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Generalized Linear Models (Glms)
San Jos´eState University Math 261A: Regression Theory & Methods Generalized Linear Models (GLMs) Dr. Guangliang Chen This lecture is based on the following textbook sections: • Chapter 13: 13.1 – 13.3 Outline of this presentation: • What is a GLM? • Logistic regression • Poisson regression Generalized Linear Models (GLMs) What is a GLM? In ordinary linear regression, we assume that the response is a linear function of the regressors plus Gaussian noise: 0 2 y = β0 + β1x1 + ··· + βkxk + ∼ N(x β, σ ) | {z } |{z} linear form x0β N(0,σ2) noise The model can be reformulate in terms of • distribution of the response: y | x ∼ N(µ, σ2), and • dependence of the mean on the predictors: µ = E(y | x) = x0β Dr. Guangliang Chen | Mathematics & Statistics, San Jos´e State University3/24 Generalized Linear Models (GLMs) beta=(1,2) 5 4 3 β0 + β1x b y 2 y 1 0 −1 0.0 0.2 0.4 0.6 0.8 1.0 x x Dr. Guangliang Chen | Mathematics & Statistics, San Jos´e State University4/24 Generalized Linear Models (GLMs) Generalized linear models (GLM) extend linear regression by allowing the response variable to have • a general distribution (with mean µ = E(y | x)) and • a mean that depends on the predictors through a link function g: That is, g(µ) = β0x or equivalently, µ = g−1(β0x) Dr. Guangliang Chen | Mathematics & Statistics, San Jos´e State University5/24 Generalized Linear Models (GLMs) In GLM, the response is typically assumed to have a distribution in the exponential family, which is a large class of probability distributions that have pdfs of the form f(x | θ) = a(x)b(θ) exp(c(θ) · T (x)), including • Normal - ordinary linear regression • Bernoulli - Logistic regression, modeling binary data • Binomial - Multinomial logistic regression, modeling general cate- gorical data • Poisson - Poisson regression, modeling count data • Exponential, Gamma - survival analysis Dr. -
Digital Electronic Circuits
DIGITAL ELECTRONIC CIRCUITS SUBJECT CODE: PEI4I103 B.Tech, Fourth Semester Prepared By Dr. Kanhu Charan Bhuyan Asst. Professor Instrumentation and Electronics Engineering COLLEGE OF ENGINEERING AND TECHNOLOGY BHUBANESWAR B.Tech (E&IE/AE&I) detail Syllabus for Admission Batch 2015-16 PEI4I103- DIGITAL ELECTRONICS University level 80% MODULE – I (12 Hours)1. Number System: Introduction to various number systems and their Conversion. Arithmetic Operation using 1’s and 2`s Compliments, Signed Binary and Floating Point Number Representation Introduction to Binary codes and their applications. (5 Hours) 2. Boolean Algebra and Logic Gates: Boolean algebra and identities, Complete Logic set, logic gates and truth tables. Universal logic gates, Algebraic Reduction and realization using logic gates (3 Hours) 3. Combinational Logic Design: Specifying the Problem, Canonical Logic Forms, Extracting Canonical Forms, EX-OR Equivalence Operations, Logic Array, K-Maps: Two, Three and Four variable K-maps, NAND and NOR Logic Implementations. (4 Hours) MODULE – II (14 Hours) 4. Logic Components: Concept of Digital Components, Binary Adders, Subtraction and Multiplication, An Equality Detector and comparator, Line Decoder, encoders, Multiplexers and De-multiplexers. (5 Hours) 5. Synchronous Sequential logic Design: sequential circuits, storage elements: Latches (SR,D), Storage elements: Flip-Flops inclusion of Master-Slave, characteristics equation and state diagram of each FFs and Conversion of Flip-Flops. Analysis of Clocked Sequential circuits and Mealy and Moore Models of Finite State Machines (6 Hours) 6. Binary Counters: Introduction, Principle and design of synchronous and asynchronous counters, Design of MOD-N counters, Ring counters. Decade counters, State Diagram of binary counters (4 hours) MODULE – III (12 hours) 7. -
Design and Analysis of Power Efficient PTL Half Subtractor Using 120Nm Technology
International Journal of Computer Trends and Technology (IJCTT) – volume 7 number 4– Jan 2014 Design and Analysis of Power Efficient PTL Half Subtractor Using 120nm Technology 1 2 Pranshu Sharma , Anjali Sharma 1(Department of Electronics & Communication Engineering, APG Shimla University, India) 2(Department of Electronics & Communication Engineering, APG Shimla University, India) ABSTRACT : In the designing of any VLSI System, There are a number of logic styles by which a circuit can arithmetic circuits play a vital role, subtractor circuit is be implemented some of them used in this paper are one among them. In this paper a Power efficient Half- CMOS, TG, PTL. CMOS logic styles are robust against Subtractor has been designed using the PTL technique. voltage scaling and transistor sizing and thus provide a Subtractor circuit using this technique consumes less reliable operation at low voltages and arbitrary transistor power in comparison to the CMOS and TG techniques. sizes. In CMOS style input signals are connected to The proposed Half-Subtractor circuit using the PTL transistor gates only, which facilitate the usage and technique consists of 6 NMOS and 4 PMOS. The characterization of logic cells. Due to the complementary proposed PTL Half-Subtractor is designed and simulated transistor pairs the layout of CMOS gates is not much using DSCH 3.1 and Microwind 3.1 on 120nm. The complicated and is power efficient. The large number of power estimation and simulation of layout has been done PMOS transistors used in complementary CMOS logic for the proposed PTL half-Subtractor design. Power style is one of its major disadvantages and it results in comparison on BSIM-4 and LEVEL-3 has been high input loads [3]. -
The Hexadecimal Number System and Memory Addressing
C5537_App C_1107_03/16/2005 APPENDIX C The Hexadecimal Number System and Memory Addressing nderstanding the number system and the coding system that computers use to U store data and communicate with each other is fundamental to understanding how computers work. Early attempts to invent an electronic computing device met with disappointing results as long as inventors tried to use the decimal number sys- tem, with the digits 0–9. Then John Atanasoff proposed using a coding system that expressed everything in terms of different sequences of only two numerals: one repre- sented by the presence of a charge and one represented by the absence of a charge. The numbering system that can be supported by the expression of only two numerals is called base 2, or binary; it was invented by Ada Lovelace many years before, using the numerals 0 and 1. Under Atanasoff’s design, all numbers and other characters would be converted to this binary number system, and all storage, comparisons, and arithmetic would be done using it. Even today, this is one of the basic principles of computers. Every character or number entered into a computer is first converted into a series of 0s and 1s. Many coding schemes and techniques have been invented to manipulate these 0s and 1s, called bits for binary digits. The most widespread binary coding scheme for microcomputers, which is recog- nized as the microcomputer standard, is called ASCII (American Standard Code for Information Interchange). (Appendix B lists the binary code for the basic 127- character set.) In ASCII, each character is assigned an 8-bit code called a byte. -
Design of Adder / Subtractor Circuits Based On
ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 8, August 2013 DESIGN OF ADDER / SUBTRACTOR CIRCUITS BASED ON REVERSIBLE GATES V.Kamalakannan1, Shilpakala.V2, Ravi.H.N3 Lecturer, Department of ECE, R.L.Jalappa Institute of Technology, Doddaballapur, Karnataka, India 1 Asst. Professor & HOD, Department of ECE, R.L.Jalappa Institute of Technology, Doddaballapur, Karnataka, India2 Lab Assistant, Dept. of ECE, U.V.C.E, Bangalore Karnataka, India3 ABSTRACT: Reversible logic has extensive applications in quantum computing, it is a unconventional form of computing where the computational process is reversible, i.e., time-invertible. The main motivation behind the study of this technology is aimed at implementing reversible computing where they offer what is predicted to be the only potential way to improve the energy efficiency of computers beyond von Neumann-Landauer limit. It is relatively new and emerging area in the field of computing that taught us to think physically about computation Quantum Computing will be a total change in how the computer will operate and function. The reversible arithmetic circuits are efficient in terms of number of reversible gates, garbage output and quantum cost. In this paper design Reversible Binary Adder- Subtractor- Mux, Adder-Subtractor- TR Gate., Adder-Subtractor- Hybrid are proposed. In all the three design approaches, the Adder and Subtractor are realized in a single unit as compared to only full adder/subtractor in the existing design. The performance analysis is verified using number reversible gates, Garbage input/outputs and Quantum Cost.The reversible 4-bit full adder/ subtractor design unit is compared with conventional ripple carry adder, carry look ahead adder, carry skip adder, Manchester carry adder based on their performance with respect to area, timing and power. -
Area and Power Optimized D-Flip Flop and Subtractor
IT in Industry, Vol. 9, No.1, 2021 Published Online 28-02-2021 AREA AND POWER OPTIMIZED D-FLIP FLOP AND SUBTRACTOR T. Subhashini1, M. Kamaraju2, K. Babulu3 1,2 Department of ECE, Gudlavalleru Engineering College, India 3Department of ECE, UCOE, JNTUK, Kakinada, India Abstract: Low power is essential in today’s technology. It LITERATURE SURVEY is most significant with high speed, small size and stability. So, power reduction is most important in modern In the era of microelectronics, power dissipation is technology using VLSI design techniques. Today most of limiting factor. In any CMOS technology, provides the market necessities require low power, long run time very low Static power dissipation. Due to the and market which also deserve small size and high speed. discharge of capacitance, during the switching In this paper several logic circuits DFF with 5 transistors operation, that cause a power dissipation. Which and sub tractor circuit using powerless XOR gate and increases with the clock frequency. Using Groundless XNOR gates are implemented. In the proposed Transmission Gate Technique Such unnecessary DFF, the area can be decreased by 62% & substarctor discharging can be prevented. Due to the resistance of circuit, area decreased by 80% and power consumption of switches, impartial losses may be occurred for any DFF and subtractor circuit are 15.4µW and 13.76µW logic operation. respectively, but these are very less as compared to existing techniques. To avoid these minor losses keep clock frequency to be small than the technological limit. There are Keyword: D FF, NMOS, PMOS, VLSI, P-XOR, G-XNOR different types of Transmission Gate logic families. -
High Performance Full Subtractor Using Floating-Gate MOSFET
Microelectronic Engineering 162 (2016) 75–78 Contents lists available at ScienceDirect Microelectronic Engineering journal homepage: www.elsevier.com/locate/mee Accelerated publication High performance full subtractor using floating-gate MOSFET Roshani Gupta, Rockey Gupta, Susheel Sharma Department of Physics and Electronics, University of Jammu, Jammu 180006, India article info abstract Article history: Low power consumption has become one of the primary requirements in the design of digital VLSI circuits in re- Received 18 March 2016 cent years. With scaling down of device dimensions, the supply voltage also needs to be scaled down for reliable Received in revised form 10 May 2016 operation. The speed of conventional digital integrated circuits is degrading on reducing the supply voltage for a Accepted 11 May 2016 given technology. Moreover, the threshold voltage of MOSFET does not scale down proportionally with its di- Available online 13 May 2016 mensions, thus putting a limitation on its suitability for low voltage operation. Therefore, there is a need to ex- Keywords: plore new methodology for the design of digital circuits well suited for low voltage operation and low power Full subtractor consumption. Floating-gate MOS (FGMOS) technology is one of the design techniques with its attractive features FGMOS of reduced circuit complexity and threshold voltage programmability. It can be operated below the conventional TG threshold voltage of MOSFET leading to wider output signal swing at low supply voltage and dissipates less CPL power as compared to CMOS circuits without much compromise on the device performance. This paper presents GDI the design of full subtractor using FGMOS technique whose performance has been compared with full subtractor Power delay product circuits employing CMOS, transmission gates (TG), complementary pass transistor logic (CPL) and gate diffusion input (GDI). -
Design and Implementation of Adder/Subtractor and Multiplication Units for Floating-Point Arithmetic
International Journal of Electronics Engineering, 2(1), 2010, pp. 197-203 Design and Implementation of Adder/Subtractor and Multiplication Units for Floating-Point Arithmetic Dhiraj Sangwan1 & Mahesh K. Yadav2 1MITS University, Laxmangarh, Rajasthan, India 2BRCM College of Engg. & Tech., Bahal, M D University, Rohtak, Haryana, India Abstract: Computers were originally built as fast, reliable and accurate computing machines. It does not matter how large computers get, one of their main tasks will be to always perform computation. Most of these computations need real numbers as an essential category in any real world calculations. Real numbers are not finite; therefore no finite, representation method is capable of representing all real numbers, even within a small range. Thus, most real values will have to be represented in an approximate manner. The scope of this paper includes study and implementation of Adder/Subtractor and multiplication functional units using HDL for computing arithmetic operations and functions suited for hardware implementation. The algorithms are coded in VHDL and validated through extensive simulation. These are structured so that they provide the required performance i.e. speed and gate count. This VHDL code is then synthesized by Synopsys tool to generate the gate level net list that can be implemented on the FPGA using Xilinx FPGA Compiler. Keywords: Algorithm, ALU, Adder/Subtractor, Arithmetic Computation, Floating Point, IEEE 754 Standard, Multiplication, RTL, Significand, Simulation, Synthesis, VHDL. 1. INTRODUCTION normalized floating-point numbers as if they were The Arithmetic logic unit or ALU is the brain of the computer, integers)[9]. the device that performs the arithmetic operations like addition and subtraction or logical operations like AND and 1.2. -
Generalized Linear Models
CHAPTER 6 Generalized linear models 6.1 Introduction Generalized linear modeling is a framework for statistical analysis that includes linear and logistic regression as special cases. Linear regression directly predicts continuous data y from a linear predictor Xβ = β0 + X1β1 + + Xkβk.Logistic regression predicts Pr(y =1)forbinarydatafromalinearpredictorwithaninverse-··· logit transformation. A generalized linear model involves: 1. A data vector y =(y1,...,yn) 2. Predictors X and coefficients β,formingalinearpredictorXβ 1 3. A link function g,yieldingavectoroftransformeddataˆy = g− (Xβ)thatare used to model the data 4. A data distribution, p(y yˆ) | 5. Possibly other parameters, such as variances, overdispersions, and cutpoints, involved in the predictors, link function, and data distribution. The options in a generalized linear model are the transformation g and the data distribution p. In linear regression,thetransformationistheidentity(thatis,g(u) u)and • the data distribution is normal, with standard deviation σ estimated from≡ data. 1 1 In logistic regression,thetransformationistheinverse-logit,g− (u)=logit− (u) • (see Figure 5.2a on page 80) and the data distribution is defined by the proba- bility for binary data: Pr(y =1)=y ˆ. This chapter discusses several other classes of generalized linear model, which we list here for convenience: The Poisson model (Section 6.2) is used for count data; that is, where each • data point yi can equal 0, 1, 2, ....Theusualtransformationg used here is the logarithmic, so that g(u)=exp(u)transformsacontinuouslinearpredictorXiβ to a positivey ˆi.ThedatadistributionisPoisson. It is usually a good idea to add a parameter to this model to capture overdis- persion,thatis,variationinthedatabeyondwhatwouldbepredictedfromthe Poisson distribution alone. -
Modelling Binary Outcomes
Modelling Binary Outcomes 01/12/2020 Contents 1 Modelling Binary Outcomes 5 1.1 Cross-tabulation . .5 1.1.1 Measures of Effect . .6 1.1.2 Limitations of Tabulation . .6 1.2 Linear Regression and dichotomous outcomes . .6 1.2.1 Probabilities and Odds . .8 1.3 The Binomial Distribution . .9 1.4 The Logistic Regression Model . 10 1.4.1 Parameter Interpretation . 10 1.5 Logistic Regression in Stata . 11 1.5.1 Using predict after logistic ........................ 13 1.6 Other Possible Models for Proportions . 13 1.6.1 Log-binomial . 14 1.6.2 Other Link Functions . 16 2 Logistic Regression Diagnostics 19 2.1 Goodness of Fit . 19 2.1.1 R2 ........................................ 19 2.1.2 Hosmer-Lemeshow test . 19 2.1.3 ROC Curves . 20 2.2 Assessing Fit of Individual Points . 21 2.3 Problems of separation . 23 3 Logistic Regression Practical 25 3.1 Datasets . 25 3.2 Cross-tabulation and Logistic Regression . 25 3.3 Introducing Continuous Variables . 26 3.4 Goodness of Fit . 27 3.5 Diagnostics . 27 3.6 The CHD Data . 28 3 Contents 4 1 Modelling Binary Outcomes 1.1 Cross-tabulation If we are interested in the association between two binary variables, for example the presence or absence of a given disease and the presence or absence of a given exposure. Then we can simply count the number of subjects with the exposure and the disease; those with the exposure but not the disease, those without the exposure who have the disease and those without the exposure who do not have the disease. -
Design of Fault Tolerant Full Subtractor
IJRECE VOL. 6 ISSUE 2 APR.-JUNE 2018 ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) Design of Fault Tolerant Full Subtractor Rita Mahajan1, Sharu Bansal2, Deepak Bagai3 Department of Electronics and Communication Engineering Punjab Engineering College (Deemed to be University), Chandigarh, India. [email protected],[email protected],[email protected] Abstract- Now days, the complexity of integrated circuits is extra circuit like generating 2's complement of a number increasing while reliability of the components is decreasing which is to be subtracted. The proposed design is an due to small gates and transistor. One of the impacts of independent subtractor so that addition and subtraction can be technology scaling is more sensitivity to transient and carried out parallel which improves the performance of permanent faults. It is very difficult to detect these faults system. So, the design of faster and reliable subtractor is of offline. The fault tolerant subtractor can detect the faults and great importance in such systems. There are many approaches tolerate the detected faults. There are many fault tolerant full to achieve the fault tolerance in full subtractor. The subtractor which can detect these faults online. So, an efficient researchers have introduced the concept of redundancy to fault tolerant full subtractor is proposed in this paper which detect the fault in full subtractor. But in order to detect and can detect the faults with their exact location and also tolerate tolerate the faults there is need of an efficient fault tolerant full the faults. The proposed subtractor can detect the faults in subtractor. single and multi net. -
Section II Descriptive Statistics for Continuous & Binary Data (Including
Section II Descriptive Statistics for continuous & binary Data (including survival data) II - Checklist of summary statistics Do you know what all of the following are and what they are for? One variable – continuous data (variables like age, weight, serum levels, IQ, days to relapse ) _ Means (Y) Medians = 50th percentile Mode = most frequently occurring value Quartile – Q1=25th percentile, Q2= 50th percentile, Q3=75th percentile) Percentile Range (max – min) IQR – Interquartile range = Q3 – Q1 SD – standard deviation (most useful when data are Gaussian) (note, for a rough approximation, SD 0.75 IQR, IQR 1.33 SD) Survival curve = life table CDF = Cumulative dist function = 1 – survival curve Hazard rate (death rate) One variable discrete data (diseased yes or no, gender, diagnosis category, alive/dead) Risk = proportion = P = (Odds/(1+Odds)) Odds = P/(1-P) Relation between two (or more) continuous variables (Y vs X) Correlation coefficient (r) Intercept = b0 = value of Y when X is zero Slope = regression coefficient = b, in units of Y/X Multiple regression coefficient (bi) from a regression equation: Y = b0 + b1X1 + b2X2 + … + bkXk + error Relation between two (or more) discrete variables Risk ratio = relative risk = RR and log risk ratio Odds ratio (OR) and log odds ratio Logistic regression coefficient (=log odds ratio) from a logistic regression equation: ln(P/1-P)) = b0 + b1X1 + b2X2 + … + bkXk Relation between continuous outcome and discrete predictor Analysis of variance = comparing means Evaluating medical tests – where the test is positive or negative for a disease In those with disease: Sensitivity = 1 – false negative proportion In those without disease: Specificity = 1- false positive proportion ROC curve – plot of sensitivity versus false positive (1-specificity) – used to find best cutoff value of a continuously valued measurement DESCRIPTIVE STATISTICS A few definitions: Nominal data come as unordered categories such as gender and color.