A Remark on Unified Error Exponents: Hypothesis Testing, Data Compression and Measure Concentration

This study examines the antecedents and consequence variables of earnings management. This study is expected to explain the motive of earnings management practices by public property and real estate companies in Indonesia: opportunistic or efficient. The theory which is the basis for developing the hypotheses ise agency, positive accounting, and signaling theories simultaneously. This study is explanatory research which aims to explain the causal relationship between variables through hypothesis testing. Data of this research are financial statements of public companies in the property and real estate sector in Indonesia (2014-2018) with some criteria. There are 60 firm-years data used in the analysis. Hypothesis testing uses multiple linear regression two-stage. The first stage analysis is used to examine the effect of the antecedent earnings management variable. Regression second stage test the consequences of earnings management practices. The results show debt and independent commissioners affect earnings management. Management performs more dominant earnings management because of opportunistic interests than to maintain market value and the interests of its shareholders. The implication of this research is to provide a comprehensive discourse on the motives for earnings management behavior in Indonesia.

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Large Deviations and Error Exponents for Hypothesis Testing

Statistical Inference for Engineers and Data Scientists ◽

10.1017/9781107185920.010 ◽

2018 ◽

pp. 184-207

Keyword(s):

Large Deviations ◽

Hypothesis Testing ◽

Error Exponents

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Statistical mechanical evaluation of error exponents for lossy data compression

International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings. ◽

10.1109/isit.2004.1365514 ◽

2004 ◽

Author(s):

T. Hosaka ◽

Y. Kabashima

Keyword(s):

Data Compression ◽

Error Exponents ◽

Statistical Mechanical

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Generalized Error Exponents for Small Sample Universal Hypothesis Testing

IEEE Transactions on Information Theory ◽

10.1109/tit.2013.2283266 ◽

2013 ◽

Vol 59 (12) ◽

pp. 8157-8181 ◽

Cited By ~ 2

Author(s):

Dayu Huang ◽

Sean Meyn

Keyword(s):

Hypothesis Testing ◽

Small Sample ◽

Error Exponents ◽

Universal Hypothesis Testing

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The Development of IoT Compression Technique To Cloud

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.47270 ◽

2019 ◽

Vol 13 (4) ◽

pp. 345

Author(s):

Kartika Sari ◽

Mardhani Riasetiawan

Keyword(s):

Data Compression ◽

Data Transmission ◽

Data Exchange ◽

Compression Technique ◽

Compression Efficiency ◽

Data Coding ◽

Testing Data ◽

Modelling Techniques ◽

Original Size ◽

Size Data

The main problem of data transmission is how to reduce the length of data packet delivery, so it can reduce the time of sending data. One method that can be used to reduce the data size is by compressing the data size. Data compression is a technique for compressing data to get the data with smaller size than the original size so that it can shorten the data exchange timeThis study aims to develop the data compression techniques by modifying and combining the coding and modelling techniques based on the RAKE algorithm. This study testing experiments use 4 different methods in 5 different time-periods to determine the value of the compression, decompression efficiency parameters, and the data transmission time parameters.The result of this study is the data coding technique that using decimal to binary converter data and the modeling technique by calculating the residue from the sensor value will produce data in small sizes and get a compression efficiency value of 45%. For coding techniques using ASCII and modeling techniques with XOR operations will produce bigger size data and the compression efficiency value of 71%. In testing data decompression, the decompression efficiency value of 100%, there is no data loss.

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