled together, even though they appear to singular or whole. Mixed data could instead mul- tiply the partiality, increase the uncertainty, and further entangle the subject. Our view is that mixed methods may confuse, split, fracture, trouble, or disturb what is (thought to be) studied and we need an alternative way of acknowledging this possibility. After all, we tend to assume that one method depicts one part or aspect of the object of study and if another method presents a different part or aspect, then the methods have together shown different parts or aspects of the same thing. But what if one method captures the ‘‘ear of the elephant’’ and another method cap- tures the ‘‘tail of a mouse’’? What if mixed methods, very successfully, capture multiple aspects of multiple parts that are entangled together instead of revealing some (singular) ‘‘thing’’ as ‘‘more’’ whole? There is no sure way of knowing whether empirical data are reflecting one or more objects; these are interpretations that are made after the fact.

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Normalization of Indonesian English Code Mixed Twitter Data

Normalization of Indonesian English Code Mixed Twitter Data

Twitter is an excellent source of data for NLP researches as it offers a tremendous amount of textual data. However, processing tweet to ex- tract meaningful information is very challeng- ing, at least for two reasons: (i) using non- standard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifi- cally in Indonesian-English language. We pro- pose a pipeline that consists of four modules, i.e tokenization, language identification, lex- ical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.

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Parallel Computing for Mixed-Stable Modelling of Large Data Sets

Parallel Computing for Mixed-Stable Modelling of Large Data Sets

The OpenMP programming standard [16] allows to specify a scheduling method for the assignment of summands (2) (i.e. loop iterations) to the parallel processes (threads). We have found that the choice of the scheduling method has a significant impact on the performance of our parallel application. It appears that the static schedule with the chunk of one summand is better for the small data set, when the additional overhead of dynamic assignment does not pay off. On the other hand, for the large data set, the dynamic assignment of chunks of 100 summands produces better results, because it is more evenly distributing the workload between the parallel processes. Note that the calculation of each summand in (2) can be of different computational complexity due to adaptive integration of PDF integral (3).

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Mixed Stochastic Input Oriented Data Envelopment Analysis Model

Mixed Stochastic Input Oriented Data Envelopment Analysis Model

Note that the efficiency score of the second college differs in the above two cases. Assuming the student registration variable is normally distributed leads to different results than assuming it following Poisson distribution. Therefore, the above results show the assumption that any random variable under consideration in a stochastic DEA model is following normal distribution will lead to misleading measurement for the DMUs efficiency. Interpreting the efficiency scores, while assuming all the random variables are normally distributed, may lead to wrong management decision. Therefore, it is recommended to choose the appropriate distribution for the random variables (inputs or outputs) before using data envelopment analysis approach.

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Data converters for Scalable CMOS mixed signal Systems for VLSI

Data converters for Scalable CMOS mixed signal Systems for VLSI

B Applications such as wireless communications and digital audio and video have created the need for cost-effective data converters that will achieve higher speed and resolution. The needs required by digital signal processors continually challenge analog designers to improve and develop new ADC and DAC architectures. There are many different types of architectures, each with unique characteristics and different limitations. This section presents a basic overview of the more popular data converter architectures and discusses the advantages and disadvantages of each along with their limitations. The basic architectures will be discussed using a top-down approach. Because many of the converters use op- amps, comparators, and resistor and capacitor arrays, the top-down approach will allow a broader discussion of the key component limitations in later sections.

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Mixed Stochastic Input Oriented Data Envelopment Analysis Model

Mixed Stochastic Input Oriented Data Envelopment Analysis Model

Data envelopment analysis has become a powerful tool for measuring product or organizational performance [14]. It was first introduced by Charnes and Cooper [7], in 1978. DEA, which is a non-parametric method, uses mathematical programming to analyze the performance of comparable organizations. These organizations are known by the decision-making units. Information regarding predetermined inputs and outputs are gathered from different DMUs and implemented in a suitable DEA model. Using this model, one can computes the maximum relative efficiency of each DMU under analysis. DEA method is not restricted to business and economics problems. It has a wide range of applications in different fields. This method is used to compute the efficiency in agriculture [1, 5, 20], ecological development [8], consumption and production of energy [20, 27, 28], etc. It is especially used in the studies of sustainable energy [12, 21]. More applications of DEA, in various fields, can be found in the literature. The efficiency score is the ratio of the weighted output to the weighted input [29]. This produces a DEA ratio problem. This last is transformed from fractional programming to linear programming, forming either input or output oriented model. Finally, the dual of this last formulation is generated (for more details refer to [2]). In this paper, we will consider input oriented data envelopment analysis model. There are many types of DEA models. We differentiate between variable return to scale (VRS), and constant return to scale (CRS). The former is stemmed from a model set by Banker, Charnes and Cooper (BCC), while the last is based on a model described by Charnes, Cooper and Rhodes (CCR) [4]. In this paper, we consider the VRS DEA models, for which the change in inputs is not necessarily proportional to the change in outputs [23].

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A mixed methods inquiry into the validity of data

A mixed methods inquiry into the validity of data

osophical or methodological platform. Generally, qualita- tive approaches are received with scepticism by the natural scientific community because of an accused sub- jective nature and the absence of 'facts' [11]. However, in many cases the two approaches are mixed. For example, qualitative research results often include some kind of quasi-statistics to report conclusions [10]. Similarly, much quantitative research includes some kind of litera- ture review that is subsequently modified by (qualitative) expert opinion and value(s) [17]. In other words: any researcher will use his or her background and position to judge and select focus areas for an angle of investigations, appropriate methodologies to answer the research ques- tion, and interpreting the results and the framing and communication of scientific claims [20,21]. Conse- quently, contemporary theory of knowledge acknowl- edges the effect of a researcher's position and perspectives, and disputes the belief of an unprejudiced observer [22]. Mixed Methods Research (MMR) is defined as an intellec- tual and practical synthesis based on the combination of qualitative and quantitative research methodologies and results [23]. It recognizes the importance of both quanti- tative and qualitative research methods but also offers a powerful third mixed research methodology that poten- tially will provide the most informative, complete, bal- anced, and useful research results. MMR aims at linking theory and practice [19,24] as illustrated in Figure 1. We believe that an appropriate and well-reflected integration of different scientific methods may contribute signifi- cantly to the understanding of any data potentially influ- enced by human action. In the following, it is suggested that scientists with a need to understand a certain field of human action and the consequences and background of these actions can reach far by implementing different methods in their research, and we point to three different methodologies [10,25]: a) supplementary validation; b) triangulation and c) knowledge generation.

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A mixed-method approach to extracting the value of social media data

A mixed-method approach to extracting the value of social media data

In order to test the proposed approach to social media data analysis for NPD decision-making, social media data from the official Samsung Mobile Facebook page was extracted using the NCapture tool of NVivo 10 software. Two months’ worth of data in the form of consumer comments were downloaded for the analysis; in total, 86,055 comments were downloaded. These ranged from general enquiries by Samsung consumers to queries related to a particular Samsung smart phone model, and included comments posted in all languages. In order to keep the focus of this research project on NPD, it was decided to extract comments related to the latest Samsung smart phone model, the Samsung Galaxy S4. To ensure a good understanding of the comments and thus accurate content analysis, only comments posted in English were considered, as English is the common language shared with the researchers. With this imposed control, 1,674 comments in English related to the Samsung Galaxy S4 model were used for the final analysis.

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Mixed effects models for GAW18 longitudinal blood pressure data

Mixed effects models for GAW18 longitudinal blood pressure data

100 simulations were performed. We compared the two proposed methods with each other and with two other existing methods, the original EMMA and R/qtlbim methods. The last two methods only work for univariate data, so we applied them to the simulated data with only first-time measurements used. To make the meth- ods comparable, we generated the receiver operating characteristic (ROC) curve for each method as described later. For a given cutoff of p-value or BF, we calculated the true and false positive findings as follows: a signifi- cant finding is claimed to be a true positive finding if it is located less than 1 Mb from any one of the simulated causal SNPs; otherwise the finding is false. The ROC curves with the false-positive rate less than 0.2 are pre- sented in Figure 2. Intuitively, our two methods that used all available data are more powerful than their cor- responding univariate analysis methods that only used the first-time data. Furthermore, the Bayesian method is

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Mixed Transforms Generated by Tensor Product and Applied in Data Processing

Mixed Transforms Generated by Tensor Product and Applied in Data Processing

Wavelets are functions which allow data analysis of signals or images, according to scales or resolutions and it provide a powerful and remarkably flexible set of tools for handling fundamental problems in science and engineering, such as signal (image) compression, image de-noising, image enhancement, image recognition [9]. The wavelet transform is a type of signal transform that is commonly used in image compression [10], so, by using a wavelet image compression, the compressed image size is reduced but the quality of the image is quite similar to original image [9]. Wavelet analysis can be used to divide the information of an image into approximation and detail sub signals. The approximation sub signal shows the general trend of pixel values, and three detail sub signals show the vertical, horizontal and diagonal details or fast changes in the image [5]. In the decomposition level =

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Bayesian nonparametric models for spatially indexed data of mixed type

Bayesian nonparametric models for spatially indexed data of mixed type

In observational studies the task of identifying important predictors for an outcome of interest can be impeded by the presence of complex interactions and high correlations among confounding variables. Adequately controlling for the effects of such variables can be a challenging task and it may require the inclusion of main and high order interaction effects in the linear predictor of the model, while being subject to multicollinearity problems, which usually lead researchers to select an arbitrary subset of variables to include in the model. Hence, the purpose of this article is to propose a general framework, suitable for spatially structured data, aiming at inferring the effects of explanatory variables on possibly multivariate responses of mixed type, consisting of continuous, count and categorical responses, in the presence of confounding variables, also of mixed type, that can exhibit complex interactions and high correlations.

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The mixed model for the analysis of a repeated‐measurement multivariate count data

The mixed model for the analysis of a repeated‐measurement multivariate count data

the resulting sequences are either categorized based on similarity into operational taxonomical units followed by annota- tion or directly annotated using relevant databases (eg, Ribosomal Database Project, Greengenes, or Silva). The counts for a specific category represent the abundances of the bacteria at a biological taxonomy level. Data sets generated through this sequencing process comprise features that have not been adequately accounted for by currently available statistical methods. 3 Firstly, the data set might be represented by a matrix of taxonomical counts with a compositional structure,

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The prediction accuracy of dynamic mixed-effects models in clustered data

The prediction accuracy of dynamic mixed-effects models in clustered data

As can be seen in Fig. 2, gains in prediction accuracy from dynamic mixed-effects models were seen across all clinic-size quintiles, although the greatest improvement was seen in the largest clinics. This pattern likely reflects the fact that improvements from dynamic modeling were seen relatively rapidly, with approximately 80 % of the total gains in predictive performance for the dynamic BLME models occurring on aver- age after about 7 and 9 predictions at a given clinic for the model with a random inter- cept and the model with both a random intercept and random slope, respectively (Fig. 3). Because there were 480 clinics in the testing sample and the model was up- dated after every 500 predictions, model updates occurred after almost every predic- tion, especially at smaller clinics. The rate of improvement in predictive accuracy was somewhat sensitive to changes in σ 2 , however, with 80 % of the total gains in predict- ive performance for the dynamic BLME model with both a random intercept and slope occurring after about 17 predictions at a given clinic, on average, when the residual error was equal to 50 % of the overall variance in Y ij (Additional file 1: Figure S2).

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An alternative method for correcting fluorescence quenching

An alternative method for correcting fluorescence quenching

Acknowledgements. This work received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technol- ogy for Scotland) and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant refer- ence HR09011) and contributing institutions. A number of field assistants were involved in the deployment of tags on Marion and we are particularly grateful to Mia Wege. FCTD-SRDL tags and fluorescence data were generously provided by CEBC-CNRS as part of the research program supported by CNES-TOSCA, ANR-VMC, Total Foundation and IPEV. The Mammal Research Institute, University of Pretoria, covered Marion Island Argos transmission costs and we thank Trevor McIntyre for facilitating this. We are grateful to Sandy Thomalla and other reviewers for significantly improving this manuscript. Special thanks to Drew Lucas, Mick Wu and Hayley Evers-King for their assistance with the editing, statistics and bio-optics, respectively.

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The Use of Mixed Methodology To Enhance Monitoring Process of Regional Development Strategies

The Use of Mixed Methodology To Enhance Monitoring Process of Regional Development Strategies

Sayer [2] advises that the term ‘method’ “proposes a cautiously considered means of approaching the real-world so as to we may understand it better.” Notions used by the researcher significantly form concepts about the applicable methods for detecting and generating practical data about phenomena. Sayer enlarged his identification of method to include both experiential study and theorizing ones which seems to be very useful as it lights the ways for the interpretation of techniques of data collection and analysis, to be formed as how we hypothesize the world around us. This is important for regional development and regional planning strategies researches as it points to the strain of using one type of research methodologies that may be incompatible with the contexts of subject.

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